agentleFS
MegapromptingMegaprompting/torque-loop/skills/prompt-audit/SKILL.md

prompt-audit

Skill10 starsChanged 4 months agoMITView on GitHub

Audit a prompt library as an operating system, not a pile of prompts. Use to find what a prompt collection over-does, under-does, and where it leaks into meta-looping. Classifies each prompt by the work it performs, finds overrepresented and missing moves, flags prompts that cause meta-loops or never produce artifacts, and returns the minimum upgraded set as a sequenced workflow.

5 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

/ratchet:prompt-audit — the library as an OS

A prompt library is not a pile of good words; it is an operating system for cognition. This command audits it as one. It does not rewrite prompts first — it diagnoses the system: what work each prompt does, which moves are missing, and where the library lets the model escape into elegant analysis instead of producing something.

Step 0 — Load the library

Take the prompt list from the argument, a file, or the current context. Do not rewrite yet.

Classify

Tag each prompt by the type of work it performs (a prompt may do more than one):

  • target locking · diagnosis · artifact production · adversarial testing · decision · patching · memory/state · orchestration · compression · boundary pushing

Find the system's shape

  1. Overrepresented moves — where the library is thick (usually diagnosis and "next move" prompts).
  2. Missing moves — where it is thin (usually artifact production, patching, testing, serialized state).
  3. Meta-looping prompts — those that ask what to ask next without ever forcing a move. These are the hallway of mirrors.
  4. Artifact-producing prompts — the ones that actually leave something behind.
  5. Prompts to delete or merge — redundant, vague, or low-consequence.
  6. The minimum new prompt set that would outperform the current library.

The operating rule

Judge every prompt against this bar. Keep it only if it does at least one:

  1. Forces a choice. 2. Creates an artifact. 3. Tests an artifact. 4. Patches a defect.
  2. Serializes useful state. 6. Kills a bad option. 7. Pushes to a higher-yield move.

Everything else is smoke.

Output contract

CLASSIFICATION: <table: prompt → work type(s)>
OVERREPRESENTED: <moves the library over-does>
MISSING: <moves it lacks — the expensive gaps>
META-LOOPERS: <prompts that never force a move>
DELETE / MERGE: <the cuts>
UPGRADED SET (sequenced workflow, not a pile):
1. <prompt> — <the move it forces>
2. ...

Serialize

ratchet artifact add '{"title":"prompt-library audit","kind":"decision-record","status":"v0","holes":["..."]}'

Produce the upgraded library as a sequenced workflow — frame → choose → build → attack → patch → serialize → advance — never a flat list. Next: /ratchet:build the new set.

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.