agentleFS
Rishabh-ChoudhryRishabh-Choudhry/anchor-memory/skills/capture-decision/SKILL.md

capture-decision

Skill0 starsChanged 5 months agoMITView on GitHub

Use when a load-bearing decision was made or reversed — appends a dated, WHY-first entry to the memory ledger, optionally with a verifiable @meta anchor.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If the decision is tied to a file/metric, add an @meta block (see docs/anchor-anchors-spec.md) so the linter can verify it later.

    line 10
  2. Very specific:

    If this reverses a prior decision, set the old entry's status: superseded and add supersedes: <old-id> to the new one.

    line 12
  3. Very specific:

    Run python3 <linter> --root .

    line 14

≈ “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

capture-decision

  1. Read memory/decisions.md.
  2. Append an entry: ## {DATE} — {short title} then **Why:** … **Impact:** … **Status:** active.
  3. If the decision is tied to a file/metric, add an @meta block (see docs/anchor-anchors-spec.md) so the linter can verify it later.
  4. If this reverses a prior decision, set the old entry's status: superseded and add supersedes: <old-id> to the new one.
  5. Run python3 <linter> --root . to confirm no new drift was introduced.

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.