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-remembergit 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-remember)<a href="https://agentmods.dev/skills/butchokoy25/lightrag-claude-skills/rag-remember"><img src="https://agentmods.dev/badge/skills/butchokoy25/lightrag-claude-skills/rag-remember/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-remember"><img src="https://agentmods.dev/badge/skills/butchokoy25/lightrag-claude-skills/rag-remember.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 | $0.00021 | $0.00648 |
| Opus 5 | $0.00010 | $0.00324 |
| Sonnet 5 | $0.00004 | $0.00130 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
rag-remember 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()); This is a copy
100% identical to rag-remember — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Type | Use for |
|---|---|
DECISION |
Architecture choices, tool selections, approach decisions |
FEEDBACK |
User corrections, preferences, "do this / don't do that" |
CONFIG |
Server settings, env vars, ports, credentials (NO actual secrets) |
PROJECT |
Project status, goals, deadlines, stakeholders |
PERSON |
People, roles, contact preferences, working styles |
REFERENCE |
URLs, docs, external resources, where to find things |
INSIGHT |
Debugging 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 blameinstead
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 · 84 lines · 21 tokens per session scan A d2c8cfb3e667
rag-remember is a skill published in the GitHub repository butchokoy25/lightrag-claude-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 648 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to rag-remember, differing in 0 lines, and is treated as a copy.
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