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 alivirgo/Major-AI-Skills --skill rag-retrieval-auditgit clone --depth 1 https://github.com/alivirgo/Major-AI-SkillsWrote 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/alivirgo/major-ai-skills/rag-retrieval-audit)<a href="https://agentmods.dev/skills/alivirgo/major-ai-skills/rag-retrieval-audit"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/rag-retrieval-audit/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/alivirgo/major-ai-skills/rag-retrieval-audit"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/rag-retrieval-audit.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00030 | $0.00320 |
| Opus 5 | $0.00015 | $0.00160 |
| Sonnet 5 | $0.00006 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
rag-retrieval-audit 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 today.
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.
What it actually says
RAG Retrieval Audit
Scope
Obtain a bounded query set, reference documents, index revision, filters, and access-control rules. Inspect the existing retriever before changing embeddings or chunking. Keep experiments in a test index unless production changes are authorized.
Procedure
For each query, record eligible relevant document IDs and retrieved chunk IDs, ranks, scores, filters, and source offsets. Separate ingestion omissions, permission filtering, chunk boundary problems, ranking failures, and generation failures.
Checks
Calculate recall at the application's retrieval cutoff only where relevance labels exist. Inspect zero-result queries and relevant documents excluded by filters. Do not compare raw similarity scores between different embedding models as if calibrated.
Failure Handling
Change one variable at a time, preserving the baseline. Test whether the answer-bearing passage survives chunking and appears in the final model context. Report retrieval metrics separately from answer quality, latency, and cost.
Deliverable
Deliver a per-query failure table, reproducible configuration, and before/after evidence. Exclude inaccessible documents from the relevance denominator rather than recommending an authorization bypass.
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.
- today First seen · 35 lines · 30 tokens per session scan A c7da20e526d9
rag-retrieval-audit is a skill published in the GitHub repository alivirgo/Major-AI-Skills (1 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 320 once invoked, about $0.0002 per session on Opus 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 2026-09-12.
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