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0xallamandClaude Opus 4.7 9d7f754b59 feat(tools): python_action — stateless Python execution with proxy helpers
Restores the legacy persistent-IPython tool's *ergonomics* (proxy
helpers pre-bound, structured stdout/stderr/error returns) without the
in-container daemon: each call ships ``strix.tools.proxy._calls`` source
into ``/tmp`` alongside a per-call driver, runs ``python3 -u`` against
it, and parses a sentinel-delimited JSON payload back from stdout. The
driver fetches its own guest token from Caido at ``localhost:48080``
and binds ``list_requests`` / ``view_request`` / ``send_request`` /
``repeat_request`` / ``scope_rules`` to that client; user code runs
inside an ``async def`` wrapper so top-level ``await`` works.

The proxy SDK call sequences live in one file —
``strix/tools/proxy/_calls.py`` — and are reused by both the host-side
``@function_tool`` wrappers (which add JSON serialization for the LLM)
and the in-container kernel (which exposes the bare async functions).
No code duplication; the helper logic itself is host-shipped, so
tweaking the proxy helpers does not require an image rebuild.

Image: a single ``pip install caido-sdk-client`` line so the driver's
``import caido_sdk_client`` resolves. Skill ``tooling/python`` is
always-loaded alongside ``tooling/agent_browser``.

Trade-off accepted: state does not persist across calls (no kernel).
For multi-step workflows the agent combines into one ``code`` block or
writes a script to ``/workspace/scratch/`` and runs via
``exec_command``. If a workflow surfaces that genuinely needs
persistence, the same tool surface migrates to a kernel-backed
executor without changing the LLM contract.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-25 22:58:53 -07:00

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---
name: python
description: python_action — execute Python in the sandbox with Caido proxy helpers (list_requests, view_request, send_request, repeat_request, scope_rules) pre-bound as awaitables. Stateless per call; persistence via files.
---
# python_action — when and how
Use ``python_action`` for any Python-side work: payload encoding/decoding,
parsing/transforming captured HTTP traffic, crypto operations, custom
exploit scripts, log/JSON analysis. Use ``exec_command`` for shell tools
(nmap, sqlmap, ffuf, agent-browser, package managers, daemons).
**Do not** wrap Python in bash heredocs, ``python3 -c`` one-liners, or
``echo | python3`` chains via ``exec_command`` — ``python_action`` exists
so structured output replaces fragile stdout parsing.
## What's pre-bound (no imports needed)
All proxy helpers are **async** — call them with ``await``:
- ``list_requests(httpql_filter=, first=50, after=, sort_by=, sort_order=,
scope_id=)`` → cursor-paginated SDK ``Connection``. Iterate
``connection.edges``; each edge has ``.cursor`` and ``.node.request`` /
``.node.response``.
- ``view_request(request_id, part="request")`` → SDK request object.
``.request.raw`` and ``.response.raw`` are bytes.
- ``send_request(method, url, headers=None, body="")`` → dict with
``status``, ``error``, ``elapsed_ms``, ``response_raw`` (bytes or None),
``session_id``.
- ``repeat_request(request_id, modifications={...})`` → same shape.
``modifications`` keys: ``url`` / ``params`` / ``headers`` / ``body`` /
``cookies``.
- ``scope_rules(action, allowlist=, denylist=, scope_id=, scope_name=)``
— same actions as the host-side tool (``list``/``get``/``create``/
``update``/``delete``).
Top-level ``await`` works — the body is wrapped in an async function for
you. ``print()`` to emit visible output; the last expression is **not**
auto-shown.
## Stateless model + how to keep state
Each ``python_action`` call is a **fresh process**: variables, imports,
and definitions do not survive. To carry state across steps:
- **Combine into one call** when the workflow is short — write the full
multi-step routine as one ``code`` block.
- **Persist to disk** for longer-lived state. ``/workspace/scratch/`` is
pentester-writable and survives across calls within a scan.
- **Build a script** with ``apply_patch`` to ``/workspace/scratch/<name>.py``
and run it via ``exec_command python3 ...`` when you need a file the
agent can iterate on.
## Examples
### Hunt SQLi candidates by inspecting captured traffic
```python
# All POSTs that look interesting
posts = await list_requests(
httpql_filter='req.method.eq:"POST" AND req.path.cont:"/api/"',
first=50,
)
candidates = []
for edge in posts.edges:
body = await view_request(edge.node.request.id, part="request")
raw = body.request.raw.decode("utf-8", errors="replace")
if "id=" in raw or "user=" in raw:
candidates.append(edge.node.request.id)
print(f"{len(candidates)} candidates")
print(candidates[:10])
```
### Replay with a SQLi probe and a tampered cookie
```python
result = await repeat_request(
"req_abc123",
modifications={
"params": {"id": "1' OR '1'='1"},
"cookies": {"session": "ATTACKER_TOKEN"},
},
)
print(result["status"], result["elapsed_ms"], "ms")
if result["response_raw"]:
print(result["response_raw"].decode("utf-8", errors="replace")[:500])
```
### Decode/encode payloads
```python
import base64, urllib.parse, hashlib
token = "eyJhbGciOiJIUzI1NiJ9.eyJ1c2VyIjoiYWxpY2UifQ.sig"
header_b64, payload_b64, _ = token.split(".")
print(base64.urlsafe_b64decode(payload_b64 + "=="))
```
### Iterate an exploit by writing to scratch
When iterating, prefer writing the script to disk so you can edit-and-rerun
without re-sending the whole code each call:
```text
# 1. Use apply_patch to create /workspace/scratch/exploit.py
# 2. exec_command: python3 /workspace/scratch/exploit.py
# 3. Edit + re-run; repeat until working
```
For one-shot crypto/encoding work or a single proxy-data analysis,
``python_action`` is the cleaner choice.