ci-maintenance-workflow

name

ci-maintenance-workflow

description

CI and GitHub Actions maintenance workflows — fix a failing test from a CI URL, fix a failing smoke test, add @pytest.mark.slow markers to slow tests, or review a PR against agent-checkable standards. Use when user asks to fix a failing test, fix a smoke test, mark slow tests, or review a PR. Trigger when the user asks you to run the "Write a PR For A Failing Test", "Fix A Failing Smoke Test", "Mark Slow Tests", or "Review PR According to Agent-Checkable Standards" workflow.

CI Maintenance Workflows

This skill covers four CI/GitHub Actions-related workflows:

• Fix A Failing Test: Diagnose and fix a failing test from a CI run
• Fix A Failing Smoke Test: Diagnose and fix a failing smoke test (eval that fails with --limit 0)
• Mark Slow Tests: Add @pytest.mark.slow markers to tests exceeding thresholds
• Review PR: Automatically check a PR against agent-checkable standards

---

Write a PR For A Failing Test

This workflow is for fixing failing pytest tests (not smoke tests — see "Fix A Failing Smoke Test" below for those).

1. The user should provide you with a link that looks like https://github.com/ArcadiaImpact/inspect-evals-actions/actions/runs/<number>/job/<number2>, or a test to fix. If they haven't, please ask them for the URL of the failing Github action, or a test that should be fixed.
2. If a link is provided, you will be unable to view this link directly, but you can use 'gh run view' to see it.
3. Checkout main and do a git pull. Make sure you're starting from a clean slate and not pulling in any changes from whatever you or the user was previously doing.
4. Create a branch agent/<number> for your changes, in inspect_evals. Note this is not the same as inspect-evals-actions where the above URL was from.
5. Identify the failing test(s) and any other problems. Verify the test currently fails in this environment by running it with uv run pytest tests/<eval> --runslow.
6. Attempt these fixes one by one and verify they work locally, with uv run pytest tests/<eval> --runslow.
7. On every change, assess if a task version bump and changelog entry are required. Read TASK_VERSIONING.md and PACKAGE_VERSIONING.md, then explicitly decide for this change. Default to bumping unless you can articulate why results remain comparable. Crash-fixes count: turning a sample that previously errored into one that now scores (correct or incorrect) changes which samples contribute to the aggregate, so the fix affects comparability even though no "scoring logic" was rewritten. If you bump, update the eval's README changelog and run uv run scriv create for a package-level fragment.
8. Perform linting as seen in .github/workflows/build.yml, and fix any problems that arise.
9. Commit your changes to the branch.
10. Before pushing, go over your changes and make sure all the changes that you have are high quality and that they are relevant to your finished solution. Remove any code that was used only for diagnosing the problem.
11. Push your changes.
12. Use the Github API to create a pull request with the change. Include a link to the failing test, which test failed, and how you fixed it. Include information on what you did to verify the fix worked. Always include the full checklist from .github/PULL_REQUEST_TEMPLATE.md verbatim in the PR body — the Check PR template CI job greps for the literal checklist substrings and fails the PR otherwise. This applies to bot/automated PRs too.

---

Fix A Failing Smoke Test

This workflow is for fixing evals that fail their smoke test (running with --limit 0). Smoke test failures are different from regular test failures — they often involve upstream changes to datasets, APIs, or dependencies, and the root cause is not always obvious from the error message alone. Take extra care to understand the problem before proposing a fix.

Hard rules (read first, no exceptions)

• Reproduce before fixing. Run the smoke command locally and try to observe the same failure CI saw before changing any code.

• Verify after fixing. Rerun the same smoke command after your change and confirm it now runs cleanly.

• Do not skip, xfail, or disable the eval as a "fix". All of the following are forbidden:

  • Adding @pytest.mark.skip/skipif/xfail, pytest.skip(...), pytest.xfail(...), unittest.skip(...) to the eval's tests
  • Removing the eval from the smoke-test target list, the eval registry, tools/run_evals.py allowlists, or any CI matrix
  • Commenting out the failing solver/scorer/dataset-loading path
  • Wrapping the failing call in a bare try/except that swallows the error and returns a dummy result
  • Short-circuiting the eval to return success without doing the work (e.g. early-return on --limit 0)
  • Loosening a scorer or assertion to accept whatever the broken code currently produces
  • Replacing a real dataset or upstream call with a stub purely to make the smoke pass

  If a candidate fix matches any of the above, it is not a fix. Treat the situation as "blocked" instead and use the blocked-PR path described under "Reporting" below.

