rag-query
Skill2 starsChanged 7 months agoMITView on GitHub
Query the personal LightRAG knowledge graph for persistent memory across sessions
- Reads credentials
- Sends data out
2 other projects use a version of this.
Adopted by
Most recently taken up by
- chriswestt/claude-skillstheir own version, first seen Oct 9, 2026
- Zengxiansheng007/skills-oldtheir own version, first seen Aug 24, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- Zengxiansheng007/skills-old★ 1same name
- chriswestt/claude-skills★ 0same 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 repository6 other files it gives its agents.
- rag-project-querySkill
- rag-project-rememberSkill
- rag-project-syncSkill
- rag-rememberSkill
- rag-setup-projectSkill
- rag-syncSkill
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
- Distinctive: line 80
When presenting results to the user:
- Distinctive: line 84
If no results found, say so clearly — don't fabricate
≈ “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.
Files
RAG Query — Personal Knowledge Graph
Query your personal LightRAG knowledge graph to recall decisions, preferences, project context, and learnings from past sessions.
Query Modes
| Mode | When to use | What it does |
hybrid (default) | Most queries | Combines local entity relationships + global topic summaries |
local | "What's related to X?" | Traverses entity relationships from specific nodes |
global | "What do you know about topic Y?" | Summarizes across all documents on a topic |
naive | Simple keyword lookup | Basic text search, fastest but least intelligent |
How to query
Full query (returns answer)
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 + '/query', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer ' + token
},
body: JSON.stringify({
query: 'QUERY_HERE',
mode: 'hybrid'
})
});
const data = await res.json();
console.log(JSON.stringify(data, null, 2));
})();
"Context-only query (returns raw context for Claude to synthesize)
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 + '/query', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer ' + token
},
body: JSON.stringify({
query: 'QUERY_HERE',
mode: 'hybrid',
only_need_context: true
})
});
const data = await res.json();
console.log(typeof data === 'string' ? data : JSON.stringify(data, null, 2));
})();
"Response format
When presenting results to the user:
- Be concise — summarize, don't dump raw JSON
- Cite the source type when relevant (e.g., "from a stored decision on 2026-03-15")
- Flag potentially stale information (anything >30 days old)
- If no results found, say so clearly — don't fabricate
Infrastructure
- Server:
YOUR_LIGHTRAG_HOST:YOUR_PERSONAL_PORT - Auth: Bearer token from
/auth-status(guest/disabled mode) - Env var:
LIGHTRAG_SERVER_URL
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
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.

