RAG Evaluate Quality

RAG Evaluate Quality is an AI Engineering skill for Claude Code, published by lyonzin in knowledge-rag.

265 stars38 forkson lyonzin/knowledge-ragAdded 2026/09/02+2% in starsRepository updated 2026/09/01

antigravityclaudeclaude-codeclaude-code-clicodexcursor-aidocument-searchhybrid-searchinteligencia-artificialknowledge-baselocal-aimcpmcp-serverragrag-chatbotrag-pipelinererankingretrieval-augmented-generationsemantic-searchvector-database

Install in seconds

Install RAG Evaluate Quality

Copy RAG Evaluate Quality into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/lyonzin/knowledge-rag/tree/master/skills/maintenance/rag-evaluate-quality ~/.claude/skills/rag-evaluate-quality

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/lyonzin/knowledge-rag.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/maintenance/rag-evaluate-quality/SKILL.md in lyonzin/knowledge-rag
Installs to: ~/.claude/skills/rag-evaluate-quality
Collection: One of 10 skills cataloged from this repository
Category: AI Engineering — 2631 skills

What RAG Evaluate Quality does

RAG Evaluate Quality measures retrieval performance with MRR@5, Recall@5, Precision@5, and index health stats. Use it weekly, after reindexing, or when answer quality seems to drop.

RAG Evaluate Quality is cataloged under AI Engineering on DirSkills. RAG Evaluate Quality comes from a repository tagged antigravity, claude, claude-code, claude-code-cli and codex.

Documentation

README

Excerpt

rag-evaluate-quality — measure, do not guess

When to use this skill

Trigger this skill:

Weekly cadence — set a recurring reminder (Monday morning, Friday afternoon)
After significant reindex activity — new content added, models changed, presets swapped
When answer quality feels off — the user reports "search is worse than it used to be"
After a version upgrade — pip install -U knowledge-rag bump, worth confirming no regression
Before proposing tuning changes — if you are about to suggest hybrid_alpha=0.5 or min_score=0.3, measure first

Do NOT run:

Every session (waste of cycles; retrieval quality is stable session-to-session)
On brand-new empty corpora (nothing to evaluate)

What this skill commits to

This is the opening of the README. Read the full README on GitHub.

Commands RAG Evaluate Quality provides

Slash commands named in this skill’s SKILL.md, listed in the order they first appear.

/metrics

Frequently asked about RAG Evaluate Quality

What else does lyonzin publish alongside RAG Evaluate Quality?
RAG Evaluate Quality is one of 10 skills that DirSkills catalogs from lyonzin/knowledge-rag, the repository it ships in. Its siblings there include RAG Check First, RAG Code Review and RAG Index Decisions. Each one is a separate skill with its own page in this directory, installs the same way RAG Evaluate Quality does, and is maintained by lyonzin in that same repository. The rest of the collection is listed on the lyonzin/knowledge-rag page.
How does RAG Evaluate Quality compare to other AI Engineering skills?
RAG Evaluate Quality ranks #2240 by stars among the 2631 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of RAG Evaluate Quality against them. Open each page to compare what they document and how they install.