Rag setup project
Skill butchokoy25/lightrag-claude-skills/skills/rag-setup-project
Set up LightRAG persistent memory for a Cursor project with MCP configurationFrom its SKILL.md
npx -y skills add butchokoy25/lightrag-claude-skills --skill rag-setup-projectAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
4 things to look at
- reads credentialsReads from 1 credential source: `LIGHTRAG_API_KEY`.
- 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
- runs commandsInstructs the agent to run 4 commands, including `node -e "const fs = require('fs'); const config = { mcpServers: { 'lightrag-projects': { command: 'npx', args: ['-y', 'lightrag-mcp@latest'], env: { LIGHTRAG_URL: 'http` and 3 more.
- fetches URLsInstructs the agent to fetch 1 URL, including BASE + '/documents/text'.
SKILL.md
3.5 KB, 884 tokens by cl100k_base, as published. Nobody here has run it
RAG Setup Project — Configure LightRAG for a Cursor Project
Set up a project-level LightRAG knowledge graph instance for the current Cursor project. Creates the MCP configuration so Claude Code can query and store project-specific memory.
What this does
- Creates
.cursor/mcp.jsonin the project root with the LightRAG MCP server entry - Merges with existing MCP config if other servers are already configured
- Optionally seeds the graph with project context (README, package.json, architecture docs)
Setup steps
1. Create or merge MCP config
If .cursor/mcp.json does NOT exist:
node -e "
const fs = require('fs');
const config = {
mcpServers: {
'lightrag-projects': {
command: 'npx',
args: ['-y', 'lightrag-mcp@latest'],
env: {
LIGHTRAG_URL: 'http://YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORT',
LIGHTRAG_API_KEY: process.env.LIGHTRAG_API_KEY || 'YOUR_API_KEY'
}
}
}
};
fs.mkdirSync('.cursor', { recursive: true });
fs.writeFileSync('.cursor/mcp.json', JSON.stringify(config, null, 2));
console.log('Created .cursor/mcp.json');
"
If .cursor/mcp.json already exists, merge:
node -e "
const fs = require('fs');
const existing = JSON.parse(fs.readFileSync('.cursor/mcp.json', 'utf-8'));
existing.mcpServers = existing.mcpServers || {};
existing.mcpServers['lightrag-projects'] = {
command: 'npx',
args: ['-y', 'lightrag-mcp@latest'],
env: {
LIGHTRAG_URL: 'http://YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORT',
LIGHTRAG_API_KEY: process.env.LIGHTRAG_API_KEY || 'YOUR_API_KEY'
}
};
fs.writeFileSync('.cursor/mcp.json', JSON.stringify(existing, null, 2));
console.log('Merged lightrag-projects into existing .cursor/mcp.json');
"
2. Update .gitignore
Suggest adding .cursor/ to .gitignore if not already present:
node -e "
const fs = require('fs');
const gitignore = fs.existsSync('.gitignore') ? fs.readFileSync('.gitignore', 'utf-8') : '';
if (!gitignore.includes('.cursor')) {
fs.appendFileSync('.gitignore', '\n# Cursor IDE config\n.cursor/\n');
console.log('Added .cursor/ to .gitignore');
} else {
console.log('.cursor/ already in .gitignore');
}
"
3. Optionally seed the graph
If the user wants to seed the project graph with existing docs:
node -e "
const fs = require('fs');
const BASE = 'http://YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORT';
const API_KEY = process.env.LIGHTRAG_API_KEY || 'YOUR_API_KEY';
const files = ['README.md', 'package.json', 'ARCHITECTURE.md', 'CLAUDE.md']
.filter(f => fs.existsSync(f));
(async () => {
for (const file of files) {
const text = fs.readFileSync(file, 'utf-8');
await fetch(BASE + '/documents/text', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-API-Key': API_KEY
},
body: JSON.stringify({
file_source: '[SEED] ' + file + ' — project bootstrap',
text: text.slice(0, 10000)
})
});
console.log('Seeded:', file);
}
console.log('Done. Seeded ' + files.length + ' files.');
})();
"
After setup
- Restart Claude Code to pick up the new MCP config
- Use
/rag-project-queryto query project memory - Use
/rag-project-rememberto store project-specific knowledge - Use
/rag-project-syncat end of session to persist learnings
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.