Normalize mechanical punctuation fixes

Apply mechanical punctuation and wording-token fixes
Fix LiteLLM cost model resolution
2026-08-16 09:26:39 +02:00 · 2026-08-14 19:43:51 +00:00 · 2026-08-14 19:42:22 +00:00 · 2026-08-12 17:26:00 +03:00 · 2026-08-10 21:28:52 +03:00 · 2026-08-10 11:24:02 -07:00
64 changed files with 1310 additions and 234 deletions
@@ -10,10 +10,10 @@ Install the agent skills for step-by-step workflows:
 npx skills add usestrix/strix
 ```

- `strix-pentest` — run a headless pentest against code, URLs, domains, or IPs and read results (covers both run modes below)
- `strix-cloud-api` — drive the managed app.strix.ai platform via REST (no local Docker/LLM needed)
- `strix-fix-findings` — remediate findings and re-run Strix to verify
- `strix-ci-setup` — add PR scanning to CI/CD (self-hosted CLI or managed app)
+- `penetration-testing-with-strix` — run a headless pentest against code, URLs, domains, or IPs and read results (covers both run modes below)
+- `managed-pentesting-with-strix` — drive the managed app.strix.ai platform via REST (no local Docker/LLM needed)
+- `fix-security-vulnerabilities-with-strix` — remediate findings and re-run Strix to verify
+- `ci-security-scanning-with-strix` — add PR scanning to CI/CD (self-hosted CLI or managed app)

 **Two ways to run, same engine — pick per situation:**

@@ -116,7 +116,7 @@ Strix is agent-ready. Give Claude Code, Cursor, Codex, or any [SKILL.md-compatib
 npx skills add usestrix/strix
 ```

-This installs four skills: **strix-pentest** (run headless scans and read results), **strix-cloud-api** (drive the managed [app.strix.ai](https://app.strix.ai) platform via REST — no local Docker or LLM key), **strix-fix-findings** (remediate + re-scan to verify), and **strix-ci-setup** (PR scanning in CI). Agents can run Strix two ways with the same engine — the open-source CLI locally, or the managed cloud when there's no local infra — and read [`AGENTS.md`](AGENTS.md) for a quick reference, [docs.strix.ai/llms.txt](https://docs.strix.ai/llms.txt) for the CLI docs, and [docs.app.strix.ai](https://docs.app.strix.ai) for the API.
+This installs four skills: **penetration-testing-with-strix** (run headless scans and read results), **managed-pentesting-with-strix** (drive the managed [app.strix.ai](https://app.strix.ai) platform via REST — no local Docker or LLM key), **fix-security-vulnerabilities-with-strix** (remediate + re-scan to verify), and **ci-security-scanning-with-strix** (PR scanning in CI). Agents can run Strix two ways with the same engine — the open-source CLI locally, or the managed cloud when there's no local infra — and read [`AGENTS.md`](AGENTS.md) for a quick reference, [docs.strix.ai/llms.txt](https://docs.strix.ai/llms.txt) for the CLI docs, and [docs.app.strix.ai](https://docs.app.strix.ai) for the API.

 ---

@@ -117,6 +117,21 @@ ENV AGENT_BROWSER_EXECUTABLE_PATH=/usr/bin/chromium
 ENV AGENT_BROWSER_USER_AGENT="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36"
 ENV AGENT_BROWSER_ARGS="--disable-blink-features=AutomationControlled,--no-first-run,--no-default-browser-check,--lang=en-US"
 ENV AGENT_BROWSER_SCREENSHOT_DIR=/workspace/.agent-browser-screenshots
+ENV AGENT_BROWSER_IDLE_TIMEOUT_MS=180000
+USER root
+RUN set -eu; \
+    { \
+        for var in AGENT_BROWSER_EXECUTABLE_PATH AGENT_BROWSER_USER_AGENT \
+                   AGENT_BROWSER_ARGS AGENT_BROWSER_SCREENSHOT_DIR \
+                   AGENT_BROWSER_IDLE_TIMEOUT_MS; do \
+            eval "value=\${$var}"; \
+            printf 'export %s="${%s:-%s}"\n' "$var" "$var" "$value"; \
+        done; \
+    } > /tmp/agent-browser.sh; \
+    install -m 0644 /tmp/agent-browser.sh /etc/profile.d/agent-browser.sh; \
+    rm /tmp/agent-browser.sh; \
+    env -i bash -lc 'test "${AGENT_BROWSER_IDLE_TIMEOUT_MS}" = "180000"'
+USER pentester
 RUN /home/pentester/.npm-global/bin/agent-browser doctor --offline --quick

 RUN set -eux; \
@@ -8,7 +8,7 @@ Configure Strix using environment variables or a config file.
 ## LLM Configuration

 <ParamField path="STRIX_LLM" type="string" required>
-  Model name in LiteLLM format (e.g., `openai/gpt-5.4`, `anthropic/claude-sonnet-4-6`).
+  Model name in LiteLLM format (for example, `openai/gpt-5.4`, `anthropic/claude-sonnet-4-6`).
 </ParamField>

 <ParamField path="LLM_API_KEY" type="string">
@@ -20,7 +20,7 @@ Configure Strix using environment variables or a config file.
 </ParamField>

 <ParamField path="LLM_EXTRA_HEADERS" type="string">
-  Extra HTTP headers sent on every LLM request, as a JSON object (e.g.
+  Extra HTTP headers sent on every LLM request, as a JSON object (for example
  `{"X-Feature-Key":"value","X-Tenant":"acme"}`). Useful for OpenAI-compatible
  gateways that require attribution or routing headers in addition to the bearer
  token. The bearer token itself still comes from `LLM_API_KEY`. Applies to both
@@ -65,8 +65,8 @@ affecting the agents that do the actual testing.

 <ParamField path="DEDUPE_LLM_EXTRA_HEADERS" type="string">
  Optional JSON object of extra HTTP headers sent on every deduplication-model
-  request, e.g. `{"X-Feature-Key":"value"}`. A dedicated dedupe model never
-  inherits `LLM_EXTRA_HEADERS`; set this when its endpoint needs custom headers.
+  request, for example `{"X-Feature-Key":"value"}`. A dedicated dedupe model never
+  inherits `LLM_EXTRA_HEADERS`. Set this when its endpoint needs custom headers.
 </ParamField>

 <ParamField path="STRIX_DEDUPE_REASONING_EFFORT" type="string">
@@ -7,7 +7,7 @@ Skills are structured knowledge packages that give Strix agents deep expertise i

 ## The Idea

-LLMs have broad but shallow security knowledge. They know _about_ SQL injection, but lack the nuanced techniques that experienced pentesters use—parser quirks, bypass methods, validation tricks, and chain attacks.
+LLMs have broad but shallow security knowledge. They know _about_ SQL injection, but lack the nuanced techniques that experienced pentesters use: parser quirks, bypass methods, validation tricks, and chain attacks.

 Skills inject this deep, specialized knowledge directly into the agent's context, transforming it from a generalist into a specialist for the task at hand.

@@ -25,9 +25,9 @@ create_agent(

 The skills are injected into the agent's system prompt, giving it access to:

- **Advanced techniques** — Non-obvious methods beyond standard testing
- **Working payloads** — Practical examples with variations
- **Validation methods** — How to confirm findings and avoid false positives
+- **Advanced techniques**: Non-obvious methods beyond standard testing
+- **Working payloads**: Practical examples with variations
+- **Validation methods**: How to confirm findings and avoid false positives

 ## Skill Categories

@@ -138,7 +138,7 @@ How to confirm findings and avoid false positives.

 Community contributions are welcome. Create a `.md` file in the appropriate category with YAML frontmatter (`name` and `description` fields). Good skills include:

-1. **Real-world techniques** — Methods that work in practice
-2. **Practical payloads** — Working examples with variations
-3. **Validation steps** — How to confirm without false positives
-4. **Context awareness** — Version/environment-specific behavior
+1. **Real-world techniques**: Methods that work in practice
+2. **Practical payloads**: Working examples with variations
+3. **Validation steps**: How to confirm without false positives
+4. **Context awareness**: Version/environment-specific behavior
@@ -24,10 +24,10 @@ Skip the setup. Run Strix in the cloud at [app.strix.ai](https://app.strix.ai).

 ## What You Get

- **Penetration test reports** — Validated findings with PoCs
- **Shareable dashboards** — Collaborate with your team
- **CI/CD integration** — Block risky changes automatically
- **Continuous monitoring** — Catch new vulnerabilities quickly
+- **Penetration test reports**: Validated findings with PoCs
+- **Shareable dashboards**: Collaborate with your team
+- **CI/CD integration**: Block risky changes automatically
+- **Continuous monitoring**: Catch new vulnerabilities quickly

 ## Getting Started

@@ -52,20 +52,20 @@ Skills are specialized knowledge packages that enhance agent capabilities. They

 1. Choose the right category
 2. Create a `.md` file with YAML frontmatter (`name` and `description` fields)
-3. Include practical examples—working payloads, commands, test cases
+3. Include practical examples: working payloads, commands, test cases
 4. Provide validation methods to confirm findings
-5. Submit via PR
+5. Submit through PR

 ## Contributing Code

 ### Pull Request Process

-1. **Create an issue first** — Describe the problem or feature
-2. **Fork and branch** — Work from `main`
-3. **Make changes** — Follow existing code style
-4. **Write tests** — Ensure coverage for new features
-5. **Run checks** — `make check-all` should pass
-6. **Submit PR** — Link to issue and provide context
+1. **Create an issue first**: Describe the problem or feature
+2. **Fork and branch**: Work from `main`
+3. **Make changes**: Follow existing code style
+4. **Write tests**: Ensure coverage for new features
+5. **Run checks**: `make check-all` should pass
+6. **Submit PR**: Link to issue and provide context

 ### Code Style

@@ -77,7 +77,7 @@ Skills are specialized knowledge packages that enhance agent capabilities. They

 ## Package Builds

-Editable installs do not require Go; they run the TUI from source (`go run`).
+Editable installs do not require Go. They run the TUI from source (`go run`).

 Wheels are intentionally strict: they always bundle the matching Go sidecar and
 are platform-specific.
@@ -3,7 +3,7 @@ title: "Introduction"
 description: "Open-source AI hackers to secure your apps"
 ---

-Strix are autonomous AI agents that act like real hackers—they run your code dynamically, find vulnerabilities, and validate them with proof-of-concepts. Built for developers and security teams who need fast, accurate security testing without the overhead of manual pentesting or the false positives of static analysis tools.
+Strix are autonomous AI agents that act like real hackers: they run your code dynamically, find vulnerabilities, and validate them with proof-of-concepts. Built for developers and security teams who need fast, accurate security testing without the overhead of manual pentesting or the false positives of static analysis tools.

 <Frame>
  <img src="/images/screenshot.png" alt="Strix Demo" />
@@ -26,17 +26,17 @@ Strix are autonomous AI agents that act like real hackers—they run your code d

 ## Use Cases

- **Application Security Testing** — Detect and validate critical vulnerabilities in your applications
- **Rapid Penetration Testing** — Get penetration tests done in hours, not weeks
- **Bug Bounty Automation** — Automate research and generate PoCs for faster reporting
- **CI/CD Integration** — Block vulnerabilities before they reach production
+- **Application Security Testing**: Detect and validate critical vulnerabilities in your applications
+- **Rapid Penetration Testing**: Get penetration tests done in hours, not weeks
+- **Bug Bounty Automation**: Automate research and generate PoCs for faster reporting
+- **CI/CD Integration**: Block vulnerabilities before they reach production

 ## Key Capabilities

- **Full hacker toolkit** — Browser automation, HTTP proxy, terminal, Python runtime
- **Real validation** — PoCs, not false positives
- **Multi-agent orchestration** — Specialized agents collaborate on complex targets
- **Developer-first CLI** — Interactive TUI or headless mode for automation
+- **Full hacker toolkit**: Browser automation, HTTP proxy, terminal, Python runtime
+- **Real validation**: PoCs, not false positives
+- **Multi-agent orchestration**: Specialized agents collaborate on complex targets
+- **Developer-first CLI**: Interactive TUI or headless mode for automation

 ## Security Tools

@@ -67,9 +67,9 @@ Strix agents come equipped with a comprehensive toolkit:

 Strix uses a graph of specialized agents for comprehensive security testing:

- **Distributed Workflows** — Specialized agents for different attacks and assets
- **Scalable Testing** — Parallel execution for fast comprehensive coverage
- **Dynamic Coordination** — Agents collaborate and share discoveries
+- **Distributed Workflows**: Specialized agents for different attacks and assets
+- **Scalable Testing**: Parallel execution for fast comprehensive coverage
+- **Dynamic Coordination**: Agents collaborate and share discoveries

 ## Quick Example

@@ -7,7 +7,7 @@ Strix is built to be driven by AI coding agents. Install the official agent skil

 ## Install the Skills

-Works with any agent that supports the open [SKILL.md standard](https://agentskills.io) — Claude Code, Cursor, Codex, Gemini CLI, OpenCode, and dozens more:
+Works with any agent that supports the open [SKILL.md standard](https://agentskills.io): Claude Code, Cursor, Codex, Gemini CLI, OpenCode, and dozens more:

 ```bash
 npx skills add usestrix/strix
@@ -15,34 +15,34 @@ npx skills add usestrix/strix

 | Skill | What your agent learns |
 |-------|------------------------|
-| `strix-pentest` | Run headless scans against code, URLs, domains, or IPs — self-hosted CLI or managed cloud — with budget caps, and read the results |
-| `strix-cloud-api` | Drive the managed [app.strix.ai](https://app.strix.ai) platform over REST — no local Docker or LLM key needed |
-| `strix-fix-findings` | Triage findings, fix root causes, and re-run Strix to verify each fix |
-| `strix-ci-setup` | Add PR security scanning to GitHub Actions or any CI (self-hosted CLI or managed app) |
+| `penetration-testing-with-strix` | Run headless scans against code, URLs, domains, or IPs (self-hosted CLI or managed cloud) with budget caps, and read the results |
+| `managed-pentesting-with-strix` | Drive the managed [app.strix.ai](https://app.strix.ai) platform over REST: no local Docker or LLM key needed |
+| `fix-security-vulnerabilities-with-strix` | Triage findings, fix root causes, and re-run Strix to verify each fix |
+| `ci-security-scanning-with-strix` | Add PR security scanning to GitHub Actions or any CI (self-hosted CLI or managed app) |

-Install a single skill with `npx skills add usestrix/strix --skill strix-pentest`, or use one without installing:
+Install a single skill with `npx skills add usestrix/strix --skill penetration-testing-with-strix`, or use one without installing:

