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 agentmods add commands/codingthefuturewithai/rag-retriever/rag-audit-collectionsgit clone --depth 1 https://github.com/codingthefuturewithai/rag-retrieverWrote 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/commands/codingthefuturewithai/rag-retriever/rag-audit-collections)<a href="https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-audit-collections"><img src="https://agentmods.dev/badge/commands/codingthefuturewithai/rag-retriever/rag-audit-collections.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00509 |
| Opus 5 | $0.00000 | $0.00254 |
| Sonnet 5 | $0.00000 | $0.00102 |
| Haiku 4.5 | $0.00000 | $0.00051 |
Grade A, and why
rag-audit-collections 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 5d 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.
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.
Audit RAG Retriever Collections
Review and validate the current state of all vector store collections.
Prerequisites
This command requires the RAG Retriever MCP server to be configured in your Claude Code setup. The server provides access to vector store collections for analysis and validation.
Implementation Approach
This command uses direct implementation as it involves comprehensive analysis of existing collections.
Your Task
-
Collection Inventory
- Use
list_collectionsto get complete inventory - Analyze document counts and metadata for each collection
- Identify collections that may need attention
- Use
-
Content Quality Assessment
- Perform sample searches in each collection using known topics
- Evaluate result quality and relevance - are answers accurate and complete?
- Test for contradictory information - do different results conflict?
- Check for potential duplicates or outdated content
- Assess metadata richness and accuracy
- AI Quality Review: Use AI to evaluate sample content for accuracy, completeness, and currency
-
Usage Analysis
- Identify most and least used collections
- Evaluate collection organization and naming
- Check for overlapping content across collections
-
Health Check
- Verify all collections are accessible
- Check for any technical issues or corruption
- Validate search functionality across collections
-
Quality Assessment Workflow
- For each collection, search for 3-5 known topics and verify accuracy
- Check relevance scores - collections with consistently low scores (< 0.3) need attention
- Look for contradictory information within collections
- Identify outdated content that should be removed or updated
- Test cross-collection searches to find duplicate or conflicting information
-
Recommendations
- Suggest collections that need updating or re-indexing
- Recommend consolidation of similar collections
- Identify gaps in knowledge coverage
- Propose new collections for missing topic areas
- Flag quality issues: Highlight collections with poor, outdated, or contradictory content
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.
- 5d ago First seen · 58 lines · 0 tokens per session scan A 2707cd170834
rag-audit-collections 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 509 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.
Other commands, from other repositories
querying
Query documents from a search index using type-safe filters with support for pagination, sorting, field selection, scoring, and highlighting. Count matching documents efficiently without returning results.
agent-brain-index
Index documents for semantic search.
agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
ingest
Manually add knowledge to the Weaviate store.
web-ingest
Crawl the configured source and generate knowledge markdown files.
web-update
Re-ingest a URL or refresh a local knowledge file, then rebuild the search index.