docs(skills): use current OWASP editions (Top 10:2025, API Top 10 2023)

fix(skills): avoid unquoted colon in api-security-testing description
feat(skills): add target-specific security testing skills (web app, API, OWASP Top 10, code review)
2026-08-16 09:26:39 +02:00 · 2026-08-14 15:12:04 +00:00 · 2026-08-14 15:07:26 +00:00 · 2026-08-14 15:06:58 +00:00 · 2026-08-12 17:26:00 +03:00 · 2026-08-10 21:28:52 +03:00
50 changed files with 1454 additions and 136 deletions
@@ -10,10 +10,17 @@ 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)
+
+Target-specific workflows built on the same engine:
+
+- `web-app-penetration-testing` — black-box pentest of a live web app or staging site
+- `api-security-testing` — REST/GraphQL APIs and the OWASP API Security Top 10 (BOLA/IDOR, authz)
+- `owasp-top-10-testing` — systematic OWASP Top 10 assessment with honest per-category coverage
+- `find-security-vulnerabilities-in-code` — white-box review of a repo or working tree

 **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 eight 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), **ci-security-scanning-with-strix** (PR scanning in CI), plus target-specific workflows: **web-app-penetration-testing**, **api-security-testing**, **owasp-top-10-testing**, and **find-security-vulnerabilities-in-code**. 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; \
@@ -15,15 +15,19 @@ 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) |
+| `web-app-penetration-testing` | Black-box pentest of a live web app or staging site — scope, credentials, and multi-account access-control testing |
+| `api-security-testing` | Test a REST/GraphQL API against the OWASP API Security Top 10 — schema-driven enumeration, BOLA/IDOR, authz |
+| `owasp-top-10-testing` | Systematic OWASP Top 10 assessment with honest per-category coverage |
+| `find-security-vulnerabilities-in-code` | White-box security review of a repo or working tree, with exploits to confirm findings |

-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
@@ -31,7 +35,7 @@ npx skills use usestrix/strix@strix-pentest | claude
 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.
+- **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 `managed-pentesting-with-strix` skill has the full flow.

 ## Agent-Friendly Interfaces

@@ -1,6 +1,6 @@
 [project]
 name = "strix-agent"
-version = "1.5.0"
+version = "1.5.3"
 description = "Open-source AI Hackers for your apps"
 readme = "README.md"
 license = "Apache-2.0"
@@ -0,0 +1,56 @@
+---
+name: api-security-testing
+description: Security-test a REST, GraphQL, or gRPC API with Strix — autonomous agents that enumerate endpoints from an OpenAPI/GraphQL schema (or by crawling), then actually exploit the API-specific vulnerability classes in the OWASP API Security Top 10 (2023) — broken object-level authorization (BOLA/IDOR), broken object property level authorization (excessive data exposure and mass assignment), broken function-level authorization, unrestricted resource consumption, SSRF, injection, and auth/token flaws. Every finding comes with a working proof-of-concept request. Use when the user asks to pentest, security-test, audit, or find vulnerabilities in an API, endpoint, or backend service.
+license: Apache-2.0
+metadata:
+  author: usestrix
+  homepage: https://docs.strix.ai
+---
+
+# Security-test an API
+
+APIs fail differently from web UIs: there's no rendered surface to crawl, the interesting bugs are authorization-shaped rather than injection-shaped, and the same endpoint behaves differently per token. This workflow targets those specifics with Strix's autonomous agents, using the current [OWASP API Security Top 10 (2023)](https://owasp.org/API-Security/editions/2023/en/0x11-t10/) as the coverage checklist. For the web-app equivalent, the current edition is the OWASP Top 10:2025 — see **owasp-top-10-testing**.
+
+Install, LLM setup, full CLI flags, and the managed-cloud path are in the **penetration-testing-with-strix** skill. Read it if `strix --version` fails or the target isn't an API.