 ```bash
-npx skills use usestrix/strix@strix-pentest | claude
+npx skills use usestrix/strix@penetration-testing-with-strix | claude
 ```

-## Two ways to run — self-hosted or managed
+## Two ways to run: self-hosted or managed

 Both use the same engine and produce the same validated findings and SARIF, so agents can pick per situation or combine them:

- **Open-source CLI (self-hosted)** — runs locally in a Docker sandbox with your own LLM key. Free, fully local, air-gap capable. Best for local dev loops and full control.
- **Managed cloud** — runs on Strix's infrastructure via the [app.strix.ai REST API](https://docs.app.strix.ai). No Docker, no LLM key, no local install; adds team dashboards, scheduling, PR reviews, and downloadable PDF/DOCX reports (Enterprise plan). Best in sandboxed/CI environments and for teams. Create an API token under **Settings → API Access**; the `strix-cloud-api` skill has the full flow.
+- **Open-source CLI (self-hosted)**: runs locally in a Docker sandbox with your own LLM key. Free, fully local, air-gap capable. Best for local dev loops and full control.
+- **Managed cloud**: runs on Strix's infrastructure through the [app.strix.ai REST API](https://docs.app.strix.ai). No Docker, no LLM key, no local install, adds team dashboards, scheduling, PR reviews, and downloadable PDF/DOCX reports (Enterprise plan). Best in sandboxed/CI environments and for teams. Create an API token under **Settings → API Access**. The `managed-pentesting-with-strix` skill has the full flow.

 ## Agent-Friendly Interfaces

 Everything an agent needs is machine-readable:

- **Headless CLI** — `strix -n` runs without the TUI and exits with `0` (clean), `1` (error), or `2` (vulnerabilities found).
- **REST API** — the managed platform exposes a documented [OpenAPI](https://docs.app.strix.ai/openapi.json) at `https://app.strix.ai/api/v1` (scans, vulnerabilities, assets, PR reviews, schedules, webhooks) with bearer tokens and scopes.
- **Structured results** — every run writes `vulnerabilities.json`, `vulnerabilities.csv`, `findings.sarif` (SARIF 2.1.0), and per-finding Markdown under `strix_runs/<run-name>/`; the cloud exposes the same as JSON plus SARIF export.
- **Budget controls** — `--max-budget` and `--max-turns` give agents hard cost/time caps.
- **`AGENTS.md`** — the [repository's agent guide](https://github.com/usestrix/strix/blob/main/AGENTS.md) with a quick reference.
- **`llms.txt`** — this documentation is indexed at [docs.strix.ai/llms.txt](https://docs.strix.ai/llms.txt) and fully exported at [docs.strix.ai/llms-full.txt](https://docs.strix.ai/llms-full.txt); every page is also available as Markdown by appending `.md` to its URL.
+- **Headless CLI**: `strix -n` runs without the TUI and exits with `0` (clean), `1` (error), or `2` (vulnerabilities found).
+- **REST API**: the managed platform exposes a documented [OpenAPI](https://docs.app.strix.ai/openapi.json) at `https://app.strix.ai/api/v1` (scans, vulnerabilities, assets, PR reviews, schedules, webhooks) with bearer tokens and scopes.
+- **Structured results**: every run writes `vulnerabilities.json`, `vulnerabilities.csv`, `findings.sarif` (SARIF 2.1.0), and per-finding Markdown under `strix_runs/<run-name>/`. The cloud exposes the same as JSON plus SARIF export.
+- **Budget controls**: `--max-budget` and `--max-turns` give agents hard cost/time caps.
+- **`AGENTS.md`**: the [repository's agent guide](https://github.com/usestrix/strix/blob/main/AGENTS.md) with a quick reference.
+- **`llms.txt`**: this documentation is indexed at [docs.strix.ai/llms.txt](https://docs.strix.ai/llms.txt) and fully exported at [docs.strix.ai/llms-full.txt](https://docs.strix.ai/llms-full.txt). Every page is also available as Markdown by appending `.md` to its URL.

 ## Example Prompts

@@ -37,7 +37,7 @@ Add these secrets to your repository:

 | Secret | Description |
 |--------|-------------|
-| `STRIX_LLM` | Model name (e.g., `openai/gpt-5.4`) |
+| `STRIX_LLM` | Model name (for example, `openai/gpt-5.4`) |
 | `LLM_API_KEY` | API key for your LLM provider |

 ## Exit Codes
@@ -46,8 +46,8 @@ The workflow fails when vulnerabilities are found:

 | Code | Result |
 |------|--------|
-| 0 | Pass — No vulnerabilities |
-| 2 | Fail — Vulnerabilities found |
+| 0 | Pass: No vulnerabilities |
+| 2 | Fail: Vulnerabilities found |

 ## Scan Modes for CI

@@ -1,6 +1,6 @@
 ---
 title: "Azure OpenAI"
-description: "Configure Strix with OpenAI models via Azure"
+description: "Configure Strix with OpenAI models through Azure"
 ---

 ## Setup
@@ -19,7 +19,7 @@ export AZURE_API_VERSION="2025-11-01-preview"
 | `STRIX_LLM` | `azure/<your-deployment-name>` |
 | `AZURE_API_KEY` | Your Azure OpenAI API key |
 | `AZURE_API_BASE` | Your Azure OpenAI endpoint URL |
-| `AZURE_API_VERSION` | API version (e.g., `2025-11-01-preview`) |
+| `AZURE_API_VERSION` | API version (for example, `2025-11-01-preview`) |

 ## Example

@@ -33,5 +33,5 @@ export AZURE_API_VERSION="2025-11-01-preview"
 ## Prerequisites

 1. Create an Azure OpenAI resource
-2. Deploy a model (e.g., GPT-5.4)
+2. Deploy a model (for example, GPT-5.4)
 3. Get the endpoint URL and API key from the Azure portal
@@ -1,6 +1,6 @@
 ---
 title: "AWS Bedrock"
-description: "Configure Strix with models via AWS Bedrock"
+description: "Configure Strix with models through AWS Bedrock"
 ---

 ## Installation
@@ -17,7 +17,7 @@ pipx install "strix-agent[bedrock]"
 export STRIX_LLM="bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0"
 ```

-No API key required—uses AWS credentials from environment.
+No API key required: uses AWS credentials from environment.

 ## Authentication

@@ -59,7 +59,7 @@ export LLM_API_BASE="http://localhost:1234/v1"  # Adjust port as needed

 Some OpenAI-compatible gateways require extra HTTP headers (for attribution or
 tenant routing) alongside the bearer token. Set them with `LLM_EXTRA_HEADERS` as
-a JSON object — they are sent on every request:
+a JSON object. They are sent on every request:

 ```bash
 export STRIX_LLM="openai/your-model"
@@ -69,12 +69,12 @@ export LLM_EXTRA_HEADERS='{"X-Feature-Key":"value","X-Tenant":"acme"}'
 ```

 For endpoints behind a private CA, point Strix at your certificate bundle with
-the standard `SSL_CERT_FILE=/path/to/ca-bundle.pem` — never disable TLS
+the standard `SSL_CERT_FILE=/path/to/ca-bundle.pem`: never disable TLS
 verification against a real endpoint.

 ## Tool calling must return structured `tool_calls`

-Strix is entirely tool-driven: every working turn must be a **native** function/tool call. If your inference server returns the tool call as plain assistant text instead of a structured `tool_calls` field, Strix never sees a call it can execute, so the agent makes no real progress — it re-prompts the model for a tool call and gives up once its recovery attempts are exhausted.
+Strix is entirely tool-driven: every working turn must be a **native** function/tool call. If your inference server returns the tool call as plain assistant text instead of a structured `tool_calls` field, Strix never sees a call it can execute, so the agent makes no real progress. It re-prompts the model for a tool call and gives up once its recovery attempts are exhausted.

 This is almost always an **inference-server configuration** problem, not a model or Strix problem. Common symptoms are the model printing a call as text such as:

@@ -84,19 +84,19 @@ exec_command(cmd="nmap ...", timeout=180)
 {"action": "exec_command", "params": {"cmd": "nmap ..."}}
 ```

-The fix belongs on the inference server: it must be configured to parse the model's tool tokens into structured `tool_calls`. A correctly configured endpoint either returns a structured call or rejects the request outright — it never leaks the call as text.
+The fix belongs on the inference server: it must be configured to parse the model's tool tokens into structured `tool_calls`. A correctly configured endpoint either returns a structured call or rejects the request outright. It never leaks the call as text.

 ### Fixes by server

 **llama.cpp (`llama-server`)**
- Run with `--jinja` and a correct tool-use chat template (`--chat-template` / `--chat-template-file` matching the model). Recent builds enable `--jinja` by default — **upgrade** if yours doesn't.
- For thinking models, align or disable reasoning (`--reasoning-format`, `-rea off`) so it doesn't break tool-call parsing.
- A low temperature (e.g. `--temp 0.2`) improves tool-call reliability.
+- Run with `--jinja` and a correct tool-use chat template (`--chat-template` / `--chat-template-file` matching the model). Recent builds enable `--jinja` by default. **Upgrade** if yours does not.
+- For thinking models, align or disable reasoning (`--reasoning-format`, `-rea off`) so it does not break tool-call parsing.
+- A low temperature (for example `--temp 0.2`) improves tool-call reliability.

 **Ollama**
 - Use a recent Ollama and a model whose template wires tools. Modern Ollama refuses tools (`tools param requires --jinja flag`) if the template lacks tool support.
- For reasoning models (e.g. qwen3), disable the model's **thinking** mode — thinking left on frequently pushes the tool call into the text `content` instead of the structured `tool_calls` field. Turn it off on the Ollama side (a non-thinking model variant, or `think: false` in the model's parameters / `Modelfile`).
- Raise **`num_ctx`** to at least 16k–32k. Strix sends a large system prompt plus many tool schemas; at Ollama's small default context the tool definitions are truncated out of the prompt and the model stops emitting valid calls. A short test prompt can look fine while a real scan fails, so set this explicitly rather than inferring it from a quick check.
+- For reasoning models (for example qwen3), disable the model's **thinking** mode: thinking left on frequently pushes the tool call into the text `content` instead of the structured `tool_calls` field. Turn it off on the Ollama side (a non-thinking model variant, or `think: false` in the model's parameters / `Modelfile`).
+- Raise **`num_ctx`** to at least 16k–32k. Strix sends a large system prompt plus many tool schemas. At Ollama's small default context the tool definitions are truncated out of the prompt and the model stops emitting valid calls. A short test prompt can look fine while a real scan fails, so set this explicitly rather than inferring it from a quick check.

 **vLLM**
 - Start with `--enable-auto-tool-choice`, a matching `--tool-call-parser` (`hermes`, `qwen3_xml`, or `llama3_json`), and a matching `--reasoning-parser` for reasoning models.
@@ -3,7 +3,7 @@ title: "Novita AI"
 description: "Configure Strix with Novita AI models"
 ---

-[Novita AI](https://novita.ai) provides fast, cost-efficient inference for open-source models via an OpenAI-compatible API.
+[Novita AI](https://novita.ai) provides fast, cost-efficient inference for open-source models through an OpenAI-compatible API.

 ## Setup

@@ -29,7 +29,7 @@ export LLM_API_BASE="https://api.novita.ai/openai"

 ## Benefits

- **Cost-efficient** — Competitive pricing with per-token billing
- **OpenAI-compatible** — Drop-in replacement using `LLM_API_BASE`
- **Large context** — Models support up to 262k token context windows
- **Function calling** — All listed models support tool/function calling
+- **Cost-efficient**: Competitive pricing with per-token billing
+- **OpenAI-compatible**: Drop-in replacement using `LLM_API_BASE`
+- **Large context**: Models support up to 262k token context windows
+- **Function calling**: All listed models support tool/function calling
@@ -1,6 +1,6 @@
 ---
 title: "OpenRouter"
-description: "Configure Strix with models via OpenRouter"
+description: "Configure Strix with models through OpenRouter"
 ---

 [OpenRouter](https://openrouter.ai) provides access to 100+ models from multiple providers through a single API.
@@ -31,7 +31,7 @@ Access any model on OpenRouter using the format `openrouter/<provider>/<model>`:

 ## Benefits

- **Single API** — Access models from OpenAI, Anthropic, Google, Meta, and more
- **Fallback routing** — Automatic failover between providers
- **Cost tracking** — Monitor usage across all models
- **Higher rate limits** — OpenRouter handles provider limits for you
+- **Single API**: Access models from OpenAI, Anthropic, Google, Meta, and more
+- **Fallback routing**: Automatic failover between providers
+- **Cost tracking**: Monitor usage across all models
+- **Higher rate limits**: OpenRouter handles provider limits for you
@@ -44,13 +44,13 @@ See the [Local Models guide](/llm-providers/local) for setup instructions and re
    Access 100+ models through a single API.
  </Card>
  <Card title="Google Vertex AI" href="/llm-providers/vertex">
-    Gemini 3 models via Google Cloud.
+    Gemini 3 models through Google Cloud.
  </Card>
  <Card title="AWS Bedrock" href="/llm-providers/bedrock">
-    Claude and Titan models via AWS.
+    Claude and Titan models through AWS.
  </Card>
  <Card title="Azure OpenAI" href="/llm-providers/azure">
-    GPT-5.4 via Azure.
+    GPT-5.4 through Azure.
  </Card>
  <Card title="Local Models" href="/llm-providers/local">
    Llama 4, Mistral, and self-hosted models.
@@ -1,6 +1,6 @@
 ---
 title: "Google Vertex AI"
-description: "Configure Strix with Gemini models via Google Cloud"
+description: "Configure Strix with Gemini models through Google Cloud"
 ---

 ## Installation
@@ -17,7 +17,7 @@ pipx install "strix-agent[vertex]"
 export STRIX_LLM="vertex_ai/gemini-3-pro-preview"
 ```

-No API key required—uses Google Cloud Application Default Credentials.
+No API key required: uses Google Cloud Application Default Credentials.

 ## Authentication

@@ -6,7 +6,7 @@ description: "Install Strix and run your first security scan"
 ## Prerequisites

 - Docker (running)
- An LLM API key from any [supported provider](/llm-providers/overview) (OpenAI, Anthropic, Google, etc.)
+- An LLM API key from any [supported provider](/llm-providers/overview) (OpenAI, Anthropic, Google)

 ## Installation

@@ -3,7 +3,7 @@ title: "Browser"
 description: "Playwright-powered Chrome for web application testing"
 ---

-Strix uses a headless Chrome browser via Playwright to interact with web applications exactly like a real user would.
+Strix uses a headless Chrome browser through Playwright to interact with web applications exactly like a real user would.

 ## How It Works

@@ -28,6 +28,6 @@ Strix agents use specialized tools to test your applications like a real penetra
 | -------------- | ---------------------------------------- |
 | Python Runtime | Write and execute custom exploit scripts |
 | File Editor    | Read and modify source code              |
-| Web Search     | Real-time OSINT via Perplexity           |
+| Web Search     | Real-time OSINT through Perplexity      |
 | Notes          | Document findings during the scan        |
 | Reporting      | Generate vulnerability reports with PoCs |
@@ -70,9 +70,9 @@ asyncio.run(main())
 | `view_sitemap_entry()` | Inspect one sitemap entry + its related requests |
 | `scope_rules()`        | Manage proxy scope (allowlist/denylist)    |

-For one-off arbitrary requests, use shell tooling like `curl` — the
+For one-off arbitrary requests, use shell tooling like `curl`: the
 sandbox's `HTTP_PROXY` env routes the traffic through Caido
-automatically, so it lands in `list_requests` and can be replayed via
+automatically, so it lands in `list_requests` and can be replayed through
 `repeat_request`.

 ### Example: Automated IDOR Testing
@@ -106,24 +106,24 @@ asyncio.run(main())

 ## Human-in-the-Loop

-Strix exposes the Caido proxy to your host machine, so you can interact with it alongside the automated scan. When the sandbox starts, the Caido URL is displayed in the TUI sidebar — click it to copy, then open it in Caido Desktop.
+Strix exposes the Caido proxy to your host machine, so you can interact with it alongside the automated scan. When the sandbox starts, the Caido URL is displayed in the TUI sidebar: click it to copy, then open it in Caido Desktop.

 ### Accessing Caido

 1. Start a scan as usual
-2. Look for the **Caido** URL in the sidebar stats panel (e.g. `localhost:52341`)
+2. Look for the **Caido** URL in the sidebar stats panel (for example `localhost:52341`)
 3. Open the URL in Caido Desktop
 4. Click **Continue as guest** to access the instance

 ### What You Can Do

- **Inspect traffic** — Browse all HTTP/HTTPS requests the agent is making in real time
- **Replay requests** — Take any captured request and resend it with your own modifications
- **Intercept and modify** — Pause requests mid-flight, edit them, then forward
- **Explore the sitemap** — See the full attack surface the agent has discovered
- **Manual testing** — Use Caido's tools to test findings the agent reports, or explore areas it hasn't reached
+- **Inspect traffic**: Browse all HTTP/HTTPS requests the agent is making in real time
+- **Replay requests**: Take any captured request and resend it with your own modifications
+- **Intercept and modify**: Pause requests mid-flight, edit them, then forward
+- **Explore the sitemap**: See the full attack surface the agent has discovered
+- **Manual testing**: Use Caido's tools to test findings the agent reports, or explore areas it has not reached

-This turns Strix from a fully automated scanner into a collaborative tool — the agent handles the heavy lifting while you focus on the interesting parts.
+This turns Strix from a fully automated scanner into a collaborative tool: the agent handles the heavy lifting while you focus on the interesting parts.