Reporting (when you can't reproduce, can't verify, or can't fix)

The smoke-fix workflow always produces a PR — even when you fail at one of the gates. Do not silently abandon the run, do not push a skip-based fallback, and do not just leave a comment somewhere. The PR is the report.

If you hit any of these situations, still raise a draft PR on your branch (use git commit --allow-empty if there are no code changes to push) with the standard structured body from "PR Description" below, populated as follows:

• Could not reproduce the CI failure locally: put Unable to reproduce locally — <command run>, <local outcome> in How I validated, and put your hypotheses about the env/dependency/dataset discrepancy in My uncertainty. Leave My bugfix as None — blocked on reproduction.
• Reproduced but could not verify a fix (rerun still fails, or eval can't be run locally): put Unable to verify because <reason> in How I validated, and explain in My uncertainty.
• Root cause is genuinely unfixable here (e.g. dataset removed upstream with no replacement): describe what you tried and why you stopped, in My bugfix and My uncertainty.

Mark the PR as draft (override the usual non-draft convention for this case) so it's clear a human needs to take over. Keep the automated-smoke-test-fix label so the existing triage flow picks it up.

Investigation

1. The user should provide the name of the failing eval, or a link to the failing smoke test run. If they haven't, ask them.

2. If a link is provided, you will be unable to view this link directly, but you can use 'gh run view' to see it.

3. Checkout main and do a git pull. Make sure you're starting from a clean slate.

4. Create a branch for your changes.

5. Read the eval source code. Find and read the source for the failing eval under src/inspect_evals/.

6. Check the eval's commit history. Run git log --oneline -20 -- src/inspect_evals/<eval_name>/ to understand recent changes. This can reveal whether the eval was recently modified in ways that are relevant to the failure — for example, a parameter that was recently added or removed.

7. Reproduce the failure (gate). Run the eval with:

   uv run python tools/run_evals.py --pred <eval_name> --fail-fast
   

   Read the full error output carefully. Note the exact error message and traceback — it will populate the The error message field in the PR body.

   If the eval runs cleanly locally (i.e. you cannot reproduce the CI failure), do NOT write a speculative fix. Follow the "Reporting" section above to raise a blocked draft PR; a PR that claims to fix a failure you never observed is not acceptable.

8. Formulate a hypothesis. Before writing any code, articulate:

   • What the error message means
   • How it connects to the eval's source code
   • What specifically is broken and why
   • Whether recent commits (from step 6) are relevant

   If the error is ambiguous or could have multiple causes, investigate further before jumping to a fix. Check upstream dependencies, dataset availability, and API changes.

Fix

1. Implement the fix. Make the minimal change needed to resolve the issue. Avoid speculative changes — only fix what is directly connected to the error. Re-read the "Hard rules" above before you start typing — if your candidate fix matches anything in the forbidden list, it is not a fix.

2. Verify the fix (gate). Run the eval again with the same command you used to reproduce:

   uv run python tools/run_evals.py --pred <eval_name> --fail-fast
   

   The eval must complete without errors. The command and outcome go in the How I validated field of the PR body. If it still fails, you have not fixed the bug — keep iterating, or follow the "Reporting" section above to raise a blocked draft PR. If you cannot run the eval at all (e.g. disk space, credentials, network), use the blocked-PR path with Unable to validate because <reason> in How I validated — do not claim verification you did not perform.

3. On every change, assess if a task version bump and changelog entry are required. Read TASK_VERSIONING.md and PACKAGE_VERSIONING.md, then explicitly decide for this change. Default to bumping unless you can articulate why results remain comparable. Crash-fixes count: turning a sample that previously errored into one that now scores (correct or incorrect) changes which samples contribute to the aggregate, so the fix affects comparability even though no "scoring logic" was rewritten. If you bump, update the eval's README changelog and run uv run scriv create for a package-level fragment.