+
+## 1. Gather what the agents need
+
+APIs are near-impossible to test blind, so collect first:
+
+| Input | Why it matters |
+|---|---|
+| **Schema** — OpenAPI/Swagger URL or file, GraphQL endpoint (introspection), or `.proto` | Turns guesswork into full endpoint enumeration. Biggest single win in coverage. |
+| **Two sets of credentials/tokens**, ideally in different tenants | BOLA/IDOR — API1:2023, still the #1 API risk — can only be *proven* by accessing tenant A's objects with tenant B's token. |
+| **A low-privilege and a high-privilege token** | Required to prove broken function-level authorization (API5:2023 — a `user` calling admin-only routes). |
+| **Example object IDs** | Lets agents test ID tampering immediately instead of hunting for valid identifiers. |
+| **Out-of-scope routes** | Payments, mass notification, destructive admin endpoints. |
+| **Rate limits / WAF** in front of the API | Avoids agents burning budget on throttled requests; mention them so testing adapts. |
+
+Ask the user for anything missing — don't fabricate tokens or scan an API they don't own.
+
+## 2. Run the scan
+
+```bash
+strix -n -t https://api.staging.example.com --max-budget 20 \
+  --instruction "OpenAPI spec: https://api.staging.example.com/openapi.json.
+Tenant A token: <tokenA> (org 1111, user id 11, order id 501).
+Tenant B token: <tokenB> (org 2222, user id 22).
+Admin token: <tokenAdmin>.
+Focus: BOLA across orgs (API1), function-level authz on /admin/* (API5), object property level authz on PATCH /users/{id} — both mass assignment and over-exposed fields in list responses (API3), unrestricted resource consumption (API4).
+Out of scope: POST /billing/*, POST /notifications/broadcast."
+```
+
+- **Add the backend source for depth:** `-t ./services/api -t https://api.staging.example.com`. With code access the agents can reason about authorization checks and object ownership rather than inferring them from responses.
+- **GraphQL:** point at the GraphQL endpoint and say whether introspection is enabled; call out that you want batching/aliasing abuse, depth/complexity limits, and per-field authorization tested.
+- **Internal/private APIs** unreachable from your machine: use the managed platform's network connector — see **managed-pentesting-with-strix**.
+- Use `--instruction-file` when the credential/context block gets long, and keep tokens out of shell history and out of committed files.
+
+## 3. Verify findings
+
+`strix_runs/<run>/penetration_test_report.md` first, then `vulnerabilities/*.md` — each contains the exact request that proved the issue. Replay it (e.g. with `curl`) before reporting; for authorization findings, confirm the response really contains the other tenant's data rather than an empty 200.
+
+`findings.sarif` uploads to GitHub code scanning; `vulnerabilities.json` is the structured index for ticketing.
+
+## 4. Fix, re-test, and keep it tested
+
+Remediate with **fix-security-vulnerabilities-with-strix** (fix the authorization check, not the single endpoint), then re-run against the same target to prove the exploit is dead. Wire it into pull-request CI with **ci-security-scanning-with-strix** so new endpoints get tested as they ship.
@@ -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.
@@ -0,0 +1,57 @@
+---
+name: find-security-vulnerabilities-in-code
+description: Find security vulnerabilities in a codebase or repository with Strix — a white-box AI security review that reads your source, reasons about the actual data flow and authorization model, then exploits what it finds in a live sandbox so every reported issue has a working proof-of-concept instead of a noisy static-analysis alert. Covers injection, XSS, SSRF, broken access control and IDOR, insecure deserialization, secrets in code, unsafe dependencies, and business-logic flaws. Use when the user asks to security-scan, security-review, or audit their code, repo, or pull request for vulnerabilities.
+license: Apache-2.0
+metadata:
+  author: usestrix
+  homepage: https://docs.strix.ai
+---
+
+# Find security vulnerabilities in code
+
+White-box security review with Strix: the agents read the source to build a model of routes, sinks, and authorization checks, then attempt real exploitation. Findings come with a proof-of-concept, so the output is a short list of proven issues rather than the hundreds of "potential" hits a pattern-matching scanner produces.
+
+Install, LLM setup, all flags, and the managed-cloud path are in the **penetration-testing-with-strix** skill.