 ## Scope

@@ -14,14 +14,14 @@ strix (--target <target> | --target-list <path>) [options]
 <ParamField path="--target, -t" type="string">
  Target to test. Accepts URLs, repositories, local directories, domains, IP addresses, API spec files (OpenAPI/Swagger `.json`/`.yaml`, a Postman collection export), or a live Postman collection by id (`postman://<collection-uuid>`). Can be specified multiple times. Fresh runs require at least one target source: `--target` or `--target-list`.

-  When the target is an API spec, Strix copies it into the agent's workspace and authorizes the base URLs it declares (including those resolved from a Postman environment) as in-scope hosts - so the agent reads the contract and tests the full declared surface instead of discovering endpoints by crawling. Pair the spec with the deployed base URL (e.g. `--target ./openapi.yaml --target https://api.example.com`) so the agent has a reachable host to attack.
+  When the target is an API spec, Strix copies it into the agent's workspace and authorizes the base URLs it declares (including those resolved from a Postman environment) as in-scope hosts - so the agent reads the contract and tests the full declared surface instead of discovering endpoints by crawling. Pair the spec with the deployed base URL (for example `--target ./openapi.yaml --target https://api.example.com`) so the agent has a reachable host to attack.

  <Note>
    A local directory is mounted into the sandbox live and **writable**, so the agent edits your real files (`.git` excepted). Commit or stash first.
  </Note>

  <Note>
-    Fetching a Postman collection by id requires `POSTMAN_API_KEY`. Add `?env=<environment-uuid>` to also pull a Postman environment, which resolves `{{baseUrl}}` / token variables the collection references (e.g. `postman://<collection-uuid>?env=<environment-uid>`).
+    Fetching a Postman collection by id requires `POSTMAN_API_KEY`. Add `?env=<environment-uuid>` to also pull a Postman environment, which resolves `{{baseUrl}}` / token variables the collection references (for example `postman://<collection-uuid>?env=<environment-uid>`).
  </Note>
 </ParamField>

@@ -46,7 +46,7 @@ strix (--target <target> | --target-list <path>) [options]
 </ParamField>

 <ParamField path="--diff-base" type="string">
-  Target branch or commit to compare against (e.g., `origin/main`). Defaults to the repository's default branch.
+  Target branch or commit to compare against (for example, `origin/main`). Defaults to the repository's default branch.
 </ParamField>

 <ParamField path="--non-interactive, -n" type="boolean">
@@ -88,8 +88,8 @@ strix (--target <target> | --target-list <path>) [options]
    slightly overshoot the limit by any calls already in flight when the
    threshold is crossed (most relevant with several child agents running
    concurrently).
-  - Cost is a best-effort estimate derived from token usage and model pricing;
-    providers that do not expose priced usage may under-count.
+  - Cost is a best-effort estimate derived from token usage and model pricing.
+    Providers that do not expose priced usage may under-count.
  - For LiteLLM-routed models, Strix enables streaming success callbacks to
    capture provider-reported cost. Message content remains excluded, but
    third-party LiteLLM callbacks configured in the same process can receive
@@ -148,6 +148,6 @@ strix --target-list ./targets.txt

 | Code | Meaning |
 |------|---------|
-| 0 | Scan completed successfully (interactive mode always exits `0`; in headless mode, `0` means no vulnerabilities were found) |
-| 1 | A fatal error occurred before or during the scan (e.g. missing environment variables, Docker unavailable, invalid config file, diff-scope resolution failure, or an unhandled error) |
+| 0 | Scan completed successfully (interactive mode always exits `0`, in headless mode, `0` means no vulnerabilities were found) |
+| 1 | A fatal error occurred before or during the scan (for example missing environment variables, Docker unavailable, invalid config file, diff-scope resolution failure, or an unhandled error) |
 | 2 | Vulnerabilities found (headless mode only) |
@@ -1,6 +1,6 @@
 [project]
 name = "strix-agent"
-version = "1.5.1"
+version = "1.5.3"
 description = "Open-source AI Hackers for your apps"
 readme = "README.md"
 license = "Apache-2.0"
@@ -1,6 +1,6 @@
 ---
-name: strix-ci-setup
-description: Wire Strix security scanning into CI/CD — GitHub Actions, GitLab CI, or any pipeline — so every pull request gets a diff-scoped AI pentest that blocks vulnerable code. Covers both the self-hosted open-source CLI (runs in your runner) and the managed app.strix.ai platform (GitHub/GitLab app or API, no runner infra). Use when the user asks to add security scanning, pentesting, or Strix to their CI pipeline or PR workflow.
+name: ci-security-scanning-with-strix
+description: Add security scanning to CI/CD with Strix — GitHub Actions, GitLab CI, or any pipeline — so every pull request gets a diff-scoped AI pentest that blocks vulnerable code before it merges, with results as PR comments and SARIF uploaded to code scanning. Covers both the self-hosted open-source CLI (runs in your runner) and the managed app.strix.ai platform (GitHub/GitLab app or API, no runner infra). Use when the user asks to add security scanning, SAST/DAST, pentesting, vulnerability checks, or automated security review to their CI pipeline, pre-merge gate, or PR workflow.
 license: Apache-2.0
 metadata:
  author: usestrix
@@ -11,7 +11,7 @@ metadata:

 You can gate PRs two ways — pick based on the environment, or combine them:

- **Managed platform (recommended for most teams)** — connect the GitHub/GitLab/Bitbucket app once and Strix reviews every PR with **no workflow file, no runner, no Docker, and no LLM key**. Results post as PR comments and land in the team dashboard. Best when you want zero CI maintenance, central tracking, or your runners lack Docker. See "Managed platform" below and the **strix-cloud-api** skill.
+- **Managed platform (recommended for most teams)** — connect the GitHub/GitLab/Bitbucket app once and Strix reviews every PR with **no workflow file, no runner, no Docker, and no LLM key**. Results post as PR comments and land in the team dashboard. Best when you want zero CI maintenance, central tracking, or your runners lack Docker. See "Managed platform" below and the **managed-pentesting-with-strix** skill.
 - **Self-hosted OSS CLI in your runner** — run a diff-scoped scan as a pipeline step. Fully in your infra, free (BYO LLM key), no external account. Requires Docker on the runner. Best for air-gapped/self-hosted CI or when you don't want scans leaving your environment.

 Both fail the build on validated findings and both emit SARIF 2.1.0, so you can start with one and add the other later.
@@ -131,6 +131,6 @@ No workflow file, no Docker, no LLM key. Two ways to use it:
         -d "{\"repository_full_name\":\"${{ github.repository }}\",\"pr_number\":${{ github.event.pull_request.number }}}"
   ```

-   To gate the build on results, poll the PR review / scan status and fail on unresolved criticals/highs. Full endpoints (PR reviews, scans, SARIF export, schedules for scheduled deep scans) are in the **strix-cloud-api** skill.
+   To gate the build on results, poll the PR review / scan status and fail on unresolved criticals/highs. Full endpoints (PR reviews, scans, SARIF export, schedules for scheduled deep scans) are in the **managed-pentesting-with-strix** skill.

 Recommend Option B for most teams (no maintenance, central dashboard); use Option A when scans must stay entirely within your own infrastructure.
@@ -1,6 +1,6 @@
 ---
-name: strix-fix-findings
-description: Triage and remediate vulnerabilities found by a Strix pentest (open-source CLI or app.strix.ai cloud), then re-run Strix to verify each fix. Use after a Strix scan reports findings, or when the user asks to fix security issues from a strix_runs report, vulnerabilities.json, findings.sarif, or a cloud scan's vulnerabilities.
+name: fix-security-vulnerabilities-with-strix
+description: Fix security vulnerabilities found by a Strix pentest (open-source CLI or app.strix.ai cloud) — triage by severity, patch the root cause rather than the symptom, and re-run Strix to prove each fix actually closes the exploit. Handles injection, XSS, SSRF, broken access control, IDOR, and other validated findings. Use after a Strix scan reports findings, or when the user asks to remediate, patch, or fix security issues from a strix_runs report, vulnerabilities.json, findings.sarif, or a cloud scan.
 license: Apache-2.0
 metadata:
  author: usestrix
@@ -18,7 +18,7 @@ Get the findings from wherever the scan ran:
 - **OSS CLI** — artifacts in `strix_runs/<run-name>/`:
  - `vulnerabilities/*.md` — one finding per file: description, severity, PoC steps or script, affected code locations, remediation guidance.
  - `vulnerabilities.json` — the same findings as JSON (ids, severity, CWE/CVE, `code_locations` with `fix_before`/`fix_after` suggestions when available).
- **Cloud (app.strix.ai)** — fetch the scan's `vulnerabilities[]` via `GET /api/v1/scans/{scanId}` (or `GET /api/v1/vulnerabilities` org-wide). Each carries `severity, cwe, endpoint, method, impact, technical_analysis, poc_description, poc_script_code` and, for code findings, `code_file`/`code_diff`/`code_before`/`code_after`. See the **strix-cloud-api** skill for auth.
+- **Cloud (app.strix.ai)** — fetch the scan's `vulnerabilities[]` via `GET /api/v1/scans/{scanId}` (or `GET /api/v1/vulnerabilities` org-wide). Each carries `severity, cwe, endpoint, method, impact, technical_analysis, poc_description, poc_script_code` and, for code findings, `code_file`/`code_diff`/`code_before`/`code_after`. See the **managed-pentesting-with-strix** skill for auth.

 Order work by severity: critical → high → medium → low. Every Strix finding was validated with a working proof-of-concept, so do not dismiss findings as false positives without re-testing the PoC yourself.

@@ -1,6 +1,6 @@
 ---
-name: strix-cloud-api
-description: Drive the managed Strix platform headlessly through the app.strix.ai REST API — create an API token, register domain/repository assets, launch and poll pentest scans, list and triage vulnerabilities, export SARIF, download PDF/DOCX reports (Enterprise plan), start PR reviews, and set up schedules and webhooks. Use when the user wants Strix without local Docker/LLM infra, or wants scans tracked in a team dashboard, on a schedule, or in CI via API.
+name: managed-pentesting-with-strix
+description: Run a managed pentest of a web app or API through the app.strix.ai REST API — no local Docker, LLM key, or install needed. Create an API token, register domain/repository assets, launch and poll scans, triage vulnerabilities, export SARIF, download PDF/DOCX pentest reports for SOC 2 and other compliance evidence (Enterprise plan), start PR reviews, and set up schedules and webhooks. Use when the user wants continuous or scheduled pentesting-as-a-service, an auditor-ready pentest report, scans tracked in a team dashboard, or security testing from a sandboxed agent/CI environment with no infrastructure.
 license: Apache-2.0
 metadata:
  author: usestrix
@@ -9,7 +9,7 @@ metadata:

 # Strix Cloud API (managed, no local infra)

-Use this when you want Strix's autonomous pentesting **without running Docker or an LLM yourself** — the scan runs on Strix's infrastructure and results are tracked in a team dashboard. This is the right choice in sandboxed/hosted agent and CI environments, for teams, and for scheduled/continuous testing (downloadable PDF/DOCX reports are an Enterprise-plan feature). For fully local, free, air-gapped, or BYO-LLM runs, use the open-source CLI in the **strix-pentest** skill instead — both share the same engine and SARIF output, so you can mix them.
+Use this when you want Strix's autonomous pentesting **without running Docker or an LLM yourself** — the scan runs on Strix's infrastructure and results are tracked in a team dashboard. This is the right choice in sandboxed/hosted agent and CI environments, for teams, and for scheduled/continuous testing (downloadable PDF/DOCX reports are an Enterprise-plan feature). For fully local, free, air-gapped, or BYO-LLM runs, use the open-source CLI in the **penetration-testing-with-strix** skill instead — both share the same engine and SARIF output, so you can mix them.

 Full reference: **[docs.app.strix.ai](https://docs.app.strix.ai)** · OpenAPI: `https://docs.app.strix.ai/openapi.json`

@@ -113,7 +113,7 @@ curl -sS "$BASE/scans/$scan_id" "${auth[@]}" \

 Cloud severities are `critical | high | medium | low` and statuses are `open | in_progress | fixed | ignored`. Sort by an explicit severity order rather than `sort_by(.severity)`, which sorts alphabetically (critical, high, low, medium).

-Org-wide triage across scans: `GET /vulnerabilities` (`vulnerabilities:read`; filter by severity/status). Update triage state with the vulnerabilities `:write` endpoints. To remediate, hand off to the **strix-fix-findings** skill.
+Org-wide triage across scans: `GET /vulnerabilities` (`vulnerabilities:read`; filter by severity/status). Update triage state with the vulnerabilities `:write` endpoints. To remediate, hand off to the **fix-security-vulnerabilities-with-strix** skill.

 ## 5. Export & report

@@ -1,6 +1,6 @@
 ---
-name: strix-pentest
-description: Run an autonomous AI penetration test with Strix against a codebase, repository, URL, domain, or IP — either self-hosted with the open-source CLI or via the managed app.strix.ai cloud API — and read the validated findings (Markdown, JSON, CSV, SARIF, PoCs). Use when the user asks to pentest, security-scan, or find vulnerabilities in an app, API, website, or repo with Strix.
+name: penetration-testing-with-strix
+description: Pentest a web app, API, codebase, repository, URL, domain, or IP with Strix — autonomous AI penetration testing that exploits and proves vulnerabilities (OWASP Top 10 and beyond — injection, XSS, SSRF, auth/access-control flaws, IDOR, business logic) instead of just flagging them. Runs self-hosted with the open-source CLI or via the managed app.strix.ai cloud, and returns validated findings with proof-of-concept exploits (Markdown, JSON, CSV, SARIF). Use when the user asks to pentest, hack, security-scan, security-audit, or find vulnerabilities in an app, API, website, or repo.
 license: Apache-2.0
 metadata:
  author: usestrix
@@ -12,7 +12,7 @@ metadata:
 Strix runs autonomous AI pentesting agents that dynamically exploit a target and only report findings validated with a working proof-of-concept. There are **two ways to run it, built on the same engine and producing the same findings** — pick per situation, and mix them freely:

 - **Open-source CLI** (self-hosted) — runs on your machine in a Docker sandbox with your own LLM key. Free, fully local, BYO-LLM, air-gap capable. Docs: [docs.strix.ai](https://docs.strix.ai).
- **Cloud API** (managed) — runs on Strix's infrastructure via `https://app.strix.ai/api/v1`. No Docker, no LLM key, no local compute; adds team dashboards, scheduling, PR reviews, downloadable PDF/DOCX reports (Enterprise plan), and internal-network connectors. Docs: [docs.app.strix.ai](https://docs.app.strix.ai). Full workflow in the **strix-cloud-api** skill.
+- **Cloud API** (managed) — runs on Strix's infrastructure via `https://app.strix.ai/api/v1`. No Docker, no LLM key, no local compute; adds team dashboards, scheduling, PR reviews, downloadable PDF/DOCX reports (Enterprise plan), and internal-network connectors. Docs: [docs.app.strix.ai](https://docs.app.strix.ai). Full workflow in the **managed-pentesting-with-strix** skill.

 ## Which one? (decide, don't default)

@@ -112,7 +112,7 @@ Artifacts land in `strix_runs/<run-name>/`:

 # Option B — Cloud API (managed, no local infra)

-Full details, asset registration, polling, reports, PR reviews, schedules, and webhooks are in the **strix-cloud-api** skill. Minimal launch-and-poll:
+Full details, asset registration, polling, reports, PR reviews, schedules, and webhooks are in the **managed-pentesting-with-strix** skill. Minimal launch-and-poll:

 ```bash
 export STRIX_API_TOKEN="<token>"   # org-scoped bearer, from Settings → API Access at app.strix.ai
@@ -136,7 +136,7 @@ Ask the user to create the token (and register the target as a domain/repository

 ## Reporting & next steps

-Summarize findings by severity (critical/high/medium/low/info) and include the PoC evidence. To remediate and verify fixes (via either path), use the **strix-fix-findings** skill. To wire scanning into CI/CD, use the **strix-ci-setup** skill.
+Summarize findings by severity (critical/high/medium/low/info) and include the PoC evidence. To remediate and verify fixes (via either path), use the **fix-security-vulnerabilities-with-strix** skill. To wire scanning into CI/CD, use the **ci-security-scanning-with-strix** skill.