4. Perform linting with make check and fix any problems that arise.

5. Commit your changes to the branch.

6. Before pushing, go over your changes and make sure all the changes are high quality and relevant to your finished solution. Remove any code that was used only for diagnosing the problem. Re-confirm you have not introduced any of the forbidden patterns from the "Hard rules" section above — git diff main...HEAD | grep -nE "skip|xfail" and inspect every match (some matches will be unrelated; the goal is to catch any skip/xfail you added).

7. Push your changes.

PR Description

1. Create a pull request using the Github API. The PR body must include the following sections:

   **The bug:** <What is broken — one sentence>
   **The error message:** <The key error line(s) from the traceback>
   **My hypothesis:** <Why this error occurs — connect the error to the code>
   **My bugfix:** <What you changed and why it addresses the root cause>
   **How I validated:** <Exact command(s) you ran and the outcome, or "Unable to validate because <reason>">
   **My uncertainty:** <What you are not sure about, or "None — fix is straightforward and verified">
   

   If you were unable to verify the fix, or your confidence is low, say so clearly in the uncertainty section. It is better to raise a PR that says "I think this is the issue but I could not verify" than to ship an unverified fix with false confidence.

---

Mark Slow Tests

This workflow adds @pytest.mark.slow(seconds) markers to tests that exceed the 10-second threshold in CI but are not already marked. This is necessary because our CI pipeline checks for unmarked slow tests and fails the build if any test takes >= 15 seconds without a marker.

Workflow Steps

1. Get the GitHub Actions URL: The user should provide a URL like https://github.com/ArcadiaImpact/inspect-evals-actions/actions/runs/<run_id> or https://github.com/ArcadiaImpact/inspect-evals-actions/actions/runs/<run_id>/job/<job_id>. If they haven't, please ask them for the URL of the failing Github action.

2. Extract slow test information: Use the fetch_slow_tests.py script to get all slow tests and their recommended markers:

   uv run python agent_artefacts/scripts/fetch_slow_tests.py <url_or_run_id> --threshold 10
   

   This script:

   • Fetches logs from all jobs in the GitHub Actions run
   • Parses test timing information from the "Check for unmarked slow tests" step
   • Aggregates durations across all Python versions and platforms
   • Reports the maximum duration observed for each test
   • Provides recommended @pytest.mark.slow(N) marker values (ceiling of max duration)

   For JSON output (useful for programmatic processing):

   uv run python agent_artefacts/scripts/fetch_slow_tests.py <url_or_run_id> --threshold 10 --json
   

   Tests marked [FAIL] in the output exceeded 60 seconds and caused the CI to fail. All tests above the threshold (default 10s) should be marked.

3. Check existing markers: For each identified test, check if it already has a @pytest.mark.slow marker:

   grep -E "def <test_name>|@pytest\.mark\.slow" tests/<path>
   

   If the test already has a slow marker with a value >= the observed duration, skip it (per user instruction: "If the number is there but too high, leave it as is").

4. Add slow markers: For tests without markers, add @pytest.mark.slow(N) where N is the observed maximum duration rounded up to the nearest integer. Place the marker immediately before other decorators like @pytest.mark.huggingface or @pytest.mark.asyncio.

   Example:

   # Before
   @pytest.mark.huggingface
   def test_example():
   
   # After
   @pytest.mark.slow(45)
   @pytest.mark.huggingface
   def test_example():
   

5. Run linting: After adding markers, run linting to ensure proper formatting:

   uv run ruff check --fix <modified_files>
   uv run ruff format <modified_files>
   

6. Commit: Commit the changes with a descriptive message listing all tests that were marked:

   Add slow markers to tests exceeding 10s threshold
   
   Tests marked:
   - <test_path>::<test_name>: slow(N)
   - ...
   
   Co-Authored-By: Claude <assistant_name> <[e-mail]>
   

Notes

• The slow marker value should be slightly higher than the observed maximum to account for CI variance. Rounding up to the nearest integer is usually sufficient.
• Parametrized tests (e.g., test_foo[param1]) share a single marker on the base test function. You should use the highest value found across all params for the slow marker.

