Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate).
Install RAG Auditor
$ npx skills add Mathews-Tom/armory --skill rag-auditor -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Mathews-Tom/armory rag-auditor --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/rag-auditor .claude/skills/rag-auditor && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "rag-auditor" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/rag-auditor into .claude/skills/rag-auditor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-auditor", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Mathews-Tom/armory/tree/main/skills/rag-auditorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Mathews-Tom/armory --skill rag-auditor -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Mathews-Tom/armory rag-auditor --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/rag-auditor .agents/skills/rag-auditor && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rag-auditor" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/rag-auditor into .agents/skills/rag-auditor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-auditor", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Mathews-Tom/armory --skill rag-auditor -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Mathews-Tom/armory rag-auditor --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/rag-auditor .cursor/skills/rag-auditor && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "rag-auditor" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/rag-auditor into .cursor/skills/rag-auditor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-auditor", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Mathews-Tom/armory.git --path skills/rag-auditor--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Mathews-Tom/armory --skill rag-auditor -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Mathews-Tom/armory rag-auditor --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/rag-auditor .gemini/skills/rag-auditor && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "rag-auditor" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/rag-auditor into .gemini/skills/rag-auditor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-auditor", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Mathews-Tom/armory rag-auditorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Mathews-Tom/armory --skill rag-auditor -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/rag-auditor .github/skills/rag-auditor && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "rag-auditor" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/rag-auditor into .github/skills/rag-auditor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-auditor", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Mathews-Tom/armory --skill rag-auditor -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Mathews-Tom/armory rag-auditor --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/rag-auditor .opencode/skills/rag-auditor && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "rag-auditor" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/rag-auditor into .opencode/skills/rag-auditor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-auditor", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
rag-auditor- GitHub stars
- 328
- Token cost
- ~2.4k tokens
- SKILL.md length
- 728 words
- Files
- 6 (incl. references)
- Skills in repo
- 80
- Repo updated
- First seen
- Licence
- MIT
At a glance
Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate).
- Works in 6 steps: Pipeline Inventory → Design Evaluation Queries → Evaluate Retrieval → …
- : audit RAG pipeline
- SKILL.md covers Reference Files, Prerequisites, Workflow and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
RAG Auditor is an agent skill from Mathews-Tom/armory. Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate). Triggers on: "audit RAG pipeline", "RAG quality", "hallucination detection", "why is RAG failing", "grounding check". NOT for general architecture audits, use architecture-reviewer.
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `evals/cases.yaml`, `references/diagnostic-queries.md` and `references/failure-taxonomy.md`).
It sits in AI & LLM Engineering, covering Retrieval-augmented generation. The repository describes itself as: Curated, production-grade skills for AI coding agents. Battle-tested workflows for developers who use AI seriously. The licence is MIT.
When your agent uses it
- : audit RAG pipeline
- Hallucination detection
- Why is RAG failing
- Grounding check
Example prompts
- “audit RAG pipeline”
- “RAG quality”
- “hallucination detection”
- “/rag-auditor”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4594fb7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
RAG Auditor loads about 2.4k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 728 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from Mathews-Tom/armory at commit 4594fb7, republished under its MIT licence (© Mathews-Tom). 728 words, ~2,380 tokens.
Download SKILL.mdSave it as
.claude/skills/rag-auditor/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- rag-auditor
- description
- Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate). Triggers on: "audit RAG pipeline", "RAG quality", "hallucination detection", "why is RAG failing", "grounding check". NOT for general architecture audits, use architecture-reviewer.
- metadata.version
- 1.1.1
- metadata.category
- review
- metadata.tags
- rag, retrieval, hallucination, grounding
- metadata.difficulty
- advanced
- metadata.phase
- build
RAG Auditor
Systematic RAG pipeline evaluation across the full retrieval-generation chain: designs evaluation query sets, measures retrieval metrics (Precision@K, Recall@K, MRR), evaluates generation quality (groundedness, completeness, hallucination rate), diagnoses component-level failures, and recommends targeted improvements.
Reference Files
| File | Contents | Load When |
|---|---|---|
references/retrieval-metrics.md | Precision@K, Recall@K, MRR, NDCG definitions and calculation | Always |
references/generation-metrics.md | Groundedness, completeness, hallucination detection methods | Generation evaluation needed |
references/failure-taxonomy.md | RAG failure categories: retrieval, generation, chunking, embedding | Failure diagnosis needed |
references/diagnostic-queries.md | Designing evaluation query sets, known-answer questions, difficulty levels | Evaluation setup |
Prerequisites
- Access to the RAG pipeline (or its outputs for post-hoc evaluation)
- A set of test queries with known-correct answers
- Understanding of the pipeline components (embedding model, retriever, generator)
Workflow
Phase 1: Pipeline Inventory
Document the RAG pipeline configuration:
- Document source — What documents are indexed? Format, count, size.