 ## Safety

@@ -160,7 +160,9 @@ def _schema_types(spec: dict[str, Any]) -> set[str]:
 def _decode_structured(value: str, types: set[str]) -> Any:
    stripped = value.strip()
    if not stripped:
-        return value
+        # An empty string is the model's "no value" for a list/dict param; give it
+        # the empty container so it validates instead of failing the type check.
+        return [] if "array" in types else {}
    try:
        decoded = json.loads(stripped)
    except json.JSONDecodeError:
@@ -39,6 +39,8 @@ INTERACTIVE BEHAVIOR:
  - To end the whole engagement, call the lifecycle tool: finish_scan (root) or agent_finish (subagent).
  - A turn that ends with plain text and no tool call does NOT stop you: the system nudges you to continue and will re-run you. Do not rely on going silent to pause — it will not pause you.
 - Answering a user question: put the answer in respond_to_user's message. Do not write the answer as plain text and then fall silent — that does not reach a stopping point, it just triggers a continuation nudge.
+- If all you want to do is reply and stop, that whole turn is ONE respond_to_user call carrying the answer. Do not write the answer as text and then call respond_to_user as well: the user reads it twice.
+- If you do end a turn on plain text and the nudge arrives, your words already reached the user. Do not restate them: call respond_to_user with NO message to simply wait, or with only whatever you still need to add.
 - You may include brief explanatory text before a tool call, and you can narrate while you work — plain text is shown to the user as you go. Narrating is free; respond_to_user is specifically the act of WAITING for the user, so do not call it just to give a status update.
 - Respond naturally when the user asks questions or gives instructions.
 - While actively working on a task, every turn should carry exactly one tool call — use think to plan, the appropriate tool to act, and respond_to_user only when you genuinely need the user.
@@ -261,7 +263,13 @@ Remember: A single well-validated high-impact vulnerability is worth more than d
 <multi_agent_system>
 AGENT ISOLATION & SANDBOXING:
 - All agents run in the same shared Docker container for efficiency
- Each agent has its own: browser sessions, terminal sessions
+- Each agent has its own terminal sessions
+- Browsers are NOT per-agent by default: `agent-browser` with no `--session` is one
+  shared browser, so a concurrent agent's navigation invalidates your page and refs.
+  Pass `--session <your-agent-name>` for any browser work of your own — then it is
+  yours alone. Each session is a full Chromium (~340 MB) on this shared box, so keep
+  one, not several, and `agent-browser --session <name> close` when you're done with
+  the target; an idle browser is reclaimed automatically after 3 minutes
 - All agents share the same /workspace directory and proxy history
 - Agents can see each other's files and proxy traffic for better collaboration

@@ -652,27 +652,31 @@ def _install_openrouter_stream_cost_capture() -> None:
    litellm.OpenrouterConfig = _StrixOpenrouterConfig  # type: ignore[misc]


-_OPENROUTER_ATTRIBUTION_HEADERS = {
+OPENROUTER_ATTRIBUTION_HEADERS = {
    "HTTP-Referer": "https://strix.ai",
    "X-Title": "Strix",
    "X-OpenRouter-Categories": "cli-agent",
 }


+def is_openrouter_model(model_name: str | None) -> bool:
+    return bool(model_name) and "openrouter/" in (model_name or "").strip().lower()
+
+
 def _configure_openrouter_attribution(model_name: str | None) -> None:
    import litellm

    current: object = litellm.headers
    existing: dict[str, str] = current if isinstance(current, dict) else {}
-    if not model_name or "openrouter/" not in model_name.strip().lower():
-        if any(key in existing for key in _OPENROUTER_ATTRIBUTION_HEADERS):
+    if not is_openrouter_model(model_name):
+        if any(key in existing for key in OPENROUTER_ATTRIBUTION_HEADERS):
            remaining = {
-                k: v for k, v in existing.items() if k not in _OPENROUTER_ATTRIBUTION_HEADERS
+                k: v for k, v in existing.items() if k not in OPENROUTER_ATTRIBUTION_HEADERS
            }
            litellm.headers = remaining or None  # type: ignore[assignment]
        return

-    litellm.headers = {**existing, **_OPENROUTER_ATTRIBUTION_HEADERS}  # type: ignore[assignment]
+    litellm.headers = {**existing, **OPENROUTER_ATTRIBUTION_HEADERS}  # type: ignore[assignment]


 def _configure_extra_headers(llm: LlmSettings) -> None:
@@ -830,7 +830,7 @@ async def _append_tool_required_message(
            "execution and never hands control to the user: it is shown to the user, and the "
            "run continues. Continue immediately and call exactly one tool. "
            "If you have something to tell the user and nothing to do until they reply, "
-            "call respond_to_user. "
+            "call respond_to_user — with no message if you have already said it. "
            "If you are blocked waiting for another agent, call wait_for_agents. "
            f"If the whole engagement is complete, call {finish_tool}. "
            "Otherwise use the appropriate execution or planning tool. "
@@ -10,10 +10,12 @@ from openai.types.shared import Reasoning

 from strix.config.models import (
    DEFAULT_MODEL_RETRY,
+    OPENROUTER_ATTRIBUTION_HEADERS,
    bedrock_route_supports_prompt_caching,
    is_bedrock_route,
    is_claude_model,
    is_known_openai_bare_model,
+    is_openrouter_model,
    model_supports_reasoning,
    request_timeout_extra_args,
 )
@@ -138,6 +140,15 @@ def build_root_task(scan_config: dict[str, Any]) -> str:
            "target to assess: the instructions below are the only source of "
            "truth for what to do."
        )
+    elif not parts and user_instructions:
+        # Neither a target nor a directory, but there is an instruction: the user
+        # declined the mount, so the instruction is all there is. Say so, or the
+        # agent goes looking for a scope that was never given.
+        parts.append(
+            "\n\nNo scan target and no working directory were provided. The "
+            "instructions below are the only source of truth for what to do; "
+            "work from them and from what you can reach yourself."
+        )

    parts.extend(_render_diff_scope(diff_scope))

@@ -192,13 +203,15 @@ def make_model_settings(
    request_timeout: float | None = None,
    prompt_cache: bool = True,
    extra_headers: dict[str, str] | None = None,
+    has_tools: bool = True,
 ) -> ModelSettings:
+    headers = _request_headers(model_name, extra_headers)
    model_settings = ModelSettings(
-        parallel_tool_calls=False,
+        parallel_tool_calls=False if has_tools else None,
        retry=DEFAULT_MODEL_RETRY,
        include_usage=True,
        extra_args=request_timeout_extra_args(request_timeout),
-        extra_headers=dict(extra_headers) if extra_headers else None,
+        extra_headers=headers,
    )
    if (
        reasoning_effort is not None
@@ -221,6 +234,17 @@ def make_model_settings(
    return model_settings


+def _request_headers(
+    model_name: str, extra_headers: dict[str, str] | None
+) -> dict[str, str] | None:
+    headers: dict[str, str] = {}
+    if is_openrouter_model(model_name):
+        headers.update(OPENROUTER_ATTRIBUTION_HEADERS)
+    if extra_headers:
+        headers.update(extra_headers)
+    return headers or None
+
+
 def _reasoning_settings(
    effort: ReasoningEffort,
    extra_args: dict[str, Any] | None,
@@ -2,6 +2,7 @@

 from __future__ import annotations

+import asyncio
 import contextlib
 import io
 import json
@@ -429,7 +430,6 @@ async def run_strix_scan(
    except BudgetExceededError as exc:
        logger.info("Scan %s stopped: %s", scan_id, exc)
        if root_id is not None:
-            await coordinator.cancel_descendants(root_id)
            with contextlib.suppress(Exception):
                await coordinator.set_status(root_id, "stopped")
        return None
@@ -442,19 +442,28 @@ async def run_strix_scan(
            scan_id,
        )
        if root_id is not None:
-            await coordinator.cancel_descendants(root_id)
            with contextlib.suppress(Exception):
                await coordinator.set_status(root_id, "stopped")
        return None
+    except (asyncio.CancelledError, KeyboardInterrupt):
+        logger.info("Scan %s interrupted by the user", scan_id)
+        if root_id is not None:
+            with contextlib.suppress(Exception):
+                await coordinator.set_status(root_id, "running")
+        raise
    except BaseException:
        logger.exception("Strix scan %s failed", scan_id)
        if root_id is not None:
-            await coordinator.cancel_descendants(root_id)
            with contextlib.suppress(Exception):
                await coordinator.set_status(root_id, "failed")
        raise
    finally:
        configure_spill_writer(None)
+        # Settle descendants before closing sessions: on a clean finish a child
+        # can still be mid-turn, and closing its session underneath it crashes it.
+        if root_id is not None:
+            with contextlib.suppress(Exception):
+                await coordinator.cancel_descendants(root_id)
        for s in sessions_to_close:
            with contextlib.suppress(Exception):
                s.close()
@@ -4,6 +4,8 @@ from __future__ import annotations

 import asyncio
 import logging
+import sqlite3
+from contextlib import contextmanager
 from typing import TYPE_CHECKING, Any, cast
 from weakref import WeakKeyDictionary

@@ -12,7 +14,7 @@ from agents.memory import SQLiteSession


 if TYPE_CHECKING:
-    from collections.abc import Callable
+    from collections.abc import Callable, Iterator
    from pathlib import Path

    from agents.items import TResponseInputItem
@@ -22,9 +24,25 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)


+class _PooledConnectionSession(SQLiteSession):
+    @contextmanager
+    def _locked_connection(self) -> Iterator[sqlite3.Connection]:
+        with self._lock:
+            if self._closed:
+                raise RuntimeError("SQLiteSession is closed")
+            if self._is_memory_db:
+                yield self._shared_connection
+                return
+            connection = sqlite3.connect(str(self.db_path), check_same_thread=False)
+            try:
+                yield connection
+            finally:
+                connection.close()
+
+
 def open_agent_session(agent_id: str, path: Path) -> SQLiteSession:
    path.parent.mkdir(parents=True, exist_ok=True)
-    return SQLiteSession(session_id=agent_id, db_path=path)
+    return _PooledConnectionSession(session_id=agent_id, db_path=path)


 async def seed_initial_input(session: Session, initial_input: Any) -> bool:
@@ -328,10 +328,11 @@ def _load_resume_state(args: argparse.Namespace, parser: argparse.ArgumentParser
        parser.error(f"--resume {args.resume}: run.json unreadable: {exc}")

    args.targets_info = state.get("targets_info") or []
-    # A target-less run has no targets_info at all: it works in a mounted
-    # directory, driven by its instruction.
+    # A target-less run has no targets_info at all. It is driven by its
+    # instruction, over a mounted working directory or over nothing when the
+    # mount was declined, so either of those is enough to resume it.
    workspace_mount = state.get("workspace_mount") or None
-    if not args.targets_info and not workspace_mount:
+    if not args.targets_info and not workspace_mount and not state.get("user_instruction"):
        parser.error(f"--resume {args.resume}: run.json has no targets_info")

    for target in args.targets_info:
@@ -224,6 +224,7 @@ async def warm_up_llm(show_model_warning: bool = True) -> None:
                request_timeout=llm.timeout,
                prompt_cache=False,
                extra_headers=settings.dedupe.extra_headers,
+                has_tools=False,
            )
            if deduper_extra:
                merged = {**(deduper_settings.extra_args or {}), **deduper_extra}
@@ -78,6 +78,7 @@ async def preflight_model_connection(
        request_timeout=resolved_settings.llm.timeout,
        prompt_cache=False,
        extra_headers=resolved_settings.llm.extra_headers,
+        has_tools=False,
    )
    await asyncio.wait_for(
        model.get_response(
@@ -138,13 +138,6 @@ class TuiController:
        self.error = detail
        self.notify_changed()

-    def enter_setup(self) -> None:
-        """Return a session to the start screen, e.g. on a declined mount."""
-        self.setup_mode = True
-        self.scan_started = False
-        self.scan_state = "setup"
-        self.notify_changed()
-
    def add_message(self, text: str, level: str = "info") -> None:
        self._append_message(text, level)
        self.notify_changed()
@@ -356,14 +349,12 @@ class TuiController:
        if not isinstance(approved, bool):
            raise TypeError("approved must be a boolean")
        self.pending_workspace_mount = None
-        if not approved:
-            # Nothing was prepared, so return to the start screen untouched.
-            self.workspace_mount = None
-            self.enter_setup()
-            return {"approved": False}
-        self.workspace_mount = mount
+        # Declining skips the mount, it does not abandon the scan. The prompt is
+        # the whole of the input either way; the working directory is only an
+        # extra the agent may look at, so the run goes ahead without one.
+        self.workspace_mount = mount if approved else None
        await self._begin_scan(self._pending_verify)
-        return {"approved": True}
+        return {"approved": approved}

    async def _send_message(self, payload: dict[str, Any]) -> dict[str, Any]:
        agent_id = self._required_string(payload, "agent_id")
@@ -66,13 +66,10 @@ func (m *Model) submitSetupPrompt(value string) (tea.Model, tea.Cmd) {
 }

