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-assess-qualitygit 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-assess-quality)<a href="https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-assess-quality"><img src="https://agentmods.dev/badge/commands/codingthefuturewithai/rag-retriever/rag-assess-quality.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 | $0.00908 |
| Opus 5 | $0.00000 | $0.00454 |
| Sonnet 5 | $0.00000 | $0.00182 |
| Haiku 4.5 | $0.00000 | $0.00091 |
Grade A, and why
rag-assess-quality 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 6d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assess Content Quality in RAG Retriever
Evaluate the quality, accuracy, and reliability of content in your RAG Retriever collections to ensure high-quality search results.
Prerequisites
This command requires the RAG Retriever MCP server to be configured in your Claude Code setup.
Implementation Approach
This command uses direct implementation for systematic quality assessment of indexed content.
Your Task
1. Quality Assessment Overview
CRITICAL: Poor quality, outdated, or contradictory documentation corrupts your knowledge base and leads to wrong answers. Quality assessment is essential for reliable RAG systems.
2. Pre-Assessment Setup
- Use
list_collectionsto get overview of all collections - Identify collections that need quality assessment
- Choose representative topics for each collection to test
3. Systematic Quality Testing
For each collection, perform these tests:
A. Accuracy Testing
- Search for 3-5 topics you know well in each collection
- Verify that answers are factually correct
- Check for incomplete or misleading information
B. Consistency Testing
- Search for the same topic across different collections
- Look for contradictory information
- Identify conflicting recommendations or facts
C. Currency Testing
- Search for version-specific information (e.g., "Python 3.12 features")
- Check if results reflect current vs. outdated information
- Look for deprecated methods or obsolete practices
D. Relevance Testing
- Monitor search relevance scores consistently
- Collections with scores consistently below 0.3 indicate quality issues
- Test edge cases and less common topics
4. Quality Issues to Flag
- Contradictory information: Same topic, different answers
- Outdated content: Old versions, deprecated features
- Incomplete information: Partial explanations, missing context
- Poor source quality: Unreliable or low-authority sources
- Duplicate content: Same information indexed multiple times
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.
- 6d ago First seen · 103 lines · 0 tokens per session scan A 939e4e5c21e4
rag-assess-quality 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 908 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
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
ingest
Manually add knowledge to the Weaviate store.
web-add
Ingest a single URL into the knowledge base.
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.