- Chunking — Strategy (fixed-size, semantic, paragraph), chunk size, overlap.
- Embedding — Model name and version, dimensionality.
- Vector store — Type (FAISS, Pinecone, Chroma, pgvector), index type.
- Retrieval — Method (similarity, hybrid, reranking), top-K parameter.
- Generation — Model, prompt template, context window usage.
Phase 2: Design Evaluation Queries
Create a diverse set of test queries:
| Query Type | Purpose | Count |
|---|---|---|
| Known-answer (factoid) | Measure retrieval + generation accuracy | 10+ |
| Multi-hop | Require combining info from multiple chunks | 5+ |
| Unanswerable | Not in the corpus — should abstain | 3+ |
| Ambiguous | Multiple valid interpretations | 3+ |
| Recent/updated | Test freshness | 2+ |
For each query, document the expected answer and the source chunk(s).
Phase 3: Evaluate Retrieval
For each test query, measure:
- Precision@K — Of the K retrieved chunks, how many are relevant?
- Recall@K — Of all relevant chunks in the corpus, how many were retrieved?
- MRR (Mean Reciprocal Rank) — How high is the first relevant chunk ranked?
- Chunk relevance — Score each retrieved chunk: Relevant, Partially Relevant, Irrelevant.
Phase 4: Evaluate Generation
For each test query with retrieved context:
- Groundedness — Is every claim in the response supported by the retrieved context? Score: 0 (hallucinated) to 1 (fully grounded).
- Completeness — Does the response use all relevant information from the context? Score: 0 (ignored context) to 1 (complete).
- Hallucination detection — Identify specific claims not supported by context.
- Abstention — For unanswerable queries, does the model correctly say "I don't know"?
Phase 5: Diagnose Failures
For every incorrect or low-quality response, classify the root cause:
| Failure Type | Diagnosis | Indicator |
|---|---|---|
| Retrieval failure | Relevant chunks not retrieved | Low Recall@K |
| Ranking failure | Relevant chunk retrieved but ranked low | Low MRR, high Recall |
| Chunk boundary issue | Answer split across chunk boundaries | Partial matches in multiple chunks |
| Embedding mismatch | Query semantics don't match chunk embeddings | Relevant chunk has low similarity score |
| Generation failure | Correct context but wrong answer | High retrieval scores, low groundedness |
| Hallucination | Model invents facts not in context | Claims not traceable to any chunk |
| Over-abstention | Model refuses to answer when context is sufficient | Unanswered with relevant context present |
Show full SKILL.md (263 more words)Show less
Phase 6: Recommendations
Based on failure analysis, recommend specific improvements:
| Failure Pattern | Recommendation |
|---|---|
| Chunk boundary issues | Increase overlap, try semantic chunking |
| Low Precision@K | Reduce K, add reranking stage |
| Low Recall@K | Increase K, try hybrid search |
| Embedding mismatch | Try different embedding model, add query expansion |
| Hallucination | Strengthen grounding instruction in prompt, reduce temperature |
| Over-abstention | Soften abstention criteria in prompt |
Output Format
## RAG Audit Report
### Pipeline Configuration
| Component | Value |
|-----------|-------|
| Documents | {N} ({format}) |
| Chunking | {strategy}, {size} tokens, {overlap}% overlap |
| Embedding | {model} ({dimensions}d) |
| Retrieval | {method}, K={N} |
| Generation | {model}, temperature={T} |
### Evaluation Dataset
- **Total queries:** {N}
- **Known-answer:** {N}
- **Multi-hop:** {N}
- **Unanswerable:** {N}
### Retrieval Quality
| Metric | Score | Target | Status |
|--------|-------|--------|--------|
| Precision@{K} | {score} | {target} | {Pass/Fail} |
| Recall@{K} | {score} | {target} | {Pass/Fail} |
| MRR | {score} | {target} | {Pass/Fail} |
### Generation Quality
| Metric | Score | Target | Status |
|--------|-------|--------|--------|
| Groundedness | {score} | {target} | {Pass/Fail} |
| Completeness | {score} | {target} | {Pass/Fail} |
| Hallucination rate | {score} | {target} | {Pass/Fail} |
| Abstention accuracy | {score} | {target} | {Pass/Fail} |
### Failure Analysis
| # | Query | Failure Type | Root Cause | Recommendation |
|---|-------|-------------|------------|----------------|
| 1 | {query} | {type} | {cause} | {fix} |
### Recommendations (Priority Order)
1. **{Recommendation}** — addresses {N} failures, expected impact: {description}
2. **{Recommendation}** — addresses {N} failures, expected impact: {description}
### Sample Failures
#### Query: "{query}"
- **Expected:** {answer}
- **Retrieved chunks:** {chunk summaries with relevance scores}
- **Generated:** {response}
- **Issue:** {diagnosis}Calibration Rules
- Component isolation. Evaluate retrieval and generation independently. A great retriever with a bad generator looks like retrieval failure if you only check end output.