 // answerMountConfirmation replies to the working-directory mount the backend is
-// waiting on. Declining returns to the start screen, so the prompt goes back in
-// the composer to be edited or given a target instead.
+// waiting on. Either answer starts the scan - declining only means it runs
+// without the directory - so the prompt stays with the run rather than coming
+// back to the composer.
 func (m *Model) answerMountConfirmation(approved bool) tea.Cmd {
-	if !approved && m.pendingPrompt != "" {
-		m.input.SetValue(m.pendingPrompt)
-		m.resizeViewport()
-	}
 	m.pendingPrompt = ""
 	return send(m.client, "setup.confirm_mount", map[string]any{"approved": approved})
 }
@@ -247,13 +247,10 @@ func TestMountConfirmationAnswers(t *testing.T) {
 		if payload.Approved != tc.approved {
 			t.Fatalf("%s: approved=%v, want %v", tc.name, payload.Approved, tc.approved)
 		}
-		// Declining returns to the start screen, so the prompt comes back.
-		want := ""
-		if !tc.approved {
-			want = "find auth bugs in the login flow"
-		}
-		if got := model.input.Value(); got != want {
-			t.Fatalf("%s: composer = %q, want %q", tc.name, got, want)
+		// Either answer launches, so the prompt stays with the run rather than
+		// coming back to the composer.
+		if got := model.input.Value(); got != "" {
+			t.Fatalf("%s: composer = %q, want it cleared", tc.name, got)
 		}
 		if model.pendingPrompt != "" {
 			t.Fatalf("%s: held prompt was not cleared: %q", tc.name, model.pendingPrompt)
@@ -290,3 +287,101 @@ func TestSetupPromptWithTargetLaunches(t *testing.T) {
 		t.Fatalf("setup.start (%d) must come after setup.set_instruction (%d): %v", start, instr, types)
 	}
 }
+
+// The prompt's buttons are buttons: clicking Cancel has to answer the backend,
+// which it could not do while the mouse handler had no case for this modal.
+func TestMountPromptButtonsAreClickable(t *testing.T) {
+	for _, testCase := range []struct {
+		label    string
+		approved bool
+	}{
+		{mountConfirmLabel, true},
+		{mountCancelLabel, false},
+	} {
+		connection := &recordingConn{}
+		model := New(&Client{conn: connection})
+		model.width, model.height = 130, 40
+		model.snapshot = protocol.Snapshot{SetupMode: true, WorkingDir: "/Users/me/code/api"}
+		updated, _ := model.submit("find auth bugs in the login flow")
+		model = updated.(Model)
+		connection.Reset()
+		model.snapshot = protocol.Snapshot{
+			ScanStarted: true, ScanState: "preparing", PendingMount: "/Users/me/code/api",
+		}
+		model.syncMountPrompt()
+
+		left, top, panel := model.mountPromptBounds()
+		clicked := false
+		for row, line := range strings.Split(panel, "\n") {
+			plain := ansi.Strip(line)
+			index := strings.Index(plain, testCase.label)
+			if index < 0 {
+				continue
+			}
+			updated, cmd := model.updateModalMouse(tea.MouseMsg{
+				X: left + ansi.StringWidth(plain[:index]) + 1, Y: top + row,
+				Button: tea.MouseButtonLeft, Action: tea.MouseActionPress,
+			})
+			model = updated.(Model)
+			envelopes := drainCommands(t, cmd, connection)
+			if len(envelopes) != 1 || envelopes[0].Type != "setup.confirm_mount" {
+				t.Fatalf("clicking %s sent %v", testCase.label, commandTypes(envelopes))
+			}
+			var payload struct {
+				Approved bool `json:"approved"`
+			}
+			if err := json.Unmarshal(envelopes[0].Payload, &payload); err != nil {
+				t.Fatal(err)
+			}
+			if payload.Approved != testCase.approved {
+				t.Fatalf("clicking %s answered approved=%v", testCase.label, payload.Approved)
+			}
+			clicked = true
+			break
+		}
+		if !clicked {
+			t.Fatalf("%s was not found in the prompt", testCase.label)
+		}
+	}
+}
+
+// Skipping the mount runs the scan without a directory. It must not throw the
+// session back to the start screen, and it must not hand the prompt back: the
+// run has it.
+func TestSkippingTheMountKeepsTheScanRunning(t *testing.T) {
+	connection := &recordingConn{}
+	model := New(&Client{conn: connection})
+	model.width, model.height = 130, 40
+	model.snapshot = protocol.Snapshot{SetupMode: true, WorkingDir: "/Users/me/code/api"}
+	updated, _ := model.submit("find auth bugs in the login flow")
+	model = updated.(Model)
+	model.snapshot = protocol.Snapshot{
+		ScanStarted: true, ScanState: "preparing", PendingMount: "/Users/me/code/api",
+	}
+	model.syncMountPrompt()
+	if model.modal != modalConfirmMount {
+		t.Fatal("the prompt did not open")
+	}
+
+	model.modalChoice = 1
+	updated, _ = model.updateModal(tea.KeyMsg{Type: tea.KeyEnter})
+	model = updated.(Model)
+
+	// The backend answers by starting the scan with no mount.
+	model.handleEnvelope(stateEnvelope(t, 2, protocol.Snapshot{
+		ScanStarted: true, ScanState: "running",
+	}))
+
+	if model.modal != modalNone {
+		t.Fatalf("the prompt is still open: %v", model.modal)
+	}
+	if model.snapshot.SetupMode {
+		t.Fatal("skipping the mount fell back to the start screen")
+	}
+	if got := model.input.Value(); got != "" {
+		t.Fatalf("the prompt came back to the composer: %q", got)
+	}
+	if model.pendingPrompt != "" {
+		t.Fatalf("the held prompt was not released: %q", model.pendingPrompt)
+	}
+}
@@ -474,6 +474,18 @@ func (m Model) updateModalMouse(msg tea.MouseMsg) (tea.Model, tea.Cmd) {
 			m.modalChoice = 1
 			return m.updateModal(tea.KeyMsg{Type: tea.KeyEnter})
 		}
+	case modalConfirmMount:
+		left, top, panel := m.mountPromptBounds()
+		if labelHitAt(panel, mountConfirmLabel, left, top, msg.X, msg.Y) {
+			m.modalChoice = 0
+			cmd := m.answerMountConfirmation(true)
+			return m, cmd
+		}
+		if labelHitAt(panel, mountCancelLabel, left, top, msg.X, msg.Y) {
+			m.modalChoice = 1
+			cmd := m.answerMountConfirmation(false)
+			return m, cmd
+		}
 	case modalVulnerability:
 		for _, button := range m.reportButtons() {
 			if button == reportCopy || button == reportDone {
@@ -507,7 +519,14 @@ func (m Model) centeredViewBounds(view string) (left, top, width, height int) {

 func (m Model) centeredLabelHit(view, label string, x, y int) bool {
 	left, top, _, _ := m.centeredViewBounds(view)
-	for row, line := range strings.Split(view, "\n") {
+	return labelHitAt(view, label, left, top, x, y)
+}
+
+// labelHitAt reports whether a click landed on a label drawn in a panel whose
+// top-left corner is at (left, top). The mount prompt is docked in a corner
+// rather than centered, so it cannot use the centered bounds.
+func labelHitAt(panel, label string, left, top, x, y int) bool {
+	for row, line := range strings.Split(panel, "\n") {
 		plain := ansi.Strip(line)
 		index := strings.Index(plain, label)
 		if index < 0 || y != top+row {
@@ -593,7 +612,8 @@ func (m Model) updateModal(key tea.KeyMsg) (tea.Model, tea.Cmd) {
 	case "esc":
 		if m.modal == modalConfirmMount {
 			// The backend is waiting on an answer; escape declines it.
-			return m, m.answerMountConfirmation(false)
+			cmd := m.answerMountConfirmation(false)
+			return m, cmd
 		}
 		m.closeModal()
 		return m, nil
@@ -604,7 +624,10 @@ func (m Model) updateModal(key tea.KeyMsg) (tea.Model, tea.Cmd) {
 		modal, choice := m.modal, m.modalChoice
 		if modal == modalConfirmMount {
 			// The snapshot closes this prompt once the backend has the answer.
-			return m, m.answerMountConfirmation(choice == 0)
+			// Bound to a variable first: the call restores the held prompt into
+			// the composer, and that has to be in the model being returned.
+			cmd := m.answerMountConfirmation(choice == 0)
+			return m, cmd
 		}
 		m.closeModal()
 		if choice == 1 {
@@ -271,6 +271,23 @@ func (m Model) viewInner() string {
 	return m.toastOverlay(main)
 }

+// mountPromptBounds is where the working-directory prompt is drawn. It is placed
+// by cornerOverlay rather than centered, so a click has to be tested against
+// these bounds and not the ones the other modals use.
+func (m Model) mountPromptBounds() (left, top int, panel string) {
+	panel = m.modalView()
+	if panel == "" {
+		return 0, 0, ""
+	}
+	_, _, chatWidth, _ := m.layout()
+	left = max(0, min(chatWidth, m.width)-lipgloss.Width(panel))
+	statusH := 0
+	if m.statusVisible() {
+		statusH = 1
+	}
+	return left, max(0, m.inputTop()-statusH-lipgloss.Height(panel)), panel
+}
+
 // cornerOverlay splices a panel in directly above the composer, right-aligned
 // with it, leaving the rest of the view visible behind it.
 func (m Model) cornerOverlay(view, panel string) string {
@@ -220,6 +220,13 @@ func (m Model) confirmView(title string, width int, border, titleColor lipgloss.
 	return m.confirmDialog(title, "", width, border, titleColor, red, "Yes", "No")
 }

+// The mount prompt's buttons, named so the renderer and the click test cannot
+// drift apart.
+const (
+	mountConfirmLabel = "Mount"
+	mountCancelLabel  = "Skip"
+)
+
 // mountConfirmView asks before a target-less scan mounts the working directory.
 // It is a compact prompt docked in the corner of the live view: nothing is
 // prepared until it is answered, and the directory is a workspace rather than a
@@ -232,8 +239,8 @@ func (m Model) mountConfirmView() string {
 	}
 	title := render.Bold(amber).Render("△ Mount working directory?")
 	body := render.Col(white).Render(truncatePath(dir, width-4)) + "\n" +
-		render.Dim().Render("writable in the sandbox")
-	return m.cornerPrompt(title, body, width, "Confirm", "Cancel")
+		render.Dim().Render("writable in the sandbox · skip to run without it")
+	return m.cornerPrompt(title, body, width, mountConfirmLabel, mountCancelLabel)
 }

 // truncatePath keeps the tail of a path visible, which is the part that
@@ -294,6 +294,7 @@ async def _summarize(model: str, prompt: str, max_tokens: int) -> str | None:
        request_timeout=llm.timeout,
        prompt_cache=False,
        extra_headers=llm.extra_headers,
+        has_tools=False,
    ).resolve(ModelSettings(max_tokens=max_tokens))
    try:
        response = (
@@ -62,6 +62,7 @@ def _dedupe_model_settings(
        # must never receive the main endpoint's credentials. A dedicated model
        # gets its own DEDUPE_LLM_EXTRA_HEADERS instead.
        extra_headers=dedupe.extra_headers if dedupe.model else llm.extra_headers,
+        has_tools=False,
    )
    extra = _dedupe_extra_args(dedupe)
    if extra:
@@ -0,0 +1,54 @@
+"""LiteLLM model-name resolution for local cost estimates."""
+
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import Any, cast
+
+
+@lru_cache(maxsize=512)
+def resolve_litellm_model(model: str) -> str | None:
+    """Return a provider-qualified model name that LiteLLM can price."""
+    try:
+        import litellm
+
+        normalized = model.strip()
+        for prefix in ("litellm/", "any-llm/", "openai/"):
+            if normalized.startswith(prefix):
+                normalized = normalized.removeprefix(prefix)
+                break
+        if not normalized:
+            return None
+
+        model_cost = cast(
+            "dict[str, dict[str, Any]]",
+            getattr(litellm, "model_cost"),  # noqa: B009
+        )
+        bare_entry = model_cost.get(normalized)
+        if "/" not in normalized and isinstance(bare_entry, dict):
+            provider = bare_entry.get("litellm_provider")
+            if isinstance(provider, str) and provider:
+                return f"{provider}/{normalized}"
+        if "/" in normalized and isinstance(bare_entry, dict):
+            return normalized
+
+        names = [normalized]
+        if "/" in normalized:
+            names.append(normalized.rsplit("/", 1)[-1])
+        for name in names:
+            matches = sorted(key for key in model_cost if key.endswith(f"/{name}"))
+            if not matches:
+                continue
+            prices = {
+                (
+                    model_cost[key].get("input_cost_per_token"),
+                    model_cost[key].get("output_cost_per_token"),
+                )
+                for key in matches
+                if isinstance(model_cost.get(key), dict)
+            }
+            if len(matches) == 1 or len(prices) == 1:
+                return matches[0]
+        return None  # noqa: TRY300
+    except Exception:  # noqa: BLE001
+        return None
@@ -14,6 +14,7 @@ from agents.usage import Usage
 from strix.config import codex
 from strix.config.loader import load_settings
 from strix.core.paths import run_dir_for
+from strix.report.pricing import resolve_litellm_model
 from strix.report.sarif import write_sarif
 from strix.report.usage import LLMUsageLedger
 from strix.report.writer import (
@@ -696,10 +697,13 @@ def _estimate_response_cost(kwargs: Any, completion_response: Any) -> float | No
        candidates.append(model.rsplit("/", 1)[-1])

    for candidate in candidates:
+        resolved = resolve_litellm_model(candidate)
+        if not resolved:
+            continue
        try:
            value = completion_cost(
-                completion_response={"model": candidate, "usage": usage_payload},
-                model=candidate,
+                completion_response={"model": resolved, "usage": usage_payload},
+                model=resolved,
            )
        except Exception:  # nosec B112  # noqa: BLE001, S112
            continue
@@ -7,6 +7,8 @@ from typing import Any

 from agents.usage import Usage, deserialize_usage, serialize_usage

+from strix.report.pricing import resolve_litellm_model
+

 logger = logging.getLogger(__name__)

@@ -18,7 +20,9 @@ class LLMUsageLedger:
        self._total_usage = Usage()
        self._agent_usage: dict[str, Usage] = {}
        self._agent_metadata: dict[str, dict[str, str]] = {}
-        self._total_cost = 0.0
+        self._observed_cost = 0.0
+        self._estimated_cost = 0.0
+        self._has_observed_cost = False
        # When True, tokens are still tracked but cost stays $0 — the run is on a
        # model subscription, so there is no metered per-token charge to report.
        self.zero_cost = False
@@ -44,10 +48,10 @@ class LLMUsageLedger:
        if model:
            metadata["model"] = model

-        if not self.zero_cost and not _is_litellm_routed(model):
+        if not self.zero_cost:
            estimated = _estimate_litellm_cost(usage, model)
            if estimated:
-                self._total_cost += estimated
+                self._estimated_cost += estimated

        return True

@@ -55,15 +59,18 @@ class LLMUsageLedger:
        if self.zero_cost:
            return
        if isinstance(cost, int | float) and cost > 0:
-            self._total_cost += float(cost)
+            self._observed_cost += float(cost)
+            self._has_observed_cost = True

    @property
    def total_cost(self) -> float:
-        return _round_cost(self._total_cost)
+        if self.zero_cost:
+            return 0.0
+        return _round_cost(self._observed_cost if self._has_observed_cost else self._estimated_cost)

    def to_record(self) -> dict[str, Any]:
        record = serialize_usage(self._total_usage)
-        record["cost"] = _round_cost(self._total_cost)
+        record["cost"] = self.total_cost
        record["agents"] = []

        agent_tokens = {aid: _resolve_total_tokens(u) for aid, u in self._agent_usage.items()}
@@ -72,7 +79,7 @@ class LLMUsageLedger:
            usage = self._agent_usage[agent_id]
            metadata = self._agent_metadata.get(agent_id, {})
            agent_cost = (
-                self._total_cost * (agent_tokens[agent_id] / total_tokens) if total_tokens else 0.0
+                self.total_cost * (agent_tokens[agent_id] / total_tokens) if total_tokens else 0.0
            )

            agent_record = serialize_usage(usage)
@@ -92,7 +99,9 @@ class LLMUsageLedger:
        self._total_usage = Usage()
        self._agent_usage.clear()
        self._agent_metadata.clear()
-        self._total_cost = 0.0
+        self._observed_cost = 0.0
+        self._estimated_cost = 0.0
+        self._has_observed_cost = False

        if not isinstance(raw_usage, dict):
            return
@@ -103,7 +112,9 @@ class LLMUsageLedger:
            logger.exception("Failed to hydrate aggregate llm_usage from run.json")
            self._total_usage = Usage()

-        self._total_cost = _float_or_zero(raw_usage.get("cost"))
+        persisted_cost = _float_or_zero(raw_usage.get("cost"))
+        self._observed_cost = persisted_cost
+        self._estimated_cost = persisted_cost

        for raw_agent in raw_usage.get("agents") or []:
            if not isinstance(raw_agent, dict):
@@ -136,15 +147,6 @@ def _resolve_total_tokens(usage: Usage) -> int:
    return prompt + completion


-def _is_litellm_routed(model: str | None) -> bool:
-    if not model:
-        return False
-    name = model.strip().lower()
-    if "/" not in name:
-        return False
-    return not name.startswith("openai/")
-
-
 def _usage_has_activity(usage: Usage) -> bool:
    return bool(
        usage.requests
@@ -201,24 +203,23 @@ def _estimate_litellm_entry_cost(entry: Any, model: str) -> float | None:

    candidates = [model]
    if "/" in model:
-        candidates.append(model.split("/", 1)[-1])
+        candidates.append(model.rsplit("/", 1)[-1])

-    cost: Any = None
    for candidate in candidates:
+        resolved = resolve_litellm_model(candidate)
+        if not resolved:
+            continue
        try:
            cost = completion_cost(
-                completion_response={"model": candidate, "usage": usage_payload},
-                model=model,
+                completion_response={"model": resolved, "usage": usage_payload},
+                model=resolved,
            )
-            break
        except Exception:  # nosec B112  # noqa: BLE001, S112
            continue
-
-    if cost is None:
-        logger.debug("LiteLLM cost estimate unavailable for model %s", model)
-        return None
-
-    return cost if isinstance(cost, int | float) and cost >= 0 else None
+        if cost > 0:
+            return float(cost)
+    logger.debug("LiteLLM cost estimate unavailable for model %s", model)
+    return None


 def _litellm_model_name(model: str | None) -> str | None:
@@ -58,6 +58,26 @@ agent-browser screenshot
 The browser stays running across commands so these feel like a single
 session. Use `agent-browser close` (or `close --all`) when you're done.