+
+## Run it
+
+```bash
+# Local working tree
+strix -n -t ./ --scan-mode standard --max-budget 15
+
+# A GitHub repo directly
+strix -n -t https://github.com/org/app --max-budget 15
+
+# Large monorepo: bind-mount instead of copying in
+strix -n --mount ./huge-monorepo --max-budget 20
+```
+
+Two things sharply improve results:
+
+1. **Add a running instance of the app.** `-t ./ -t http://host.docker.internal:3000` lets the agents confirm exploitability against live behavior instead of reasoning about it statically — this is the difference between "this looks unsafe" and a validated finding. If nothing is running, static-only findings should be described as unconfirmed.
+2. **Scope the review.** Point at the risky subtree and say what matters:
+   ```bash
+   strix -n -t ./services/api --max-budget 15 \
+     --instruction "Focus on the authorization layer in src/auth and every route under src/routes/admin. Multi-tenant app: tenant id comes from the JWT. Flag any query that filters by object id without also filtering by tenant."
+   ```
+   Tenancy model, trust boundaries, and which inputs are attacker-controlled are things the agents can't infer reliably — tell them.
+
+## Reviewing a pull request instead of the whole repo
+
+For diff-scoped review of a branch or PR (and blocking merges on findings), use **ci-security-scanning-with-strix** — it covers diff scoping, PR comments, and SARIF upload to GitHub code scanning. The managed platform can also review PRs directly via API (**managed-pentesting-with-strix**).
+
+## Read the results
+
+In `strix_runs/<run>/`: `penetration_test_report.md` (start here), `vulnerabilities/*.md` (one per finding, with PoC and remediation), `vulnerabilities.json` / `.csv`, `findings.sarif` (upload to code scanning), `run.json`.
+
+Before reporting to the user, open each finding and check the PoC actually demonstrates impact. Report file and line alongside the exploit so the fix is obvious.
+
+Exit `0` means nothing exploitable was proven in what was analyzed — not that the codebase is clean. Check `run.json` status and cost against `--max-budget`, and note which paths went unreviewed if the run was capped.
+
+## Complementary tooling
+
+This is exploit-validated review, not an exhaustive inventory. Keep a dependency scanner (SCA) and secret scanning in place for complete coverage of known-CVE dependencies and committed credentials; use this for the logic, authorization, and injection bugs those tools structurally can't find.
+
+## Fix and verify
+
+Hand results to **fix-security-vulnerabilities-with-strix**: patch the root cause (the shared authorization helper, not the one route), then re-run Strix to prove the exploit no longer works.
@@ -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

@@ -0,0 +1,64 @@
+---
+name: owasp-top-10-testing
+description: Test an application against the OWASP Top 10 with Strix — autonomous AI agents that attempt real exploits for each category of the current OWASP Top 10:2025 (broken access control including SSRF, security misconfiguration, software supply chain failures, cryptographic failures, injection, insecure design, authentication failures, integrity failures, logging and alerting failures, mishandling of exceptional conditions) and report only what they could actually prove, mapped back to the category with a proof-of-concept. Also covers the OWASP API Security Top 10 (2023). Use when the user asks for an OWASP Top 10 assessment, OWASP compliance testing, or a security review mapped to OWASP categories.
+license: Apache-2.0
+metadata:
+  author: usestrix
+  homepage: https://docs.strix.ai
+---
+
+# Test against the OWASP Top 10
+
+The OWASP Top 10 is a taxonomy of risk categories, not a test suite — "OWASP Top 10 testing" means exercising each category against the real application and reporting what's actually exploitable. Strix's agents do the exploitation; this skill covers running it category-by-category and reporting coverage honestly.
+
+**Use the current edition: [OWASP Top 10:2025](https://owasp.org/Top10/)** (8th installment, superseding 2021). Ask the user before targeting an older edition — some compliance checklists still reference 2021, and a report labelled with the wrong edition is misleading. Key differences from 2021: **SSRF is folded into A01**, **A03 Software Supply Chain Failures** expands the old "Vulnerable and Outdated Components", and **A10 Mishandling of Exceptional Conditions** is new; A02 Security Misconfiguration moved 5→2.
+
+Install, LLM setup, and the managed-cloud alternative: **penetration-testing-with-strix**.
+
+## What is and isn't testable by an agent
+
+Be straight with the user about this — claiming a clean sweep of all ten is misleading.
