agentmods.dev

rag-search-knowledge

rag-search-knowledge is a command for Claude Code from codingthefuturewithai/rag-retriever. It costs 0 tokens per session (536 once invoked), scanned A, original, MIT.

A command for semantic search across indexed knowledge collections. Semantic search finds text by meaning, rather than requiring an exact word match; the underlying RAG system retrieves relevant stored content.

In plain words
What is it for?
Use it to search the default collection, a named collection, or all collections, with optional limits on result count and relevance score.
Why use it?
It helps locate useful information in a knowledge base without manually browsing every document or guessing the exact wording used.

Command for Claude Code

not rated 27 1y ago A scan 0 tokens original MIT
View the source file codingthefuturewithai/rag-retriever
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.

agentmods
npx agentmods add commands/codingthefuturewithai/rag-retriever/rag-search-knowledge
Clone the repo
git clone --depth 1 https://github.com/codingthefuturewithai/rag-retriever

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-search-knowledge

README.md
[![agentmods](https://agentmods.dev/badge/commands/codingthefuturewithai/rag-retriever/rag-search-knowledge.svg)](https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-search-knowledge)
Your own site
<a href="https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-search-knowledge"><img src="https://agentmods.dev/badge/commands/codingthefuturewithai/rag-retriever/rag-search-knowledge.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 536 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.00536
Opus 5 $0.00000 $0.00268
Sonnet 5 $0.00000 $0.00107
Haiku 4.5 $0.00000 $0.00054

Measured 4d ago against content hash 99b8ae77b484, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

rag-search-knowledge scanned grade A with 0 findings 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 4d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/commands/rag-search-knowledge.md · 58 lines

How it starts

The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Search RAG Retriever Knowledge Base

Search across vector store collections for specific information and insights.

Prerequisites

This command requires the RAG Retriever MCP server to be configured in your Claude Code setup. The server provides semantic search capabilities across your indexed content collections.

Arguments

Use $ARGUMENTS to specify search parameters:

  • Query string (required)
  • Collection name (optional - if not specified, searches ONLY the "default" collection)
  • "all" - special keyword to search across ALL collections
  • Number of results (optional - defaults to 8)
  • Score threshold (optional - defaults to 0.3)

Examples:

  • "Claude Code documentation" - searches default collection only
  • "Claude Code documentation claude_code_docs" - searches specific collection
  • "Claude Code documentation all" - searches ALL collections
  • "error handling python 10 0.4" - searches default with custom limit/threshold
  • "error handling all 10 0.4" - searches all collections with custom parameters

Implementation Approach

This command uses direct implementation for focused knowledge retrieval.

Your Task

  1. Parse Arguments

    • Extract query from $ARGUMENTS
    • Identify optional collection name (or "all" for cross-collection search)
    • Parse optional limit and score threshold parameters
    • Use sensible defaults if not specified

  2. Collection Selection

    • If no collection specified: search ONLY the "default" collection
    • If "all" specified: set search_all_collections=True to search across all collections
    • If specific collection specified: search that collection only
    • Use list_collections to show available collections if user needs guidance

  3. Perform Search

    • Use vector_search with appropriate parameters:
      • search_all_collections=True if user specified "all"
      • collection_name=name if user specified a specific collection
      • Default to "default" collection if no collection specified
    • Display results with relevance scores and source information

Read the full file on GitHub · 58 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. 4d ago First seen · 58 lines · 0 tokens per session scan A 99b8ae77b484

Subscribe to this mod's changes

rag-search-knowledge is a command published in the GitHub repository codingthefuturewithai/rag-retriever (27 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 536 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AIRAG and vectors
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