+The default session is **shared with every other agent in the sandbox** — if
+another agent navigates it, your page and your refs are gone from under you. So
+claim your own by passing `--session <your-agent-name>` on **every** command:
+
+```bash
+agent-browser --session recon-3 open https://example.com
+agent-browser --session recon-3 snapshot -i
+agent-browser --session recon-3 close        # when done with the target
+```
+
+The examples in the rest of this skill omit `--session` to keep them readable;
+keep passing yours. Each session is a separate Chromium (~340 MB) on a shared
+box, so hold one rather than several, and close it when you're finished.
+
+A browser left idle for 3 minutes is reclaimed automatically to free memory for
+the other agents; the next command relaunches it, but the page, tabs, refs and
+cookies are gone. If you're authenticated and about to go do something else for a
+while, save the state first (see
+[Persist session across runs](#persist-session-across-runs)).
+
 ## Reading a page

 ```bash
@@ -307,6 +327,16 @@ agent-browser --session b fill @e1 "bob@test.com"
 `AGENT_BROWSER_SESSION=myapp` sets the default session for the current
 shell.

+Use a session named after yourself for your own work — that's what keeps a
+concurrent agent from navigating the page out from under you. Every session is a
+separate Chromium though, so hold one at a time rather than a collection, and
+close each one when its flow is finished:
+
+```bash
+agent-browser --session a close
+agent-browser --session b close
+```
+
 ### Mock network requests

 ```bash
@@ -368,8 +398,11 @@ agent-browser dialog dismiss          # cancel
 ## Readiness & recovery

 The first `agent-browser open` in a session launches the headless-Chrome
-daemon; later commands reuse it. Distinguish the two failure modes and react
-differently — do **not** blindly re-run the same failing command in a loop:
+daemon; later commands reuse it. A daemon left idle for 3 minutes shuts itself
+down to free memory for the other agents, so an `open` after a long gap is a
+fresh browser rather than a resumed one — expect to re-navigate, and re-`state
+load` if you were logged in. Distinguish the failure modes and react differently
+— do **not** blindly re-run the same failing command in a loop:

 - **Daemon / connection failure** (`Failed to connect`, `connection refused`,
  socket missing, `browser not running`): the daemon isn't up or has died. Run
@@ -15,7 +15,7 @@ def _ctx(ctx: RunContextWrapper) -> dict[str, Any]:


@function_tool
-async def respond_to_user(ctx: RunContextWrapper, message: str) -> str:
+async def respond_to_user(ctx: RunContextWrapper, message: str = "") -> str:
    """Answer the user and hand control back to them.

    This is the ONLY way to yield to the user. Delivering the message and
@@ -45,6 +45,10 @@ async def respond_to_user(ctx: RunContextWrapper, message: str) -> str:
            have followed the tool calls that led here. Lead with the
            answer or the decision you need, and if you are blocked, say
            exactly what you need from them.
+
+            Omit it when you have just said your piece as plain text and
+            only need to wait: that text has already reached them, and
+            repeating it makes them read the same answer twice.
    """
    inner = _ctx(ctx)
    coordinator = coordinator_from_context(inner)
@@ -110,12 +110,19 @@ def _get_agent_todos(agent_id: str) -> dict[str, dict[str, Any]]:


 def _normalize_priority(priority: str | None, default: str = "normal") -> str:
-    candidate = (priority or default or "normal").lower()
+    candidate = str(priority or default or "normal").strip().lower()
    if candidate not in VALID_PRIORITIES:
        raise ValueError(f"Invalid priority. Must be one of: {', '.join(VALID_PRIORITIES)}")
    return candidate


+def _coerce_priority(priority: str | None, default: str = "normal") -> str:
+    try:
+        return _normalize_priority(priority, default)
+    except ValueError:
+        return default
+
+
 def _sorted_todos(agent_id: str) -> list[dict[str, Any]]:
    todos_list = [
        {**todo, "todo_id": todo_id} for todo_id, todo in _get_agent_todos(agent_id).items()
@@ -285,11 +292,16 @@ async def create_todo(ctx: RunContextWrapper, todos: str) -> str:
            - ``description`` (str, optional): extra context or
              acceptance criteria.
            - ``priority`` (str, optional): one of ``"low"`` /
-              ``"normal"`` / ``"high"`` / ``"critical"``. Defaults to
-              ``"normal"``.
+              ``"normal"`` / ``"high"`` / ``"critical"``. Anything else,
+              including omitting it, falls back to ``"normal"`` rather
+              than failing.

            Example: ``[{"title": "Probe /admin", "priority": "high"},
            {"title": "Check JWT alg=none"}]``.
+
+        A title already on the list, or repeated within this call, is
+        skipped rather than duplicated; skipped titles come back under
+        ``skipped``.
    """
    agent_id = _agent_id_from(ctx)
    try:
@@ -302,13 +314,21 @@ async def create_todo(ctx: RunContextWrapper, todos: str) -> str:
            )

        agent_todos = _get_agent_todos(agent_id)
+        seen = {todo["title"].strip().lower() for todo in agent_todos.values()}
        created: list[dict[str, Any]] = []
+        skipped: list[dict[str, str]] = []
        for task in tasks:
-            task_priority = _normalize_priority(task.get("priority"))
+            title = task["title"]
+            key = title.lower()
+            if key in seen:
+                skipped.append({"title": title, "reason": "duplicate title"})
+                continue
+            seen.add(key)
+            task_priority = _coerce_priority(task.get("priority"))
            todo_id = str(uuid.uuid4())[:6]
            timestamp = datetime.now(UTC).isoformat()
            agent_todos[todo_id] = {
-                "title": task["title"],
+                "title": title,
                "description": task.get("description"),
                "priority": task_priority,
                "status": "pending",
@@ -316,7 +336,7 @@ async def create_todo(ctx: RunContextWrapper, todos: str) -> str:
                "updated_at": timestamp,
                "completed_at": None,
            }
-            created.append({"todo_id": todo_id, "title": task["title"], "priority": task_priority})
+            created.append({"todo_id": todo_id, "title": title, "priority": task_priority})
    except (ValueError, TypeError) as e:
        return json.dumps(
            {"success": False, "error": f"Failed to create todo: {e}"},
@@ -330,6 +350,7 @@ async def create_todo(ctx: RunContextWrapper, todos: str) -> str:
            "success": True,
            "created": created,
            "created_count": len(created),
+            "skipped": skipped,
            "todos": _sorted_todos(agent_id),
            "total_count": len(_get_agent_todos(agent_id)),
        },
@@ -70,7 +70,6 @@ async def test_encoded_list_is_decoded_for_an_array_parameter(schema: dict[str,
        "auth",
        "Endpoint /admin leaks user data, and session tokens never expire",
        '"auth"',
-        "",
    ],
 )
 async def test_free_form_strings_are_never_split_into_an_array(value: str) -> None:
@@ -79,6 +78,29 @@ async def test_free_form_strings_are_never_split_into_an_array(value: str) -> No
    assert parsed["tags"] == value


+@pytest.mark.asyncio
+@pytest.mark.parametrize("schema", [_ARRAY, _NULLABLE_ARRAY])
+@pytest.mark.parametrize("value", ["", "   "])
+async def test_empty_string_becomes_an_empty_array(schema: dict[str, Any], value: str) -> None:
+    parsed = await _roundtrip(schema, {"tags": value})
+
+    assert parsed["tags"] == []
+
+
+@pytest.mark.asyncio
+async def test_empty_string_becomes_an_empty_object() -> None:
+    parsed = await _roundtrip(_OBJECT, {"modifications": ""})
+
+    assert parsed["modifications"] == {}
+
+
+@pytest.mark.asyncio
+async def test_empty_string_for_a_string_parameter_is_untouched() -> None:
+    parsed = await _roundtrip(_STRING, {"todos": ""})
+
+    assert parsed["todos"] == ""
+
+
@pytest.mark.asyncio
 async def test_encoded_mapping_is_decoded_for_an_object_parameter() -> None:
    parsed = await _roundtrip(_OBJECT, {"modifications": '{"method": "POST"}'})
@@ -143,7 +143,7 @@ def test_cost_callback_estimates_cost_with_bare_model_fallback() -> None:
    }

    def fake_completion_cost(**kwargs: object) -> float:
-        if kwargs["model"] == "gpt-4o-mini":
+        if kwargs["model"] == "openai/gpt-4o-mini":
            return 0.025
        raise ValueError(kwargs["model"])

@@ -1228,3 +1228,40 @@ async def test_wait_kind_survives_a_snapshot_round_trip() -> None:
    assert restored.wait_kinds["root"] == "user"
    assert restored.idle_resume_counts["root"] == 1
    assert await execution._plain_waiting_timeout(restored, "root") is None
+
+
+@pytest.mark.asyncio
+async def test_interactive_nudge_offers_waiting_without_repeating() -> None:
+    """The nudge is the instruction an agent reads when it is stranded here.
+
+    It is where the option to wait on what was already said has to be, not only
+    in the system prompt: an agent that ended a turn on plain text reasons off
+    this text, and without the clause it restates its answer to reach a tool
+    call, so the user reads it twice.
+
+    The clause holds whatever the turn did, because the agent is the one who
+    knows whether it spoke — this fires for a turn that produced no text at all.
+    """
+    items = await execution._append_tool_required_message(
+        session=None,
+        context={"parent_id": None},
+        attempt=1,
+        limit=3,
+        interactive=True,
+    )
+
+    assert "with no message if you have already said it" in items[0]["content"]
+
+
+@pytest.mark.asyncio
+async def test_autonomous_nudge_does_not_offer_the_user() -> None:
+    """There is nobody attached to an autonomous run to wait for."""
+    items = await execution._append_tool_required_message(
+        session=None,
+        context={"parent_id": None},
+        attempt=1,
+        limit=3,
+        interactive=False,
+    )
+
+    assert "respond_to_user" not in items[0]["content"]
@@ -299,6 +299,16 @@ def test_make_model_settings_forces_required_for_anyllm_routed_openai_model() ->
    assert settings.tool_choice == "required"


+def test_make_model_settings_disables_parallel_tool_calls_by_default() -> None:
+    assert make_model_settings("none", model_name="gpt-4o").parallel_tool_calls is False
+
+
+def test_make_model_settings_omits_parallel_tool_calls_without_tools() -> None:
+    settings = make_model_settings("none", model_name="gpt-4o", has_tools=False)
+
+    assert settings.parallel_tool_calls is None
+
+
 def test_make_model_settings_sets_request_timeout() -> None:
    settings = make_model_settings(
        "none",
@@ -351,3 +361,32 @@ def test_make_model_settings_timeout_survives_reasoning_resolve() -> None:

