strix/strix/skills/tooling/python.md

name, description

name	description
python	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

# 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

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

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:

# 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.