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evergonlabsevergonlabs/aiframework/.claude/skills/aiframework-learn/SKILL.md

aiframework-learn

Skill0 starsChanged 7 months agoMITView on GitHub

Capture a project learning to persistent storage. Saves to tools/learnings/ (JSONL) and optionally vault/memory/notes/.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If the learning is significant (would affect future architecture decisions):

    line 29
  2. Very specific:

    Create a note in vault/memory/notes/ with YAML frontmatter

    line 30

≈ “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

/aiframework-learn — Capture Learning

Usage

/aiframework-learn "description of what was learned"

Step 1: Classify

Determine category: bug, gotcha, pattern, or decision

Step 2: Save to learnings file

Append a JSON line to tools/learnings/aiframework-learnings.jsonl:

{"date": "YYYY-MM-DD", "category": "category", "summary": "one-line", "detail": "full explanation", "files": ["relevant/files"]}

Step 3: Save to vault (if significant)

If the learning is significant (would affect future architecture decisions):

  • Create a note in vault/memory/notes/ with YAML frontmatter
  • Or create a decision record in vault/memory/decisions/ using ADR format from vault/templates/decision-record.md

Step 4: Confirm

Output: "Learning captured: [summary]"

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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