    assert settings.extra_args is not None
    assert settings.extra_args["timeout"] == 120.0
+
+
+def test_openrouter_attribution_rides_on_the_request_headers() -> None:
+    # litellm.headers is ignored once a request carries any header of its own,
+    # so the attribution must be part of the per-request headers.
+    headers = make_model_settings(
+        None, model_name="openrouter/anthropic/claude-sonnet-4-5"
+    ).extra_headers
+    assert headers == {
+        "HTTP-Referer": "https://strix.ai",
+        "X-Title": "Strix",
+        "X-OpenRouter-Categories": "cli-agent",
+    }
+
+
+def test_openrouter_attribution_absent_for_other_providers() -> None:
+    assert make_model_settings(None, model_name="anthropic/claude-sonnet-4-5").extra_headers is None
+
+
+def test_user_headers_override_openrouter_attribution() -> None:
+    headers = make_model_settings(
+        None,
+        model_name="openrouter/anthropic/claude-sonnet-4-5",
+        extra_headers={"X-Title": "Custom", "X-Tenant": "acme"},
+    ).extra_headers
+    assert headers is not None
+    assert headers["X-Title"] == "Custom"
+    assert headers["X-Tenant"] == "acme"
+    assert headers["HTTP-Referer"] == "https://strix.ai"
@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import litellm
+from agents.usage import Usage
+
+from strix.report.pricing import resolve_litellm_model
+from strix.report.usage import LLMUsageLedger
+
+
+def test_resolves_common_bare_model_names() -> None:
+    resolve_litellm_model.cache_clear()
+    assert resolve_litellm_model("deepseek-v4-flash") == "deepseek/deepseek-v4-flash"
+    assert resolve_litellm_model("openai/deepseek-v4-flash") == "deepseek/deepseek-v4-flash"
+    assert resolve_litellm_model("grok-4.5") == "xai/grok-4.5"
+    assert resolve_litellm_model("MiniMax-M3") == "minimax/MiniMax-M3"
+
+
+def test_resolver_returns_none_for_unresolvable_model() -> None:
+    resolve_litellm_model.cache_clear()
+    assert resolve_litellm_model("provider/not-a-real-model") is None
+
+
+def test_ledger_uses_estimate_when_routed_provider_reports_no_cost() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+
+    with patch("litellm.completion_cost", return_value=0.42):
+        ledger.record(agent_id="a", usage=usage, model="openai/deepseek-v4-flash")
+
+    assert ledger.total_cost == 0.42
+
+
+def test_ledger_prefers_observed_cost_over_estimate() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+
+    with patch("litellm.completion_cost", return_value=0.42):
+        ledger.record(agent_id="a", usage=usage, model="openai/deepseek-v4-flash")
+    ledger.record_observed_cost(0.17)
+
+    assert ledger.total_cost == 0.17
+
+
+def test_hydrated_estimate_continues_accumulating_new_estimates() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+    ledger.hydrate({"cost": 0.42})
+
+    with patch("litellm.completion_cost", return_value=0.17):
+        ledger.record(agent_id="a", usage=usage, model="openai/deepseek-v4-flash")
+
+    assert ledger.total_cost == 0.59
+
+
+def test_zero_cost_disables_both_observed_and_estimated_costs() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+    ledger.zero_cost = True
+
+    with patch("litellm.completion_cost", return_value=0.42) as estimate:
+        ledger.record(agent_id="a", usage=usage, model="deepseek-v4-flash")
+        ledger.record_observed_cost(1.0)
+
+    estimate.assert_not_called()
+    assert ledger.total_cost == 0.0
+
+
+def test_resolver_uses_provider_when_bare_entry_has_one() -> None:
+    original = litellm.model_cost
+    litellm.model_cost = {
+        "example": {
+            "litellm_provider": "example-provider",
+            "input_cost_per_token": 1.0,
+            "output_cost_per_token": 2.0,
+        }
+    }
+    try:
+        resolve_litellm_model.cache_clear()
+        assert resolve_litellm_model("example") == "example-provider/example"
+    finally:
+        litellm.model_cost = original
+        resolve_litellm_model.cache_clear()
+
+
+def test_resolver_does_not_guess_between_differently_priced_providers() -> None:
+    original = litellm.model_cost
+    litellm.model_cost = {
+        "provider-a/example": {
+            "input_cost_per_token": 1.0,
+            "output_cost_per_token": 2.0,
+        },
+        "provider-b/example": {
+            "input_cost_per_token": 3.0,
+            "output_cost_per_token": 4.0,
+        },
+    }
+    try:
+        resolve_litellm_model.cache_clear()
+        assert resolve_litellm_model("example") is None
+    finally:
+        litellm.model_cost = original
+        resolve_litellm_model.cache_clear()
@@ -64,3 +64,31 @@ async def test_a_message_that_already_arrived_is_taken_instead_of_parking() -> N
    assert result["wait_outcome"] == "message_arrived"
    assert result["pending_messages"] == 1
    assert coordinator.statuses["root"] == "running"
+
+
+async def _call_without_message(context: dict[str, Any]) -> dict[str, Any]:
+    ctx = ToolContext(
+        context=context,
+        tool_name="respond_to_user",
+        tool_call_id="call-1",
+        tool_arguments="{}",
+    )
+    raw = await respond_to_user.on_invoke_tool(ctx, "{}")
+    return json.loads(raw)  # type: ignore[no-any-return]
+
+
+@pytest.mark.asyncio
+async def test_parks_without_a_message() -> None:
+    """An agent that has already said its piece as plain text can just wait.
+
+    The nudge is what leaves it here, and while a message was required the only
+    way to stop was to send the same answer a second time.
+    """
+    context = await _context(interactive=True)
+
+    result = await _call_without_message(context)
+
+    assert result["success"] is True
+    assert result["wait_outcome"] == "waiting"
+    assert result["message"] == ""
+    assert context["coordinator"].statuses["root"] == "waiting"
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+import asyncio
+import types
+from typing import Any
+
+import pytest
+from agents import ModelSettings
+
+import strix.tools.notes.tools as notes_tools
+import strix.tools.todo.tools as todo_tools
+from strix.core import runner
+from strix.core.agents import AgentCoordinator
+from strix.runtime import session_manager
+
+
+def _wire_runner(monkeypatch: pytest.MonkeyPatch, tmp_path: Any) -> None:
+    monkeypatch.setattr(runner, "run_dir_for", lambda _scan_id: tmp_path)
+    monkeypatch.setattr(runner, "runtime_state_dir", lambda _run_dir: tmp_path)
+    monkeypatch.setattr(runner, "setup_scan_logging", lambda _run_dir: lambda: None)
+    monkeypatch.setattr(runner, "set_scan_id", lambda _scan_id: None)
+
+    settings = types.SimpleNamespace(
+        llm=types.SimpleNamespace(
+            model="openai/gpt-4o",
+            reasoning_effort="high",
+            force_required_tool_choice=False,
+            timeout=300,
+            prompt_cache=True,
+            extra_headers=None,
+        ),
+        runtime=types.SimpleNamespace(max_context_images=3),
+    )
+    monkeypatch.setattr(runner, "load_settings", lambda: settings)
+    monkeypatch.setattr(runner, "configure_sdk_model_defaults", lambda _settings: None)
+    monkeypatch.setattr(
+        runner, "uses_chat_completions_tool_schema", lambda _model, _settings: False
+    )
+    monkeypatch.setattr(todo_tools, "hydrate_todos_from_disk", lambda _state_dir: None)
+    monkeypatch.setattr(notes_tools, "hydrate_notes_from_disk", lambda _state_dir: None)
+
+    async def _create_or_reuse(*_args: Any, **_kwargs: Any) -> dict[str, Any]:
+        return {"client": object(), "session": object(), "caido_client": None}
+
+    async def _cleanup(*_args: Any, **_kwargs: Any) -> None:
+        return None
+
+    monkeypatch.setattr(session_manager, "create_or_reuse", _create_or_reuse)
+    monkeypatch.setattr(session_manager, "cleanup", _cleanup)
+    monkeypatch.setattr(runner, "build_root_task", lambda _scan_config: "task")
+    monkeypatch.setattr(runner, "build_scope_context", lambda _scan_config: "")
+    monkeypatch.setattr(runner, "make_model_settings", lambda *_a, **_k: ModelSettings())
+    monkeypatch.setattr(runner, "build_strix_agent", lambda **_kwargs: object())
+    monkeypatch.setattr(runner, "make_child_factory", lambda **_kwargs: lambda **_k: object())
+    monkeypatch.setattr(runner, "open_agent_session", lambda _root_id, _db: object())
+
+
+def _root_status(coordinator: AgentCoordinator) -> str:
+    roots = [aid for aid, parent in coordinator.parent_of.items() if parent is None]
+    assert len(roots) == 1
+    return coordinator.statuses[roots[0]]
+
+
+@pytest.mark.parametrize("interrupt", [KeyboardInterrupt, asyncio.CancelledError])
+@pytest.mark.asyncio
+async def test_user_interrupt_leaves_the_root_running_for_resume(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Any, interrupt: type[BaseException]
+) -> None:
+    _wire_runner(monkeypatch, tmp_path)
+
+    async def _interrupt(*_args: Any, **_kwargs: Any) -> None:
+        raise interrupt()
+
+    monkeypatch.setattr(runner, "run_agent_loop", _interrupt)
+    coordinator = AgentCoordinator()
+
+    with pytest.raises(interrupt):
+        await runner.run_strix_scan(
+            scan_config={"targets": [], "scan_mode": "deep"},
+            scan_id="scan-test",
+            image="img",
+            coordinator=coordinator,
+        )
+
+    assert _root_status(coordinator) == "running"
+
+
+@pytest.mark.asyncio
+async def test_a_real_crash_still_marks_root_failed(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Any
+) -> None:
+    _wire_runner(monkeypatch, tmp_path)
+
+    async def _boom(*_args: Any, **_kwargs: Any) -> None:
+        raise RuntimeError("boom")
+
+    monkeypatch.setattr(runner, "run_agent_loop", _boom)
+    coordinator = AgentCoordinator()
+
+    with pytest.raises(RuntimeError, match="boom"):
+        await runner.run_strix_scan(
+            scan_config={"targets": [], "scan_mode": "deep"},
+            scan_id="scan-test",
+            image="img",
+            coordinator=coordinator,
+        )
+
+    assert _root_status(coordinator) == "failed"
@@ -0,0 +1,93 @@
+from __future__ import annotations
+
+import asyncio
+import types
+from typing import Any
+
+import pytest
+from agents import ModelSettings
+
+import strix.tools.notes.tools as notes_tools
+import strix.tools.todo.tools as todo_tools
+from strix.core import runner
+from strix.core.agents import AgentCoordinator
+from strix.runtime import session_manager
+
+
+def _wire_runner(monkeypatch: pytest.MonkeyPatch, tmp_path: Any) -> None:
+    monkeypatch.setattr(runner, "run_dir_for", lambda _scan_id: tmp_path)
+    monkeypatch.setattr(runner, "runtime_state_dir", lambda _run_dir: tmp_path)
+    monkeypatch.setattr(runner, "setup_scan_logging", lambda _run_dir: lambda: None)
+    monkeypatch.setattr(runner, "set_scan_id", lambda _scan_id: None)
+
+    settings = _settings()
+    monkeypatch.setattr(runner, "load_settings", lambda: settings)
+    monkeypatch.setattr(runner, "configure_sdk_model_defaults", lambda _s: None)
+    monkeypatch.setattr(runner, "uses_chat_completions_tool_schema", lambda _m, _s: False)
+    monkeypatch.setattr(todo_tools, "hydrate_todos_from_disk", lambda _d: None)
+    monkeypatch.setattr(notes_tools, "hydrate_notes_from_disk", lambda _d: None)
+
+    async def _create_or_reuse(*_a: Any, **_k: Any) -> dict[str, Any]:
+        return {"client": object(), "session": object(), "caido_client": None}
+
+    async def _cleanup(*_a: Any, **_k: Any) -> None:
+        return None
+
+    monkeypatch.setattr(session_manager, "create_or_reuse", _create_or_reuse)
+    monkeypatch.setattr(session_manager, "cleanup", _cleanup)
+    monkeypatch.setattr(runner, "build_root_task", lambda _c: "task")
+    monkeypatch.setattr(runner, "build_scope_context", lambda _c: "")
+    monkeypatch.setattr(runner, "make_model_settings", lambda *_a, **_k: ModelSettings())
+    monkeypatch.setattr(runner, "build_strix_agent", lambda **_k: object())
+    monkeypatch.setattr(runner, "make_child_factory", lambda **_k: lambda **_kk: object())
+    monkeypatch.setattr(runner, "open_agent_session", lambda _root_id, _db: object())
+
+
+def _settings() -> Any:
+    return types.SimpleNamespace(
+        llm=types.SimpleNamespace(
+            model="openai/gpt-4o",
+            reasoning_effort="high",
+            force_required_tool_choice=False,
+            timeout=300,
+            prompt_cache=True,
+            extra_headers=None,
+        ),
+        runtime=types.SimpleNamespace(max_context_images=3),
+    )
+
+
+@pytest.mark.asyncio
+async def test_a_live_child_is_settled_before_sessions_close(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Any
+) -> None:
+    _wire_runner(monkeypatch, tmp_path)
+    coordinator = AgentCoordinator()
+    child_started = asyncio.Event()
+    child_task: dict[str, asyncio.Task[None]] = {}
+
+    async def _root_finishes(**kwargs: Any) -> None:
+        root_id = kwargs["agent_id"]
+
+        async def _child_mid_turn() -> None:
+            child_started.set()
+            await asyncio.sleep(3600)
+
+        await coordinator.register("child", "Child", parent_id=root_id)
+        task = asyncio.create_task(_child_mid_turn())
+        child_task["t"] = task
+        await coordinator.attach_runtime("child", task=task)
+        await child_started.wait()
+
+    monkeypatch.setattr(runner, "run_agent_loop", _root_finishes)
+
+    await runner.run_strix_scan(
+        scan_config={"targets": [], "scan_mode": "deep"},
+        scan_id="scan-test",
+        image="img",
+        coordinator=coordinator,
+    )
+
+    task = child_task["t"]
+    assert task.done(), "the child task was left running past scan teardown"
+    assert task.cancelled(), "the child was not cancelled cleanly on a finish"
@@ -0,0 +1,117 @@
+from __future__ import annotations
+
+import asyncio
+from pathlib import Path
+from typing import Any, cast
+
+import pytest
+
+from strix.core.sessions import open_agent_session
+
+
+def _count_open_fds() -> int | None:
+    for path in (Path("/proc/self/fd"), Path("/dev/fd")):
+        if path.is_dir():
+            return len(list(path.iterdir()))
+    return None
+
+
+@pytest.mark.asyncio
+async def test_sessions_hold_no_descriptors_while_parked(tmp_path: Path) -> None:
+    """Descriptor use must track live operations, not the number of sessions.
+
+    The SDK keeps a connection per (session, pool thread) open for the session's
+    whole life. An agent parks rather than exits, so its session lives for the
+    scan, and fan-out multiplies those handles until the process runs out of file
+    descriptors (#1018). A session that is not mid-operation should hold none.
+    """
+    baseline = _count_open_fds()
+    if baseline is None:
+        pytest.skip("no /proc/self/fd or /dev/fd on this platform")
+
+    sessions = [open_agent_session(f"a{i}", tmp_path / f"s{i}.db") for i in range(60)]
+    try:
+        for _ in range(4):
+            await asyncio.gather(
+                *(s.add_items([{"role": "user", "content": "x"}]) for s in sessions)
+            )
+            await asyncio.gather(*(s.get_items() for s in sessions))
+        parked = _count_open_fds()
+        assert parked is not None
+        # 60 parked sessions, yet descriptors are back at the baseline.
+        assert parked - baseline <= 5, f"parked fds grew by {parked - baseline}"
+    finally:
+        for s in sessions:
+            s.close()
+
+
+@pytest.mark.asyncio
+async def test_in_flight_descriptors_track_concurrency_not_session_count(
+    tmp_path: Path,
+) -> None:
+    baseline = _count_open_fds()
+    if baseline is None:
+        pytest.skip("no /proc/self/fd or /dev/fd on this platform")
+
+    sessions = [open_agent_session(f"a{i}", tmp_path / f"s{i}.db") for i in range(200)]
+    peak = baseline
+    try:
+
+        async def sample() -> None:
+            nonlocal peak
+            for _ in range(500):
+                current = _count_open_fds()
+                if current is not None:
+                    peak = max(peak, current)
+                await asyncio.sleep(0)
+
+        async def load() -> None:
+            for _ in range(4):
+                await asyncio.gather(
+                    *(s.add_items([{"role": "user", "content": "x"}]) for s in sessions)
+                )
+
+        await asyncio.gather(load(), sample())
+        # 200 sessions, but peak is bounded by the thread pool, well under 200.
+        assert peak - baseline < 100, f"in-flight fds peaked at +{peak - baseline}"
+    finally:
+        for s in sessions:
+            s.close()
+
+
+@pytest.mark.asyncio
+async def test_history_survives_the_per_operation_connection(tmp_path: Path) -> None:
+    session = open_agent_session("agent-1", tmp_path / "agents.db")
+    try:
+        for i in range(30):
+            await session.add_items([{"role": "user", "content": f"m{i}"}])
+        items = [cast("dict[str, Any]", i) for i in await session.get_items()]
+        assert [i["content"] for i in items] == [f"m{i}" for i in range(30)]
+    finally:
+        session.close()
+
+
+@pytest.mark.asyncio
+async def test_concurrent_sessions_sharing_one_file_stay_consistent(tmp_path: Path) -> None:
+    db = tmp_path / "shared.db"
+    sessions = [open_agent_session(f"a{i}", db) for i in range(10)]
+    try:
+        await asyncio.gather(
+            *(s.add_items([{"role": "user", "content": s.session_id}]) for s in sessions)
+        )
+        # Each session sees only its own row despite sharing the file.
+        for s in sessions:
+            items = [cast("dict[str, Any]", i) for i in await s.get_items()]
+            assert [i["content"] for i in items] == [s.session_id]
+    finally:
+        for s in sessions:
+            s.close()
+
+
+@pytest.mark.asyncio
+async def test_a_closed_session_refuses_operations(tmp_path: Path) -> None:
+    session = open_agent_session("agent-1", tmp_path / "agents.db")
+    await session.add_items([{"role": "user", "content": "x"}])
+    session.close()
+    with pytest.raises(RuntimeError, match="closed"):
+        await session.add_items([{"role": "user", "content": "y"}])
@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import pytest
+from agents.tool_context import ToolContext
+
+from strix.tools.todo import tools
+from strix.tools.todo.tools import _coerce_priority, create_todo
+
+
+@pytest.fixture(autouse=True)
+def _isolate_store() -> Any:
+    tools._todos_storage.clear()
+    yield
+    tools._todos_storage.clear()
+
+
+async def _create(todos: list[Any], agent_id: str = "root") -> dict[str, Any]:
+    ctx = ToolContext(
+        context={"agent_id": agent_id},
+        tool_name="create_todo",
+        tool_call_id="call-1",
+        tool_arguments="{}",
+    )
+    raw = await create_todo.on_invoke_tool(ctx, json.dumps({"todos": json.dumps(todos)}))
+    return json.loads(raw)  # type: ignore[no-any-return]
+
+
+def test_unknown_priority_falls_back_to_normal() -> None:
+    assert _coerce_priority("medium") == "normal"
+    assert _coerce_priority("urgent") == "normal"
+    assert _coerce_priority("high") == "high"
+
+
+@pytest.mark.asyncio
+async def test_one_bad_priority_no_longer_discards_the_batch() -> None:
+    result = await _create(
+        [
+            {"title": "Recon", "priority": "medium"},
+            {"title": "Probe /admin", "priority": "sky-high"},
+            {"title": "Report"},
+        ]
+    )
+
+    assert result["success"] is True
+    assert result["created_count"] == 3
+    by_title = {c["title"]: c["priority"] for c in result["created"]}
+    assert by_title["Recon"] == "normal"
+    assert by_title["Probe /admin"] == "normal"
+    assert by_title["Report"] == "normal"
+
+
+@pytest.mark.asyncio
+async def test_duplicate_titles_within_a_batch_are_skipped() -> None:
+    result = await _create(
+        [
+            {"title": "Subdomain enumeration"},
+            {"title": "Content discovery"},
+            {"title": "Subdomain enumeration"},
+            {"title": "content discovery"},
+        ]
+    )
+
+    assert result["created_count"] == 2
+    assert {c["title"] for c in result["created"]} == {
+        "Subdomain enumeration",
+        "Content discovery",
+    }
+    assert len(result["skipped"]) == 2
+    assert all(s["reason"] == "duplicate title" for s in result["skipped"])
+
+
+@pytest.mark.asyncio
+async def test_a_title_already_on_the_list_is_not_created_again() -> None:
+    await _create([{"title": "Crawl with katana"}])
+    result = await _create([{"title": "crawl with katana"}, {"title": "JS analysis"}])
+
+    assert [c["title"] for c in result["created"]] == ["JS analysis"]
+    assert [s["title"] for s in result["skipped"]] == ["crawl with katana"]
+    assert result["total_count"] == 2
+
+
+def test_coerce_never_raises() -> None:
+    assert _coerce_priority("nonsense") == "normal"
+    assert _coerce_priority(None) == "normal"
+    assert _coerce_priority("high") == "high"
+    for value in (2, ["high"], {"p": 1}, True):
+        assert _coerce_priority(value) == "normal"  # type: ignore[arg-type]
+
+
+@pytest.mark.asyncio
+async def test_non_string_priority_does_not_fail_the_batch() -> None:
+    result = await _create(
+        [
+            {"title": "Recon", "priority": 2},
+            {"title": "Probe", "priority": ["high"]},
+            {"title": "Report"},
+        ]
+    )
+
+    assert result["success"] is True
+    assert result["created_count"] == 3
+    assert {c["priority"] for c in result["created"]} == {"normal"}
@@ -234,12 +234,11 @@ async def test_confirming_the_mount_starts_the_scan_without_a_target() -> None:


