karpathy-guidelines
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid…
Use when users /learn, save, remember, or distill lessons.
Claude Code
$ npx -y skills add tigerless-labs/autoharness --skill learn --agent claude-code
Codex
$ npx skills add tigerless-labs/autoharness --skill learn -a codex -y
Cursor
$ npx skills add tigerless-labs/autoharness --skill learn -a cursor -y
Gemini CLI
$ npx skills add tigerless-labs/autoharness --skill learn -a gemini-cli -y
GitHub Copilot
$ npx skills add tigerless-labs/autoharness --skill learn -a github-copilot -y
OpenCode
$ npx skills add tigerless-labs/autoharness --skill learn -a opencode -y
Windsurf
$ npx skills add tigerless-labs/autoharness --skill learn -a windsurf -y
name: learn description: "Use when users /learn, save, remember, or distill lessons." category: general
Review the conversation so far and distill what is worth keeping — through the standard proposal chain, never by writing files.
1. Identify class-level, reusable lessons: corrected approaches, non-trivial techniques, durable user preferences. Skip one-off narratives and environment-specific failures. 2. Compare first: read the injected skill index; if an existing skill covers the topic, prefer a `patch`/`update` over creating a near-duplicate. 3. Reconcile old with new: when a lesson supersedes an earlier rule, search the managed skill trees for contradicting statements and stage updates so the new version wins everywhere. 4. Stage every change exclusively via the `stage_skill` tool, one intent per lesson, with `reason` and a verbatim `evidence` quote from this session. The deterministic promoter validates and lands; rejects are reported at the next session start.
If nothing meets the bar, say so and stage nothing.
Building the self-learning layer next-gen agents will ship with, learning skills from real work.
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