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 zhaixin244-wq/fnw --skill rag-syncgit clone --depth 1 https://github.com/zhaixin244-wq/fnwWrote 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/zhaixin244-wq/fnw/rag-sync)<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-sync"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-sync/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/zhaixin244-wq/fnw/rag-sync"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-sync.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 | $0.00020 | $0.00786 |
| Opus 5.5 | $0.00008 | $0.00314 |
| Sonnet 5.5 | $0.00004 | $0.00157 |
| Haiku 4.5 | $0.00002 | $0.00079 |
Grade A, and why
rag-sync 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 1mo 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()); Copies of this mod
1 near-identical copy found in the catalogue:
- rag-sync — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Sync — End-of-Session Personal Memory Sync
Review what happened this session and push non-obvious learnings to your personal LightRAG knowledge graph. Run before ending a productive session.
Workflow
Step 1: Review the session
Scan the conversation for items worth persisting. Look for:
- Decisions — architecture choices, tool selections, approach changes
- User corrections — "don't do X", "always do Y", preference changes
- Config changes — new env vars, port changes, server settings
- Project updates — status changes, new goals, deadline shifts
- Debugging insights — root causes found, non-obvious fixes
- New relationships — connections between systems, people, or projects
Step 2: Filter ruthlessly
Remove anything that:
- Is already in the codebase (code, comments, configs)
- Is in git history (commits, diffs, blame)
- Was already stored via
/rag-rememberthis session - Is ephemeral (task progress, temp files, current branch state)
- Is a duplicate of something already in the knowledge graph
Step 3: Format entries
Format each item using the /rag-remember format:
[TYPE] Title — YYYY-MM-DD
What: ...
Why: ...
Files: ... (if applicable)
Step 4: Confirm with user
Present the list of items to store. Example:
Ready to sync 3 items to personal memory:
- [DECISION] Switched auth from JWT to session tokens — 2026-04-01
- [FEEDBACK] User prefers single bundled PRs for refactors
- [CONFIG] LightRAG project instance moved to port 9625
Proceed? (y/n)
Wait for user confirmation before inserting.
Step 5: Insert each item
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: 'DESCRIPTION_HERE',
text: 'CONTENT_HERE'
})
});
console.log('Status:', res.status);
})();
"
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.
- 1mo ago First seen · 109 lines · 20 tokens per session scan A 25e90cae21ed
rag-sync is a skill published in the GitHub repository zhaixin244-wq/fnw (30 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 786 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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