agentleFS
nassaj2011nassaj2011/claude-skills-for-chatgpt/skills/qa-release-gatekeeper/SKILL.md

qa-release-gatekeeper

Skill0 starsChanged 25 days agoMITView on GitHub

Judge software release readiness from executed tests, change scope, migrations, security checks, incident risks and verified rollback.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    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:

    Stop a release for unresolved high-impact data corruption, payment, security or unrecoverable migration risks.

    line 10
  2. Distinctive:

    Never deploy, modify production, or mark a release complete without explicit authorization.

    line 13

≈ “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

Release gatekeeping

  1. Identify exact commit/PR set, release candidate, environment and impacted workflows. Inspect current evidence rather than relying on memory.
  2. Define required gates proportionate to risks: unit, integration, E2E, authorization, migrations, concurrency, configuration, health and rollback.
  3. For each gate mark PASS, FAIL, BLOCKED, NOT RUN or NOT APPLICABLE; include command, environment and timestamp when known.
  4. Stop a release for unresolved high-impact data corruption, payment, security or unrecoverable migration risks.
  5. Issue GO only with adequate real evidence. CONDITIONAL GO requires explicit accepted risks, accountable approver and mitigation. Otherwise NO-GO.
  6. Plan smoke tests, post-deploy monitoring, incident contacts, halt thresholds and rollback criteria.
  7. Never deploy, modify production, or mark a release complete without explicit authorization.

Output: GO/CONDITIONAL GO/NO-GO, exact SHA, evidence matrix, blockers, accepted risks, monitoring and rollback. Missing evidence is NOT RUN, never PASS.

Executable evidence gate

The source checkout includes examples/observability-gate/gate.py with explicit availability and p95 thresholds and an intentionally synthetic fixture. Use actual authorized telemetry and inspect all CI/security/migration checks for a genuine release decision; never promote a synthetic sample or an isolated GREEN result into production authorization.

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