agentleFS
d2a8k3ud2a8k3u/adaptive-cognitive-memory/skills/memory-curate/SKILL.md

memory-curate

Skill4 starsChanged 16 days agoMITView on GitHub

Review this project's memories with the user and clean them up: hypotheses, duplicates, stale facts, wrong scope and recall keys. Use when the user asks to curate, review, audit or tidy the memory, or when memory_status shows many hypotheses.

2 other projects use a version of this.

Adopted by

Most recently taken up by

  • Other versions2 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 2

    1. basicmachines-co/basic-memory★ 4.5ksame name
    2. charliehzm/medharness★ 85same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repositoryOne other file it gives its agents.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 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.

  • 2

    Very specific

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

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

    Status: Call memory_status and keep its counts by state and kind for the end.

    line 13
  2. Very specific:

    Before and after: Call memory_status again and report both sets of counts.

    line 19

≈ “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

Memory curation

Propose every change and wait for the user's confirmation before you delete, supersede or update a memory; never run this unattended. Present the proposals as a short list with each memory's id and title.

  1. Status. Call memory_status and keep its counts by state and kind for the end.
  2. Hypotheses. memory_list with state: "hypothesis"; read each with memory_get. Propose to keep it (a used verdict through memory_feedback brings it back), correct it with memory_update or delete it with memory_delete.
  3. Duplicates. For each hypothesis and each memory on the listed pages, memory_search its title; it finds active and hypothesis memories alike. A different memory that teaches the same lesson is a duplicate. Propose one merge per pair: keep the more complete one (an active one before a hypothesis), fold anything only the other says into it with memory_update, then memory_delete the other; for a changed single-valued fact use memory_store with resolution SUPERSEDE instead.
  4. Stale facts. memory_list with kind: "fact", newest first; pass next_cursor as before for the next page. A changed value is a memory_store with resolution SUPERSEDE; a fact that no longer holds is a memory_delete.
  5. Wrong scope. Scope and kind cannot change in place: store the memory again with the right scope or kind (memory_store), then delete the old one (memory_delete).
  6. Recall keys. Fix keywords and anchors with memory_update, following the authoring standard (memory/references/authoring.md); each replaces the stored value whole. A memory without questions is found less often: propose 3–6, in English and in the user's language, and add them with memory_update.
  7. Before and after. Call memory_status again and report both sets of counts.

A memory that memory_list finds only with project: "all" belongs to another project: it is read-only here and can be changed only from a session in its own project.

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.