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Honest output auditor

Skill zgroves24/agent-operator-skills/skills/honest-output-auditor

Reusable AI-agent skills for planning, verification, security reviews, SEO audits, changelogs, CLIs, and multi-step coding loops.

Install
npx -y skills add zgroves24/agent-operator-skills --skill honest-output-auditor

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when the user asks for honesty, a hard audit, a skill audit, best work, no half-finished output, when a deliverable was rejected, or before finalizing high-stakes creative, product, code, or operational work.

SKILL.md

2.9 KB, as published. Nobody here has run it

Honest Output Auditor

Core Rule

Act like the work is probably wrong until evidence proves otherwise. The job is to catch broken assumptions, stale guidance, violated user constraints, weak artifacts, and self-protective rationalization before anything is called done.

Mandatory Audit

  1. Extract the latest hard constraints from the user. The newest user constraint beats repo docs, older skill text, memory, and prior plans.
  2. Audit the skill or source docs before trusting them. If a skill conflicts with the user or current product truth, say so and patch or bypass it.
  3. Verify the actual artifact, not the intention. Inspect source diffs, rendered frames, transcripts, logs, screenshots, tests, live app state, or published state as applicable.
  4. Grade the output as Rejected, Needs Rework, or Ship Candidate.
  5. If a hard constraint is broken, mark the artifact rejected and stop presenting it as deliverable.
  6. Say the failure plainly. Do not call weak work ready unless it would survive a hostile review.

Red Flags

  • A skill says a pattern is preferred, but the user has rejected it.
  • A large source artifact produces one shallow deliverable without a full extraction queue.
  • A visual deliverable uses fake UI, stale screenshots, generic motion graphics, or decorative overlays to hide lack of proof.
  • A final answer says "done" without showing the evidence used to verify it.
  • The agent keeps explaining process instead of fixing the artifact.
  • The agent acknowledged a required workflow change, then defaulted back to the old faster workflow.
  • Product proof does not match the spoken or written claim.
  • Contact sheets, summaries, or partial checks are used as a substitute for full artifact review.

Evidence Ladder

Prefer stronger evidence over weaker evidence:

  1. Live state readback from the target system.
  2. Local run with screenshots, logs, or test output.
  3. Source diff plus targeted tests.
  4. Static inspection only.
  5. Memory-derived or assumption-based claim.

When the evidence is below the level the task deserves, say that and keep the output in Needs Rework or Rejected.

Skill Audit Standard

When auditing a skill:

  • Check whether its required reads are current and actually exist.
  • Search for stale rules that conflict with the latest user correction.
  • Search for personal data, local paths, company names, customer names, secrets, and account-specific assumptions.
  • Patch the skill if it is locally owned and the correction is durable.
  • Do not publish or share a skill until a privacy/provenance scan passes.
  • Re-run the audit against the changed skill before resuming production.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.