agentleFS
butchokoy25butchokoy25/lightrag-claude-skills/skills/rag-remember/SKILL.md

rag-remember

Skill2 starsChanged 7 months agoMITView on GitHub

Store a fact, decision, or observation into the personal LightRAG knowledge graph immediately

  • Reads credentials
  • Sends data out

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:

    Do NOT dump the raw API response

    line 68
  2. Distinctive:

    If the insert fails, tell the user and suggest trying again

    line 69

≈ “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

RAG Remember — Store to Personal Knowledge Graph

Store a specific fact, decision, or observation into your personal LightRAG knowledge graph. Use mid-session when something worth remembering comes up.

Formatting rules

Format each entry as:

[TYPE] Title — YYYY-MM-DD

What: Brief description of the fact or decision
Why: The reasoning or context behind it
Files: Relevant file paths (if applicable)

Entry types

TypeUse for
DECISIONArchitecture choices, tool selections, approach decisions
FEEDBACKUser corrections, preferences, "do this / don't do that"
CONFIGServer settings, env vars, ports, credentials (NO actual secrets)
PROJECTProject status, goals, deadlines, stakeholders
PERSONPeople, roles, contact preferences, working styles
REFERENCEURLs, docs, external resources, where to find things
INSIGHTDebugging lessons, performance findings, non-obvious learnings

How to store

node -e "
const BASE = process.env.LIGHTRAG_SERVER_URL || 'http://YOUR_LIGHTRAG_HOST:YOUR_PERSONAL_PORT';
(async () => {
  const auth = await fetch(BASE + '/auth-status').then(r => r.json());
  const token = auth.access_token;
  const res = await fetch(BASE + '/documents/text', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer ' + token
    },
    body: JSON.stringify({
      file_source: '[TYPE] Title — YYYY-MM-DD',
      text: 'Full content here with What/Why/Files structure'
    })
  });
  const data = await res.json();
  console.log('Status:', res.status);
})();
"

After inserting

  • Confirm to the user: "Stored: [TYPE] Title"
  • Do NOT dump the raw API response
  • If the insert fails, tell the user and suggest trying again

What NOT to store

  • Ephemeral task details — current conversation context, in-progress work steps
  • Code from the codebase — it's already in the repo; store the decision, not the code
  • Duplicates — query first if unsure whether something is already stored
  • Credentials or secrets — never store API keys, passwords, tokens
  • Git history — use git log / git blame instead

Infrastructure

  • Server: YOUR_LIGHTRAG_HOST:YOUR_PERSONAL_PORT
  • Auth: Bearer token from /auth-status (guest/disabled mode)
  • Env var: LIGHTRAG_SERVER_URL

Download now (ZIP)

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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.