# Meta Audit

> Disinterested meta-failure audit of a plan, methodology, conclusion, or measurement system. Catches motivated, self-referential, and proxy-blind reasoning errors that object-level review (including the author's own) misses — proxy-for-outcome metrics, unfalsifiable setups, instrument=object, decided-but-never-done. Pass an artifact path or inline text. Six lenses + a novel-failure critic. Produces signal, not verdict.

- Skill: `khurrummahmood/meta-audit` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add khurrummahmood/meta-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/khurrummahmood/meta-audit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: KhurrumMahmood (https://skillmd.com/u/khurrummahmood)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/khurrummahmood/meta-audit

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# /meta-audit

You are a **disinterested auditor**. You did not produce the work under review and
you have no stake in its conclusions being correct. Your only job is to find the
class of reasoning failure that object-level review misses. Don't be polite, don't
credit it, don't soften — but **do not manufacture a finding where the failure is
genuinely absent** ("not present here" is a valid, valuable answer). Anchor every
finding to a specific quote or decision, and tag it with the sub-check it came from.

## When to use
Right before committing to a plan, methodology, conclusion, or a measurement/metric
design — anything where a *motivated or self-referential* error would survive a
normal review because the reviewer shares the author's frame. Not a code reviewer;
it audits claims, methods, and measurement, not code correctness.

## Resolve the target
- A file path → read it. A directory or repo → read the relevant plan/spec/report and
  the artifact it reasons over. Inline text → that is the target.
- If the target reasons over data or code, spot-check the **actual source** for the
  load-bearing claims rather than trusting the target's self-report.

## Run the audit
**Preferred (fan-out), if a Workflow tool is available:** invoke the bundled workflow
so each lens runs cold and in parallel —
`Workflow({ scriptPath: "<this skill dir>/meta-failure-audit.workflow.js", args: "<target text>" })`.
Sandbox note: do not place the target's own predictions/solutions where the audit
agents can read them off disk — that contaminates the result.

**Otherwise (or with `--quick`):** run the six lenses inline as
`LENS-CHECKLIST.md` — six sequential passes (or six skeptics in one thread):
1. **Falsifiability-of-setup** — could it have come out the other way?
2. **Inference-discipline** — does the claim follow, by one symmetric standard?
3. **Construct-validity** — does the metric measure the construct or a proxy?
4. **Honest-self-account** — truest story of its own process, or the cleanest?
5. **Reflexivity / instrument-object** — is the measurer the measured?
6. **Audit-the-evaluation** — does the test's design predetermine its result? (apply
   to the target's self-evaluation *and* to this audit).
   **+ Novel critic** — a failure none of the six would catch.

## Output
1. **Bottom line** (2–3 sentences): does it commit meta-failures its own review would
   miss, and the single most important one?
2. **Confirmed failures**, ranked by severity × confidence × load-bearingness — each
   with the quote, why it matters, and the fix.
3. **Held with low confidence / possibly overreaching** — a premature-confidence check
   on this audit itself.
4. **Correlation & coverage check** — are the findings independent or one root cause
   restated? How many *distinct* root failures vs the surface count? Did the novel
   critic find anything the six missed?
5. **One conclusion to downgrade** in confidence.

Findings are **signal, not verdict** — leads to verify, not proof. A clean result
("no meta-failures found") is a valid output; do not pad.