@pytest.mark.asyncio
-async def test_declining_the_mount_returns_to_the_start_screen() -> None:
-    started = False
+async def test_declining_the_mount_runs_without_one() -> None:
+    started: list[bool] = []

-    async def start(_verify: bool = True) -> None:
-        nonlocal started
-        started = True
+    async def start(verify: bool = True) -> None:
+        started.append(verify)

    os.environ["STRIX_LLM"] = "anthropic/claude-sonnet-4"
    os.environ["ANTHROPIC_API_KEY"] = "test-key"
@@ -250,14 +249,34 @@ async def test_declining_the_mount_returns_to_the_start_screen() -> None:
    result = await controller.handle("setup.confirm_mount", {"approved": False})

    assert result == {"approved": False}
-    # Nothing was prepared, so the session goes back to the start screen and can
-    # be launched again.
-    assert started is False
+    # Declining skips the directory; it does not abandon the scan.
+    assert started == [False]
    assert controller.workspace_mount is None
    assert controller.pending_workspace_mount is None
-    assert controller.setup_mode is True
-    assert controller.scan_started is False
-    assert controller.scan_state == "setup"
+    assert controller.setup_mode is False
+    assert controller.scan_started is True
+    assert controller.scan_state == "running"
+
+
+@pytest.mark.asyncio
+async def test_approving_the_mount_runs_with_it() -> None:
+    started: list[bool] = []
+
+    async def start(verify: bool = True) -> None:
+        started.append(verify)
+
+    os.environ["STRIX_LLM"] = "anthropic/claude-sonnet-4"
+    os.environ["ANTHROPIC_API_KEY"] = "test-key"
+    loader._cached = None
+    controller = TuiController(args(), on_start=start)
+    await controller.handle("setup.start", {"verify": False, "mount_working_dir": True})
+
+    result = await controller.handle("setup.confirm_mount", {"approved": True})
+
+    assert result == {"approved": True}
+    assert started == [False]
+    assert controller.workspace_mount == str(Path.cwd())
+    assert controller.scan_state == "running"


@pytest.mark.asyncio
@@ -7,19 +7,26 @@ shows what the user actually typed; resuming has to match that.

 from __future__ import annotations

+import ast
 import json
 import sqlite3
+from pathlib import Path
 from typing import TYPE_CHECKING, Any

 import pytest

+from strix.core import execution
 from strix.core.paths import runtime_state_dir
 from strix.interface.tui.backend.live_view import TuiLiveView as GoTuiLiveView
-from strix.interface.tui.live_view import TuiLiveView, _is_internal_agent_turn
+from strix.interface.tui.live_view import (
+    _INTERNAL_TURN_PREFIXES,
+    TuiLiveView,
+    _is_internal_agent_turn,
+)


 if TYPE_CHECKING:
-    from pathlib import Path
+    from types import ModuleType


 def _write_run(run_dir: Path, items: list[dict[str, Any]], agent_id: str = "root") -> None:
@@ -176,11 +183,60 @@ def test_internal_turn_classifier_matches_every_injected_form() -> None:
        "[CRITICAL] Turn budget: 480/500 used (96%).",
        "== Inherited context from parent (background only) ==",
        "Your previous message ended a turn without a tool call.",
-        "Your previous response ended the autonomous Strix run without a lifecycle tool call.",
+        "Your previous response ended the autonomous run without a lifecycle tool call.",
    ):
        assert _is_internal_agent_turn(content), content


+def _injected_strings(module: ModuleType) -> list[str]:
+    """Every string a module can inject, and nothing it merely mentions.
+
+    Parsing rather than searching the text keeps comments out of it, so a stale
+    copy of a message left in a comment cannot pass for the message itself. It
+    also joins adjacent literals for free, which the line wrapping needs, and
+    docstrings are dropped because they describe the code rather than run in it.
+    """
+    tree = ast.parse(Path(module.__file__ or "").read_text(encoding="utf-8"))
+    docstrings = set()
+    for node in ast.walk(tree):
+        if not isinstance(node, ast.Module | ast.ClassDef | ast.FunctionDef | ast.AsyncFunctionDef):
+            continue
+        first = node.body[0] if node.body else None
+        if isinstance(first, ast.Expr) and isinstance(first.value, ast.Constant):
+            docstrings.add(id(first.value))
+
+    literals: list[str] = []
+    for node in ast.walk(tree):
+        if isinstance(node, ast.Constant):
+            if isinstance(node.value, str) and id(node) not in docstrings:
+                literals.append(node.value)
+        elif isinstance(node, ast.JoinedStr):
+            literals.append(
+                "".join(
+                    part.value
+                    for part in node.values
+                    if isinstance(part, ast.Constant) and isinstance(part.value, str)
+                )
+            )
+    return literals
+
+
+def test_internal_turn_prefixes_still_match_what_is_injected() -> None:
+    """The classifier copies sentences out of another module, so they can drift.
+
+    Both nudges are written inline in strix.core.execution, so there is nothing to
+    import and compare against. Read them back out of what that module can inject.
+    """
+    injected = _injected_strings(execution)
+    nudges = [prefix for prefix in _INTERNAL_TURN_PREFIXES if prefix.startswith("Your previous")]
+    assert nudges, "the no-tool-call nudges are no longer in the classifier"
+    for nudge in nudges:
+        assert any(nudge in literal for literal in injected), (
+            f"the classifier expects {nudge!r}, which strix.core.execution no longer "
+            f"injects. A resumed scan would show that nudge as the user's own message."
+        )
+
+
 def test_internal_turn_classifier_keeps_bracketed_user_text() -> None:
    """A leading bracket is not enough: typed text often starts with one."""
    for content in (
@@ -2378,7 +2378,7 @@ wheels = [

 [[package]]
 name = "strix-agent"
-version = "1.5.1"
+version = "1.5.3"
 source = { editable = "." }
 dependencies = [
    { name = "caido-sdk-client" },
Author	SHA1	Message	Date
Alex Schapiro	056ccd5fed	Normalize mechanical punctuation fixes	2026-08-14 19:43:51 +00:00
Alex Schapiro	79630f9ba0	Apply mechanical punctuation and wording-token fixes	2026-08-14 19:42:22 +00:00
Alex SchapiroandAhmed Allam	8ca0c4a9b8	Fix LiteLLM cost model resolution	2026-08-12 17:26:00 +03:00
Ahmed AllamandAhmed Allam	7cc9fa9faa	chore: release v1.5.3	2026-08-10 21:28:52 +03:00
devin-ai-integration[bot]andGitHub	174c16fa26	fix(llm): send OpenRouter app attribution on the request itself (#1045 )	2026-08-10 11:24:02 -07:00
Ahmed AllamandAhmed Allam	94a2586aaa	fix(container): write the browser profile as root	2026-08-10 10:08:17 +03:00
Ahmed AllamandAhmed Allam	372e27fa17	chore(container): drop explanatory comment	2026-08-10 09:54:49 +03:00
Ahmed AllamandAhmed Allam	ad727edd66	fix(container): keep the browser env alive where image ENV is dropped	2026-08-10 09:54:49 +03:00
devin-ai-integration[bot]GitHub Ahmed Allam	7b3c8f9b74	fix(container): reclaim abandoned browser sessions (#1034 ) Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>	2026-08-09 16:57:51 -07:00
Ahmed AllamandAhmed Allam	ae07af6159	chore: drop explanatory comment	2026-08-09 15:44:16 +03:00
Ahmed AllamandAhmed Allam	649a2e2140	fix(llm): omit parallel_tool_calls on tool-less requests	2026-08-09 15:44:16 +03:00
Ahmed AllamandAhmed Allam	597aae6715	chore: release v1.5.2	2026-08-09 04:29:34 +03:00
Ahmed AllamandGitHub	06b158d1fa	fix(runner): settle child agents before closing sessions at wind-down (#1025 )	2026-08-08 18:17:58 -07:00
Ahmed AllamandGitHub	c29eb73c7f	fix(tools): coerce an empty-string list/dict argument to an empty container (#1024 )	2026-08-08 16:58:30 -07:00
Ahmed AllamandGitHub	72833b8e43	fix(runner): resume after a user interrupt instead of failing (#1023 )	2026-08-08 16:44:12 -07:00
Ahmed AllamandGitHub	1117ba6d4a	fix(sessions): open a sqlite connection per operation, not per thread (#1022 )	2026-08-08 16:20:01 -07:00
Ahmed AllamandGitHub	53e4658d88	fix(todo): stop a todo plan failing on priority or duplicates (#1021 )	2026-08-08 15:18:48 -07:00
Ahmed Allam GitHub Claude Opus 4.8	58df71d3db	fix(agents): let an agent wait on what it already said (#1020 ) * let an agent wait on what it already said An agent that answers in plain text is nudged to call a tool, and the only tool that hands control back takes a required message. So it says the same thing twice: once as text the user has already read, once as the argument it had to supply to stop. Seen on a run whose whole instruction was "hi" - a greeting, then the same greeting again through respond_to_user. message is optional now. The nudge arms the tool with the text that was delivered and says not to repeat it, so an agent that has said its piece can park on it with an empty call. Anything it does want to add it passes normally. Parking still cannot leave the user on silence: an empty call is refused unless something was actually said, and the arming is single use - execution clears it as soon as a turn ends any other way. The interactive prompt now also says to answer and stop in one respond_to_user call, which is what avoids the nudge in the first place. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * drop the worked example from the interactive prompt "the user greeted you, asked something you can answer outright, or you need a decision" was the run I had been reading, written into a rule that holds whatever the reason. The rule is that replying and stopping is one call; listing occasions only invites the model to check whether this is one of them. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * drop the arming flag; an empty message just waits Passing the delivered text from execution into the tool, and refusing an empty call without it, was machinery guarding against an agent parking having said nothing. That leaves the user looking at "waiting for your reply" with a cursor in front of them - they type. It does not need a mechanism. What is left is the default on message, and the nudge saying the text already landed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * only offer waiting on words that were written The nudge told every agent its text had already been delivered, but it fires whenever a turn leaves the agent running, and a turn can end with no tool call and no text at all - _final_output_preview has carried <none> and <empty> branches all along. An agent that said nothing was being invited to wait on an answer the user never received, leaving them at a bare prompt. It now reads the turn: waiting on what was said is offered only when something was, and otherwise the agent is told plainly that the user has read nothing and to send its message. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * leave the continuation nudge alone Rewording it meant asserting from the outside whether the agent had spoken, and the nudge fires whenever a turn leaves the agent running - text or no text. The agent knows which it did without being told, so the guidance belongs in its prompt, where the condition is its own to read. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * say it in the nudge, where the agent is reading An agent stranded by the nudge reasons off the nudge. Told only to call respond_to_user, it supplies a message, and since it has just answered in plain text that message is the same answer again. The system prompt saying otherwise sits thousands of tokens earlier and loses. The clause goes on the line the agent acts on: call respond_to_user, with no message if it has already said it. That reads true whatever the turn did, including one that produced no text, because the agent is the one who knows which — nothing here has to work it out from the outside. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>	2026-08-09 00:57:16 +03:00
Ahmed Allam GitHub Claude Opus 4.8	0b9e029a5d	test(tui): correct the nudge the internal-turn test asserts (#1016 ) * correct the nudge the internal-turn test asserts The test expected "ended the autonomous Strix run", which strix.core.execution does not inject; it says "ended the autonomous run". The classifier was right and the test was not, so the suite failed on main while the behaviour it guards was fine. The sentence is written inline in another module and copied by hand into the classifier and again into the test, which is how it drifted. A second test now reads it back out of that module's source, joining the adjacent string literals its line wrapping leaves behind, and fails if either nudge is no longer injected verbatim. Reworded one and it reports which nudge went missing and what a resumed scan would do about it. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * read the nudges out of what the module can inject, not out of its text Searching the source accepted the sentence anywhere in the file, so a stale copy left behind in a comment would have kept the guard passing after the message it guards had changed - the drift it exists to catch. Parsing the module instead limits it to strings the code can actually inject. Comments never reach the tree, docstrings are dropped as description rather than behaviour, and adjacent literals are joined during parsing, which the line wrapping needed and the regex was only approximating. Checked by rewording the message and leaving the old wording in a comment: the guard fails, where searching the text passed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>	2026-08-08 22:34:45 +03:00
Ahmed Allam GitHub Claude Opus 4.8	b260a4ee38	fix(tui): make the mount prompt clickable, and skip the mount instead of abandoning the scan (#1015 ) * make the working-directory prompt answer the mouse Its Confirm and Cancel were drawn as buttons and did nothing when clicked: the modal mouse handler had a case for every dialog except this one, so a click fell through and the scan sat waiting on an answer the user believed they had given. Only the keyboard could answer it. The prompt is docked in a corner rather than centered, so it also needs its own bounds; the centered ones every other dialog uses would have put the buttons in the wrong place. Those bounds now come from the same placement cornerOverlay draws with. Two returns that hand back the model alongside a call that mutates it are now sequenced explicitly. They work, but only because the compiler happens to evaluate the call first, and one of them is what puts the prompt back in the composer when the mount is declined. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * skip the mount instead of abandoning the scan Declining the working-directory prompt threw the whole launch away and dropped back to the start screen, which is a lot to lose for answering one question about one directory. The two answers are now about the directory alone: mount it, or run without it. The prompt is the whole of the input either way. The buttons say which is which - Mount and Skip rather than Confirm and Cancel - and the prompt says what skipping costs. A run with neither target nor directory is a real run, so two things follow it. It can be resumed: its instruction is what drives it, and that is in the run record. And it tells the agent plainly that it has neither, because an agent given no scope goes looking for the one it assumes it was meant to have. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>	2026-08-08 22:34:02 +03:00
alex sandGitHub	f8a8801d56	docs(skills): rename skills to descriptive names and broaden descriptions for discoverability (#1013 )	2026-08-07 14:24:19 -04:00