rag-remember

rag-remember is a skill for Claude Code from zhaixin244-wq/fnw. It costs 21 tokens per session (648 once invoked), scanned A, original, MIT.

A personal-memory tool that stores facts, decisions, feedback, project details, references, and lessons in a LightRAG knowledge graph.

In plain words
What is it for?
Use it to remember preferences, configuration notes, decisions, external references, and debugging lessons.
Why use it?
It prevents useful personal context from being forgotten between sessions and keeps recurring information in one searchable place.

Skill for Claude Code

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

Good fit Use it to remember preferences, configuration notes, decisions, external references, and debugging lessons.

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

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

agentmods 80×15 button for rag-remember

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-remember"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 648 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 $0.00021 $0.00648
Opus 5 $0.00010 $0.00324
Sonnet 5 $0.00004 $0.00130
Haiku 4.5 $0.00002 $0.00065

Measured 8d ago against content hash d2c8cfb3e667, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 8d 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());
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.claude/skills/rag-remember/SKILL.md · 84 lines

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 blame instead

Read the full file on GitHub · 84 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. 8d ago First seen · 84 lines · 21 tokens per session scan A d2c8cfb3e667

Subscribe to this mod's changes

rag-remember is a skill published in the GitHub repository zhaixin244-wq/fnw (29 stars, last pushed 3mo 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). 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

pinecone-research

Agent RAG and long-term memory with Pinecone.

NousResearch/hermes-agent · 16 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

mem0-integration

Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.

a5c-ai/babysitter · 27 tokens

install-openviking-memory

Install and configure the OpenViking long-term memory plugin for OpenClaw via natural conversation. Once installed, the plugin automatically captures facts from chats and recalls relevant context before each reply (auto-capture + auto-recall, cross-session). Covers prerequisites, install through OpenClaw's plugin…

volcengine/OpenViking · 191 tokens