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butchokoy25butchokoy25/lightrag-claude-skills/skills/rag-project-remember/skill.md

rag-project-remember

Skill2 starsChanged 7 months agoMITView on GitHub

Store project-specific knowledge into the project-level LightRAG knowledge graph

  • Reads credentials
  • Sends data out

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:

    Do NOT dump the raw API response

    line 66
  2. Very specific:

    Use /rag-remember (personal graph) instead for:

    line 70

≈ “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 Project Remember — Store to Project Knowledge Graph

Store project-specific knowledge — architecture decisions, conventions, dependencies, and requirements — into the project-level LightRAG graph.

Formatting rules

Format each entry as:

[TYPE] Title — YYYY-MM-DD

What: Brief description
Why: The reasoning or constraint behind it
Files: Relevant file paths (if applicable)

Entry types

TypeUse for
ARCHITECTURESystem design choices, component structure, data flow
CONVENTIONCoding standards, naming patterns, file organization rules
DECISIONTechnology choices, library selections, approach decisions
DEPENDENCYExternal services, APIs, packages, version constraints
CONFIGEnvironment setup, build config, deployment settings
REQUIREMENTBusiness rules, constraints, acceptance criteria
INSIGHTPerformance findings, gotchas, non-obvious behaviors

How to store

node -e "
const BASE = 'http://YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORT';
const API_KEY = process.env.LIGHTRAG_API_KEY || 'YOUR_API_KEY';
(async () => {
  const res = await fetch(BASE + '/documents/text', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'X-API-Key': API_KEY
    },
    body: JSON.stringify({
      file_source: '[TYPE] Title — YYYY-MM-DD',
      text: 'Full content here with What/Why/Files structure'
    })
  });
  console.log('Status:', res.status);
})();
"

After inserting

  • Confirm to the user: "Stored to project memory: [TYPE] Title"
  • Do NOT dump the raw API response

When NOT to use this

Use /rag-remember (personal graph) instead for:

  • User preferences — working style, tool preferences, communication style
  • Cross-project knowledge — things that apply to all projects
  • Personal context — roles, responsibilities, machine config
  • People info — contacts, working relationships

This skill is for things specific to the current project that wouldn't be relevant elsewhere.

Infrastructure

  • Server: YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORT
  • Auth: X-API-Key header (auth-enabled mode)
  • Env var: LIGHTRAG_API_KEY

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.