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AymanShamsAymanShams/codex-coding-os/.agents/skills/deep-critic/SKILL.md

deep-critic

Skill1 starsChanged 2 months agoMPL-2.0View on GitHub

Use when the user asks to review, critique, audit, validate, challenge, stress-check, pressure-test, compare, poke holes in, find flaws in, or give a hard second opinion on a user-submitted artifact, document, screenshot, recommendation, analysis, strategy, proposal, plan, argument, model, table, deck, AI answer, workflow, rule, agent behavior, coding approach, or opinion. Use when the task needs skeptical reasoning, evidence-quality review, hidden-assumption detection, source-chain critique, methodology critique, risk identification, adversarial simulation, premortem thinking, self-grilling, or a rebuilt recommendation after critique. Use the full 12-step critique workflow when the user asks for deep analysis, a source-backed audit, recurring failure analysis, stress test, premortem, or "the same 12 steps." Do not use for normal execution, drafting from scratch, implementation, pure summarization, grammar-only editing, or routine answers unless critique depth is explicitly requested. If the primary task is fact checking, source validation, or citation review, use evidence-checker. If the primary task is numeric, forecasting, pricing, KPI, or model review, use quant-review. If the artifact is an SSOT-style SOP, policy, workflow, governance document, implementation guide, controlled template, or formal company artifact, use ssot-auditor. If the user asks what could kill a concrete plan or decision before launch, use pre-mortem.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 5 are distinctive, and 1 is 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.

  • 1

    Very specific

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

Licensed with conditions — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “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.

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.

Check your setup, free