+
+| Category (2025) | Coverage |
+|---|---|
+| A01 Broken Access Control (incl. SSRF) | **Strong** — cross-user/tenant access, privilege escalation, IDOR, and SSRF (including blind, via out-of-band callbacks) are all exploit-validated. Needs two accounts plus a privileged one to prove the authorization half. |
+| A02 Security Misconfiguration | **Strong** — debug endpoints, verbose errors, permissive CORS, missing hardening, default credentials, exposed admin surfaces. |
+| A03 Software Supply Chain Failures | **Partial** — version fingerprinting, and vulnerable/outdated dependency review when source is supplied. Build-system and distribution-infrastructure compromise (the broader half of this category) is out of scope for a runtime scan — pair with SCA plus build-provenance controls. |
+| A04 Cryptographic Failures | **Partial** — transport config, unencrypted data in transit, secrets and tokens leaked in responses. At-rest crypto and key management need source or infra review. |
+| A05 Injection | **Strong** — SQL/NoSQL/command/template injection and XSS, exploit-validated. |
+| A06 Insecure Design | **Partial** — business-logic abuse (price/quantity tampering, workflow skipping, race conditions) is found where reachable; design intent still needs human review and threat modelling. |
+| A07 Authentication Failures | **Strong** — auth bypass, weak session/token handling, password-reset and MFA flaws. |
+| A08 Software or Data Integrity Failures | **Partial** — insecure deserialization and unsigned-update paths where reachable; CI/CD trust boundaries are not runtime-testable. |
+| A09 Security Logging & Alerting Failures | **Not testable from outside** — requires reviewing the logging and alerting pipeline. State this rather than reporting it as passed. |
+| A10 Mishandling of Exceptional Conditions | **Partial** — agents actively probe error handling and fail-open behavior (malformed input, forced errors, race and timeout conditions) and report what leaks or bypasses a control; exhaustive coverage of internal error paths needs source review. |
+
+For APIs, run the same exercise against the **OWASP API Security Top 10 (2023)** — API1 BOLA, API3 Broken Object Property Level Authorization (2019's excessive data exposure + mass assignment merged), API5 broken function-level authorization — using the **api-security-testing** skill.
+
+## Run it
+
+Maximum category coverage comes from giving the agents both the source and a running instance, plus credentials at two privilege levels:
+
+```bash
+strix -n \
+  -t https://github.com/org/app \
+  -t https://staging.example.com \
+  --scan-mode deep --max-budget 30 \
+  --instruction "OWASP Top 10:2025 assessment. Cover every category systematically and map each finding to its 2025 category id.
+Accounts: userA@example.com/<pw> (org 1), userB@example.com/<pw> (org 2), admin@example.com/<pw>.
+Prioritise A01 (cross-org access, privilege escalation, SSRF), A02, A05, A07, A10.
+Out of scope: /billing/*, outbound email."
+```
+
+- `--scan-mode deep` matters here: systematically walking ten categories is not a quick scan.
+- Without a second account, A01 results are structurally incomplete — say so in the report rather than leaving it implied.
+- Need an auditor-facing PDF? Run it through the managed platform and pull the technical report (**managed-pentesting-with-strix**).
+
+## Report honestly
+
+From `strix_runs/<run>/`, group `vulnerabilities/*.md` by category and state, per category: what was attempted, what was proven, and what couldn't be assessed (A09 always; A03/A04/A06/A08/A10 partially). Label the report with the edition used. Verify each PoC yourself before it goes in front of the user.
+
+A `0` exit code means nothing exploitable was proven **in what was analyzed** — check `run.json` status and cost against `--max-budget`; a budget-capped run is not a completed assessment.
+
+## Then fix and re-test
+
+Remediate with **fix-security-vulnerabilities-with-strix** and re-run to prove each exploit is closed. For ongoing coverage as the app changes, gate pull requests using **ci-security-scanning-with-strix**.
@@ -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

@@ -0,0 +1,54 @@
+---
+name: web-app-penetration-testing
+description: Pentest a web app or website end to end — black-box testing of a live URL, staging environment, or local dev server that finds and exploits real vulnerabilities (auth bypass, broken access control, IDOR, injection, XSS, SSRF, business logic) and proves each one with a working proof-of-concept instead of a signature match. Runs with Strix, either the self-hosted open-source CLI or the managed app.strix.ai cloud. Use when the user asks to pentest, hack, security-test, or audit their web app, website, web application, or staging site.
