deadgate

Find CI checks that cannot fail.

Flaky-test tools find checks that fail randomly. This finds the opposite: checks that are structurally incapable of going red, so your pipeline is green for reasons unrelated to your code.

GitHub documents the sharpest case itself:

A job that is skipped will report its status as Success. It will not prevent a pull request from merging, even if it is a required check.

pip install deadgate
deadgate .

Exit code is 1 when there are findings, 0 when clean, and 2 when a workflow file could not be parsed. A file it could not read is never counted as a file with no problems.

What it detects

Every finding carries a reproduction. A finding without one is an opinion, and this tool does not emit opinions.

The calibration corpus is the specification

corpus/ holds workflows that are known broken and workflows that are known good, and the good ones are deliberate near misses of the broken ones. A detector has to fire on the defect and stay quiet on its near miss. A detector that fires on everything is as useless as one that fires on nothing, and it gets the whole tool switched off.

python -m pytest tests/

Fixtures g6 and g7 exist because the detectors were wrong against real repositories while the corpus was green:

• g6 a job guarding itself with a job-level if: that reads needs.*.outputs IS checking its upstream. The first version only read step-level conditions and flagged correct jobs.
• g7 if: ${{ !cancelled() }} is not skip-prone. It runs in normal operation and on failure. Treating every if: as a possible skip produced false alarms.

Both were found by running against a real 25k-star repository, not by the suite. That is the argument this tool makes about everyone else's checks, so it is held to it too.

The branch-protection tier

The structural tier reads workflow files and reports the SHAPE of a dead gate. It cannot tell you whether anything was relying on the job. That needs the branch's required status checks:

deadgate . --repo owner/name            # read-only GitHub API calls via `gh`

Two endpoints answer, and they do not have the same reach:

On a repository you do not administer, only the first answers. It says nothing about classic protection, so an empty result does not mean the branch is unprotected. That asymmetry decides what the tier is allowed to claim:

• a match proves the check is required, so a MEDIUM finding escalates to HIGH
• no match proves nothing unless the required set is complete, which needs admin on both endpoints. Without that, the verdict is AMBIGUOUS and the severity does not move

Only MEDIUM moves. MEDIUM is the tier that means "the workflow file does not say", so it is the only one this evidence can settle. A structural HIGH keeps its severity even when a check is not required, because protection can be added later and may be configured where this API does not reach. A structural LOW keeps its severity because a release pipeline that skips on purpose does not become a merge gate by appearing in a list.

Verdicts

Why it reports contexts it could not attribute

A required status check is identified by its check-run name, which is not the job key in the YAML. If name derivation breaks, no required context matches any job, every MEDIUM resolves to "not required", and the tool quietly downgrades real defects. So the run prints how many required contexts it attributed to a job in the repository, and warns when none of them matched anything. A broken matcher then appears as a number rather than as silence. Some contexts land there legitimately, from third-party apps or workflows outside the repository, so it is a signal to read and not an assertion.

Two transport facts are enforced rather than trusted, because both were observed:

• 403 is returned for rate limiting and for insufficient permissions. These are separated, because one is retryable and the other means this tier cannot help on that repository.
• 404 from the classic endpoint means "not protected" for a branch you administer and "you cannot see this" otherwise, with the same status code and only the prose differing. So it is gated on permissions.admin, never on the message text.

Measurements

Figures, and the unit each one is in, are in MEASUREMENT.md. Reproduce them with python bench/build_cache.py then python bench/ab.py.

The short version: a corpus total is weighted by workflow size, so one monorepo's release pipeline can carry most of it while the repository you actually run this on sees a handful. bench/ab.py prints the per-repo median, p90 and max alongside every total, so the unit cannot be dropped by accident, and the median is the number a user feels.

The DEADGATE_NAIVE arm was removed in 0.1.2. It had stopped isolating what it named: the new suppressions live in the shared path, so both arms reported identical figures and the pre-narrowing column had quietly become a copy of the current one.

Scope, stated plainly

This reads workflow files. It does not read branch-protection settings, so it reports the shape of a defect, not whether a given check is actually required on your default branch. Semantic gate testing, planting the condition a gate claims to catch and asserting it reacts, is a separate and harder problem and is not in this release.

Licence

MIT

0.1.2 closes four false-positive classes, two of them shipped

Upgrade from 0.1.0 or 0.1.1. Four times this tool reported correct CI as broken, each time because it asked "does this job consult its upstreams?" and looked in too few places.

1 and 2 shipped in 0.1.0 and were fixed in 0.1.1; 3 and 4 were found afterwards by pointing the fixed version at two more repositories. The whole job is now searched, so placement stops mattering.

# all four of these are a gate doing its job, and all four were reported as one that cannot
if: always()
run: if [[ "${{ contains(needs.*.result, 'failure') }}" == "true" ]]; then exit 1; fi
---
if: ${{ always() && github.ref != 'refs/heads/main' }}
env: { NEEDS_JSON: "${{ toJSON(needs) }}" }
---
uses: ./.github/workflows/report.yml
with: { job_status: "${{ needs.check-sdist.result }}" }

Measured on one 275-repository, 4543-file corpus, both arms reading identical bytes: 4562 findings and 1104 HIGH before, 2917 and 658 after — 40% of HIGH removed. On the repositories that write their gates carefully the share is far higher: openinference went from 13 HIGH to 1, ruff from 21 to 3, and in both cases the survivors are unrelated D3 findings.

That gap is the lesson worth keeping. The tool was least accurate on the repositories with the BEST CI, which is the worst place for a linter to cry wolf, and no corpus average would have surfaced it. The suite passed unchanged through every one of the four, 149 of it through the first two fixes and 161 through the second two, so not one of them was covered by anything. There are 165 tests now, and scripts/check_readme_test_count.py fails if that number and the suite ever drift apart again.