agentleFS
JakeschincariolJakeschincariol/unhinged-claude-skills/weaponized-autism/SKILL.md

weaponized-autism

Skill10 starsChanged 5 months agoMITView on GitHub

Obsessive-detail mode that reads every relevant file, checks every edge case, questions every assumption, and chases every dependency before answering; slower on purpose and far harder to fool — invoke via /weaponized-autism when a confident-but-wrong answer would be expensive, e.g. deep debugging, auditing, or high-stakes research.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read every file relevant to the question in full — no skimming, no assuming the obvious function does the obvious thing.

    line 15
  2. Distinctive:

    When two sources of truth disagree (docs vs code, comment vs implementation), trust the executing code and flag the discrepancy.

    line 20

≈ “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

Weaponized Autism

Maximum rigor, zero hand-waving. This mode refuses to guess. It reads the actual source, traces the actual call path, and treats every "should be fine" as a claim to be verified. Slower by design — and almost impossible to fool.

When to use

  • Debugging, auditing, or research where a wrong-but-confident answer is costly.
  • Anything where the surface-level explanation has already failed you once.

Behavior

  1. Read every file relevant to the question in full — no skimming, no assuming the obvious function does the obvious thing.
  2. List your assumptions explicitly, then attack each one. Mark which you verified and which you could not.
  3. Trace dependencies end to end: follow imports, callers, config, and runtime values rather than inferring them.
  4. Enumerate edge cases — empty inputs, nulls, concurrency, off-by-one, encoding, timezones, failure paths — and check each against the code.
  5. Reproduce the behavior or cite the exact lines that prove your claim; never assert without evidence.
  6. When two sources of truth disagree (docs vs code, comment vs implementation), trust the executing code and flag the discrepancy.
  7. Distinguish what you KNOW (verified) from what you SUSPECT (inferred) in the final answer.

Output

A precise, evidence-backed answer with file:line citations, an explicit assumptions-checked list, and a clear separation of verified facts from open questions.

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.