- Known answers first. Start with factoid questions where the correct answer is unambiguous. Multi-hop and ambiguous queries are harder to evaluate.
- Quantify, don't qualify. "Retrieval is bad" is not a finding. "Precision@5 is 0.3 (target: 0.8) with 70% of failures due to chunk boundary splits" is actionable.
- Sample failures deeply. Aggregate metrics identify WHERE the problem is. Individual failure analysis identifies WHY.
Error Handling
| Problem | Resolution |
|---|---|
| No known-answer queries available | Help design them from the document corpus. Pick 10 facts and formulate questions. |
| Pipeline access not available | Work from recorded inputs/outputs. Post-hoc evaluation is possible with query-context-response triples. |
| Corpus is too large to review | Sample-based evaluation. Select representative documents and generate queries from them. |
| Multiple failure types co-exist | Address retrieval failures first. Generation quality cannot exceed retrieval quality. |
When NOT to Audit
Push back if:
- The pipeline hasn't been built yet — design it first, audit after
- The corpus has fewer than 10 documents — too small for meaningful retrieval evaluation
- The user wants to compare embedding models — that's a benchmark task, not an audit
© Mathews-Tom, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (references) in skills/rag-auditor of Mathews-Tom/armory.
- SKILL.md
- evals/cases.yaml
- references/diagnostic-queries.md
- references/failure-taxonomy.md
- references/generation-metrics.md
- references/retrieval-metrics.md
Open the folder on GitHubat commit 4594fb7
Compare with similar skills
RAG Auditor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| RAG Auditor this skillMathews-Tom/armory | 328 | — | ~2.4k | Automated safety check: Pass | MIT | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit | 260 | 3 repos | ~1.4k | Automated safety check: Pass | Custom licence | |
| MCP Local RAGshinpr/mcp-local-rag | 411 | — | ~4.4k | Automated safety check: Pass | MIT | |
| Ms Agent Framework RAGshuyu-labs/WebCode | 278 | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Local RAG Searchnkapila6/mcp-local-rag | 134 | 1 repos | ~1.6k | Automated safety check: Pass | MIT |
Similar skills
Senior Prompt Engineer
maslennikov-ig/claude-code-orchestrator-kit
Provides reference guides and Python scripts for prompt optimization, RAG evaluation, and agent orchestration when building or tuning LLM systems.
MCP Local RAG
shinpr/mcp-local-rag
Searches, saves, and maintains a local document index through a local RAG MCP server.
Ms Agent Framework RAG
shuyu-labs/WebCode
Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.
Local RAG Search
nkapila6/mcp-local-rag
Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.
Blockify Integration
iternal-technologies-partners/blockify-agentic-data-optimization
Process documents with Blockify API to create optimized IdeaBlocks for RAG.
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Categories
Questions about RAG Auditor
What does RAG Auditor do?
Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate). RAG Auditor is an agent skill from Mathews-Tom/armory. Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate).
When should I use RAG Auditor?
RAG Auditor fits situations like: : audit RAG pipeline; hallucination detection; why is RAG failing; grounding check.
How do I install RAG Auditor in Claude Code?
Run `npx skills add Mathews-Tom/armory --skill rag-auditor -a claude-code`. Or copy the skill folder (skills/rag-auditor in Mathews-Tom/armory) into .claude/skills/rag-auditor in your project. Claude Code loads it when a task matches its description.
How do I install RAG Auditor in Codex?
Run `npx skills add Mathews-Tom/armory --skill rag-auditor -a codex`. Or copy the skill folder (skills/rag-auditor in Mathews-Tom/armory) into .agents/skills/rag-auditor in your project. Codex loads it when a task matches its description.
Can I use RAG Auditor in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Mathews-Tom/armory --skill rag-auditor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rag-auditor, .gemini/skills/rag-auditor, .github/skills/rag-auditor and .opencode/skills/rag-auditor in your project.
What does RAG Auditor need to run?
SKILL.md names no scripts, command-line tools or credentials: RAG Auditor is instructions for the agent only.
Does RAG Auditor access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is RAG Auditor safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does RAG Auditor use?
RAG Auditor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does RAG Auditor use?
About 2.4k tokens (SKILL.md is roughly 9.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.2k tokens, read only when the agent opens those files.
What are the alternatives to RAG Auditor?
Skills that share tags, products or a category with RAG Auditor: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), MCP Local RAG (shinpr/mcp-local-rag, 411 stars) and Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains RAG Auditor?
Mathews-Tom (a GitHub user) maintains it in Mathews-Tom/armory, which has 328 GitHub stars. The repository holds 80 skills in this directory. The repository was last updated on October 6, 2026.
Source: Mathews-Tom/armory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.