+license: Apache-2.0
+metadata:
+  author: usestrix
+  homepage: https://docs.strix.ai
+---
+
+# Pentest a web application
+
+Black-box (and optionally source-assisted) penetration testing of a running web app with Strix's autonomous agents. Every reported finding is validated with a working exploit, so there are no signature-based false positives to triage.
+
+Install, LLM setup, all CLI flags, and the managed-cloud alternative are covered in the **penetration-testing-with-strix** skill — read it if the target isn't a running web app, or if `strix --version` fails. This skill is the web-app-specific workflow.
+
+## 1. Confirm authorization and scope
+
+Before running anything, establish:
+
+- **The target is the user's** (or they're explicitly authorized to test it). Never pentest a third-party site on a hunch.
+- **Which environment.** Prefer staging over production; agents send real exploit payloads and will create/modify data.
+- **Out-of-scope paths** — payment flows, mass-email endpoints, admin destructive actions, third-party SSO providers.
+- **Credentials.** Most real vulnerabilities live behind login. Without a test account, the agents only ever see the marketing surface.
+
+Ask for anything missing rather than guessing.
+
+## 2. Run the scan
+
+```bash
+strix -n -t https://staging.example.com --max-budget 20 \
+  --instruction "Test account: qa@example.com / <password>. In scope: /app/*, /api/*. Do not touch /billing or send email. Focus on access control between the two seeded orgs."
+```
+
+Notes that matter for web apps specifically:
+
+- **Give it credentials via `--instruction`** (or `--instruction-file` for anything long), including how to log in if the flow is unusual (magic link, SSO, MFA-exempt test user).
+- **Two accounts beat one.** Multi-tenant IDOR and broken-access-control bugs — consistently the highest-impact class in web apps — can only be proven when the agent can attempt cross-account access.
+- **Add the repo for white-box depth** when you have the source: `-t https://github.com/org/app -t https://staging.example.com` (or a local path). Source access materially improves coverage of business-logic and authorization flaws.
+- **Localhost works.** Point at `http://host.docker.internal:3000` (Docker Desktop) so the sandbox can reach a dev server on the host.
+- `--scan-mode quick` for a fast dev-loop pass, `standard` (~30 min) for a normal review, `deep` for pre-release assurance. Always set `--max-budget`.
+
+For a hosted run with no Docker/LLM key, or when the user wants a shareable dashboard and an auditor-ready PDF, use the cloud path in **managed-pentesting-with-strix** instead — same engine, same findings.
+
+## 3. Review results
+
+Read `strix_runs/<run>/penetration_test_report.md` first, then per-finding files in `vulnerabilities/`. Each contains the PoC — re-run it yourself to confirm before reporting to the user.
+
+Exit codes: `0` no validated vulns in what was analyzed, `2` vulnerabilities found, `1` fatal error. A `0` is not proof of full coverage — if the budget or turn cap was hit the scan wraps up early, so check `run.json` status and cost against `--max-budget` before calling the app clean.
+
+## 4. Fix and verify
+
+Hand findings to the **fix-security-vulnerabilities-with-strix** skill: patch the root cause, then re-run Strix against the same target to prove the exploit no longer works. Re-testing is the only reliable confirmation a fix landed.
+
+To keep the app tested on every change rather than once, wire Strix into CI with **ci-security-scanning-with-strix**.
@@ -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 = "1.5.0"
+version = "1.5.3"
 source = { editable = "." }
 dependencies = [
    { name = "caido-sdk-client" },
Author	SHA1	Message	Date
Alex Schapiro	a548502494	docs(skills): use current OWASP editions (Top 10:2025, API Top 10 2023)	2026-08-14 15:12:04 +00:00
Alex Schapiro	a109aa2159	fix(skills): avoid unquoted colon in api-security-testing description	2026-08-14 15:07:26 +00:00
Alex Schapiro	6729dae902	feat(skills): add target-specific security testing skills (web app, API, OWASP Top 10, code review)	2026-08-14 15:06:58 +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
Ahmed AllamandAhmed Allam	22750077da	chore: release v1.5.1	2026-08-07 20:28:06 +03:00