agentleFS
ask-marcelask-marcel/ask-marcel-office-cli/.claude/skills/qa-audit/SKILL.md

qa-audit

Skill9 starsChanged 20 days agoMITView on GitHub

Run the full-surface QA health check of the three ask-marcel-office bins (auth, read and write) by executing docs/QA-PLAYBOOK.md phase by phase — offline gates, bundle smoke tests, manifest-driven parameter matrix, conversion contract matrix, live read-only Graph drift probes, agent-ergonomics journeys, and gap/roadmap analysis. Use when the user asks to audit the CLI, run QA, check CLI health, find bugs/regressions across the command surface, or re-verify after a Microsoft Graph change. Produces a severity-ranked report and a proposed fix plan; never fixes code during the run.

3 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read-only tenant access: Only commands whose manifest effect is read (playbook §0 rule 1.

    line 12
  2. Very specific:

    Compare against the previous run: Read the most recent .claude/qa-reports/*.md first.

    line 14
  3. Very specific:

    Keep the playbook honest: If a check is wrong/stale (a contract changed intentionally, a fragile probe is obsolete), update …

    line 16

≈ “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

QA audit runner

You are auditing the three bins ask-marcel-office-auth, ask-marcel-office-read and ask-marcel-office-write. The authoritative procedure is docs/QA-PLAYBOOK.md — read it in full before doing anything, then execute phases A→H in order. This skill only adds the operating rules:

  1. Track phases as tasks. One task per playbook phase (A–H); mark in_progress/completed as you go. Full surface every run — no sampling, no tiers.
  2. Report, then plan. You find and document; you do NOT fix during the run (not even trivial P1s — note them, finish the sweep). After delivering the report, propose a severity-ordered fix plan and WAIT for approval.
  3. Read-only tenant access. Only commands whose manifest effect is read (playbook §0 rule 1; the mail drafts and the PDF conversions are the write commands of ask-marcel-office-write). Respect every safety rule in playbook §0 (no external URL fetches, single-quote b! ids, --output-path for multi-MB bytes).
  4. Raw findings are private. Write the full report to .claude/qa-reports/YYYY-MM-DD.md (gitignored — tenant file names/subjects/IDs may appear there). The ONLY committed artifacts of a run are: the sanitized Run-log row appended to playbook §I, plus any updates to the §E2 drift register and §G roadmap register.
  5. Compare against the previous run. Read the most recent .claude/qa-reports/*.md first; anything that passed before and fails now is a REGRESSION and outranks same-severity new findings.
  6. Manifest-driven, not hand-typed. Regenerate worklists from read/dist/commands.json and write/dist/commands.json (snippets are in the playbook) so the audit scales as commands are added.
  7. Keep the playbook honest. If a check is wrong/stale (a contract changed intentionally, a fragile probe is obsolete), update docs/QA-PLAYBOOK.md in the same run — the playbook is a living document, but contract-matrix changes need the maintainer's explicit OK.
  8. Budget: a full run is several hours of focused work. If context runs low mid-run, write partial findings to the report file FIRST, then continue — never lose evidence.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.