rag-project-sync

rag-project-sync is a skill for Claude Code from zhaixin244-wq/fnw. It costs 23 tokens per session (745 once invoked), scanned A, original, MIT.

An end-of-session tool that reviews project work and saves new decisions, conventions, dependencies, configuration details, requirements, and lessons to project memory.

In plain words
What is it for?
Use it to record architecture changes, newly established rules, package changes, deployment settings, and discovered workarounds.
Why use it?
It helps preserve important project knowledge after a session ends, so later work does not have to reconstruct the same context.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter.

Good fit Use it to record architecture changes, newly established rules, package changes, deployment settings, and discovered workarounds.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zhaixin244-wq/fnw/rag-project-sync
Install

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.

Any agent
npx skills add zhaixin244-wq/fnw --skill rag-project-sync
Clone the repo
git clone --depth 1 https://github.com/zhaixin244-wq/fnw

Made for: Claude Code.

Wrote 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.

agentmods badge for rag-project-sync

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-project-sync/github.svg)](https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-project-sync)
Your own site
<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-project-sync"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-project-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.

agentmods 80×15 button for rag-project-sync

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-project-sync"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-project-sync.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 745 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.00745
Opus 5 $0.00012 $0.00373
Sonnet 5 $0.00005 $0.00149
Haiku 4.5 $0.00002 $0.00075

Measured 9d ago against content hash 2c31f5f01ffc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

rag-project-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 9d 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 res = await fetch(BASE + '/documents/text', {
.claude/skills/rag-project-sync/SKILL.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAG Project Sync — End-of-Session Project Memory Sync

Review what happened this session and push project-relevant learnings to the project-level LightRAG knowledge graph.

Workflow

Step 1: Review the session

Scan the conversation for project-specific items worth persisting:

  • Architecture decisions — component design, data flow changes, API structure
  • Conventions established — naming patterns, file organization, coding standards
  • Dependencies added/changed — new packages, version bumps, API integrations
  • Config changes — env vars, build settings, deployment config
  • Requirements clarified — business rules, constraints, edge cases discovered
  • Gotchas found — non-obvious behaviors, platform quirks, workarounds needed

Step 2: Filter ruthlessly

Remove anything that:

  • Is already in the codebase (code, comments, configs)
  • Is in git history (commits, diffs)
  • Was already stored via /rag-project-remember this session
  • Is personal (not project-specific) — redirect to /rag-sync instead
  • Is ephemeral (current branch state, temp files)

Step 3: Format entries

Format each item using the /rag-project-remember format:

[TYPE] Title — YYYY-MM-DD

What: ...
Why: ...
Files: ... (if applicable)

Step 4: Confirm with user

Present the list:

Ready to sync 2 items to project memory:

  1. [ARCHITECTURE] API routes use controller pattern with service layer — 2026-04-01
  2. [CONVENTION] All database queries go through repository classes — 2026-04-01

Proceed? (y/n)

Wait for confirmation before inserting.

Step 5: Insert each item

node -e "
const BASE = 'http://YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORT';
const API_KEY = process.env.LIGHTRAG_API_KEY || 'YOUR_API_KEY';
(async () => {
  const res = await fetch(BASE + '/documents/text', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'X-API-Key': API_KEY
    },
    body: JSON.stringify({
      file_source: 'DESCRIPTION_HERE',
      text: 'CONTENT_HERE'
    })
  });
  console.log('Status:', res.status);
})();
"

Read the full file on GitHub · 106 lines

Changes

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.

  1. 9d ago First seen · 106 lines · 23 tokens per session scan A 2c31f5f01ffc

Subscribe to this mod's changes

rag-project-sync is a skill published in the GitHub repository zhaixin244-wq/fnw (29 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 745 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 2026-09-03.

Related

Other skills, from other repositories

AgentDB Memory Patterns

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

ruvnet/RuView · 45 tokens

pinecone-research

Agent RAG and long-term memory with Pinecone.

NousResearch/hermes-agent · 16 tokens

agent-v3-memory-specialist

Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.

ruvnet/ruflo · 25 tokens

langchain

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG…

davila7/claude-code-templates · 79 tokens

llm-wiki

The foundational knowledge distillation pattern for building and maintaining an AI-powered Obsidian wiki. Based on Andrej Karpathy's LLM Wiki architecture. Use this skill whenever the user wants to understand the wiki pattern, set up a new knowledge base, or needs guidance on the three-layer architecture (raw sources…

Ar9av/obsidian-wiki · 113 tokens

browserwing-admin

Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.

MemTensor/MemOS · 47 tokens