Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add butchokoy25/lightrag-claude-skills --skill rag-querygit clone --depth 1 https://github.com/butchokoy25/lightrag-claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/butchokoy25/lightrag-claude-skills/rag-query)<a href="https://agentmods.dev/skills/butchokoy25/lightrag-claude-skills/rag-query"><img src="https://agentmods.dev/badge/skills/butchokoy25/lightrag-claude-skills/rag-query/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/butchokoy25/lightrag-claude-skills/rag-query"><img src="https://agentmods.dev/badge/skills/butchokoy25/lightrag-claude-skills/rag-query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.00677 |
| Opus 5 | $0.00008 | $0.00338 |
| Sonnet 5 | $0.00003 | $0.00135 |
| Haiku 4.5 | $0.00002 | $0.00068 |
Grade A, and why
rag-query scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
const auth = await fetch(BASE + '/auth-status').then(r => r.json()); How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 91 lines · 16 tokens per session scan A 4e7cb27869fd
rag-query is a skill published in the GitHub repository butchokoy25/lightrag-claude-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 677 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
knowledge-gravity-lab
Analyze folders of Markdown/text research notes, Obsidian vaults, paper cards, invention notes, or memory exports as a practical knowledge map. Use when asked to find center topics, noisy or low-signal notes, possible contamination, overgrown topic groups, orphan notes, cleanup actions, or a useful public…
understand-figma
Analyze a Figma file via the Figma REST API and generate an interactive design knowledge graph (pages, screens, components, component sets, instances, design tokens) with a kind:"design" dashboard.
company-brain
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so Claude can answer questions on your team's behalf. Team-scope sibling to second-brain (which is personal-scope). Seven modes — capture (drop something into the right structured dir), compile (process into wiki…
claude-md-updater
Scans the session for lessons and workflows, then proposes scoped CLAUDE.md edits. Use for save this lesson or add to context.
continuous-learning-construction
Automatically extract patterns, best practices, and reusable knowledge from construction automation sessions to improve future performance.
autograph
Schema-as-code enforcement for any Obsidian vault. Zero hardcoded domains. Use when creating vault cards, checking vault health, running schema compliance, deduplicating entities, generating MOC indexes, running decay cycles, bootstrapping a vault, fixing wikilinks, finding orphans or backlinks, extracting entities…