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Starr-delStarr-del/ShipSafe/skills/deploy-log-doctor/SKILL.md

deploy-log-doctor

Skill0 starsChanged 5 months agoMITView on GitHub

Diagnose failed build and deploy logs from Vercel, Netlify, Cloudflare Pages, or GitHub Actions. Use this whenever the user pastes a build log, says a deploy failed, mentions 'build error', 'deployment failed', 'works locally but not in production', or shows any CI output containing an error — even if they don't ask for a diagnosis explicitly. Runs fully offline — no API keys, no network, no credentials.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run the classifier on the log first, then interpret:

    line 10
  2. Distinctive:

    If it returns a diagnosis, walk the user through the fix steps, adapting them to the user's actual framework and platform.

    line 21
  3. Distinctive:

    If it returns nothing (exit 2), the log is probably truncated above the real error — ask for the section around the first ERROR line and …

    line 22
  4. Distinctive:

    After fixing, suggest the rollback-readiness skill to catch the next failure class before it happens.

    line 23
  5. Distinctive:

    All paths below are relative to this skill's directory (deploy-log-doctor/).

    line 27

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

deploy-log-doctor

Part of shipsafe — offline deploy-safety skills. Every script is stdlib-only Python 3.8+; nothing leaves the machine.

Run the classifier on the log first, then interpret:

python3 scripts/diagnose_log.py <logfile>        # or pipe: cat log | python3 scripts/diagnose_log.py -
python3 scripts/diagnose_log.py <logfile> --json # structured output

The script matches 11 curated failure signatures (missing env vars, case-sensitive imports, Node version mismatches, OOM, lockfile drift, peer-dep conflicts, Prisma generate, and more) and emits the root cause plus numbered fix steps.

Workflow:

  1. Save the user's log to a file (or pipe it) and run the script.
  2. If it returns a diagnosis, walk the user through the fix steps, adapting them to the user's actual framework and platform.
  3. If it returns nothing (exit 2), the log is probably truncated above the real error — ask for the section around the first ERROR line and rerun.
  4. After fixing, suggest the rollback-readiness skill to catch the next failure class before it happens.

Exit codes: 0 diagnosed, 2 no signature matched.

All paths below are relative to this skill's directory (deploy-log-doctor/).

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