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 skills add Mathews-Tom/armory --skill rag-auditorgit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/skills/mathews-tom/armory/rag-auditor)<a href="https://agentmods.dev/skills/mathews-tom/armory/rag-auditor"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/rag-auditor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mathews-tom/armory/rag-auditor"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/rag-auditor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 177 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00076 | $0.01966 |
| Opus 5.5 | $0.00030 | $0.00786 |
| Sonnet 5.5 | $0.00015 | $0.00393 |
| Haiku 4.5 | $0.00008 | $0.00197 |
Grade A, and why
rag-auditor 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 1mo 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 191 lines · 76 tokens per session scan A 633f6f5730a2
rag-auditor is a skill published in the GitHub repository Mathews-Tom/armory (337 stars, last pushed 2d ago), licensed MIT. It adds 76 tokens to every session and 1,966 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-05.
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