TODO: What if one param is an order of magnitude higher than the rest? Should we recommend splitting it into its own test? pytest.mark.slow contents aren't that crucial, but still.

---

Review PR According to Agent-Checkable Standards

This workflow is designed to run in CI (GitHub Actions) to automatically review pull requests against the agent-checkable standards in EVALUATION_CHECKLIST.md. It produces a SUMMARY.md file that is posted as a PR comment.

Important Notes

• This workflow is optimized for automated CI runs, not interactive use.
• The SUMMARY.md file location is /tmp/SUMMARY.md (appropriate for GitHub runners).
• Unlike other workflows, this one does NOT create folders in agent_artefacts since it runs in ephemeral CI environments.

Workflow Steps

1. Identify if an evaluation was modified: Look at the changed files in the PR to determine which evaluation(s) are affected. Use git diff commands to see what has changed. If an evaluation (found in src/inspect_evals) was modified, complete all steps in the workflow. If no evaluation was modified, skip any steps that say (Evaluation) in them.

2. Read the repo context: Read agent_artefacts/repo_context/REPO_CONTEXT.md if present. Use these lessons in your analysis.

3. (Evaluation) Read the standards: Read EVALUATION_CHECKLIST.md, focusing on the Agent Runnable Checks section. These are the standards you will check against.

4. (Evaluation) Check each standard: For each evaluation modified in the PR, go through every item in the Agent Runnable Checks section:

   • Read the relevant files in the evaluation
   • Compare against the standard
   • For any violations found, add an entry to /tmp/NOTES.md with:
     • Standard: Which standard was violated
     • Issue: Description of the issue
     • Location: file_path:line_number or file_path if not line-specific
     • Recommendation: What should be changed

   Only worry about violations that exist within the code that was actually changed. Pre-existing violations that were not touched or worsened by this PR can be safely ignored.

5. Examine test coverage: Use the ensure-test-coverage skill and appropriate commands to identify any meaningful gaps in test coverage. You can skip this step if a previous step showed no tests were found, since this issue will already be pointed out.

6. Apply code quality analysis: Examine all files changed for quality according to BEST_PRACTICES.md. Perform similar analysis and note-taking as in Step 5. If you notice poor quality in a way that is not mentioned in BEST_PRACTICES.md, add this to your notes, and under Standard, write "No explicit standard - agent's opinion". Err on the side of including issues here, unless REPO_CONTEXT.md or another document tells you not to - we will be updating these documents over time, and it is easier for us to notice something too nitpicky than to notice the absence of something important.

7. Check infrastructure standards: Also apply the Infrastructure Changes (Agent) check from EVALUATION_CHECKLIST.md — this applies to all PRs, not just eval submissions. It's a quick check: if high-impact files changed, did the relevant docs follow?

8. Write the summary: After checking all standards, create /tmp/SUMMARY.md by consolidating the issues from NOTES.md:

   ## Summary
   
   Brief overview of what was reviewed and overall assessment.
   
   ## Issues Found
   
   ### [Standard Category Name]
   
   **Issue**: Description of the issue
   **Location**: `file_path:line_number` or `file_path` if not line-specific
   **Recommendation**: What should be changed
   
   (Repeat for each issue from NOTES.md)
   
   ## Notes
   
   Any additional observations or suggestions that aren't violations but could improve the code.
   

   If you found zero issues, re-read the diff before concluding — it is worth raising tradeoff even if you agree with the conclusion. If you still find nothing, a brief confirmation is fine. Do NOT include a list of passed checks — anything not mentioned is assumed fine.

9. Important: Always write to /tmp/SUMMARY.md even if there are no issues. The CI workflow depends on this file existing.

End your comment with 'This is an automatic review performed by Claude Code. Any issues raised here should be fixed or justified, but a human review is still required in order for the PR to be merged.'

What This Workflow Does NOT Check

• Human-required checks (these need manual review)
• Evaluation report quality (requires running the evaluation)
• Whether the evaluation actually works (requires execution)

This workflow only checks code-level standards that can be verified by reading the code.