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 glebis/claude-skills --skill rag-evalgit clone --depth 1 https://github.com/glebis/claude-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/glebis/claude-skills/rag-eval)<a href="https://agentmods.dev/skills/glebis/claude-skills/rag-eval"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/rag-eval/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/glebis/claude-skills/rag-eval"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/rag-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00106 | $0.01457 |
| Opus 5 | $0.00053 | $0.00728 |
| Sonnet 5 | $0.00021 | $0.00291 |
| Haiku 4.5 | $0.00011 | $0.00146 |
Grade A, and why
rag-eval 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 8d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rag-eval
Purpose
Replace the "tweak → squint → swap model → burn credits" loop with a single command that runs a grid of eval variants on the user's gold-set, ranks them by a cost-aware score, and returns structured feedback on architecture, stack, and likely-issues. Draws on evidence-based RAG practices and learns from the user's past runs.
When to use
Trigger on: "help me test a RAG", "tune my RAG", "my RAG is bad", "compare retrieval prompts", "how do I eval this", "what's the best embedding model for X", "my RAG eval is expensive". Also trigger when the user reports burning OpenRouter / OpenAI credits with no clear signal of improvement.
Prerequisites — gather before running
Collect these from the user before the first sweep. Many are optional with sensible defaults; always confirm the ones that gate cost.
- RAG codebase root — path to the repo/module under test.
- Gold-set — at least 10 Q&A pairs. If missing, offer to generate a starter gold-set from the user's dataset (LLM-synthesized, human-reviewed). See
references/best-practices.md. - Dataset — the corpus the RAG retrieves over.
- Budget cap — hard dollar limit per run (default: $2 if user doesn't specify). Always confirm before any sweep.
- Provider keys —
OPENROUTER_API_KEYorOPENAI_API_KEY(read from env). - Vector-store config — collection name, embedding model, chunk size (read from repo; confirm if ambiguous).
- Eval history path (optional) — defaults to
.rag-eval/history.jsonlin the repo root.
Workflow
Follow this order. Refer to references/best-practices.md for the canonical checklist and references/evidence-base.md for the research-backed defaults.
Step 0 — (Optional) Ingest a prior iteration session
When the user provides a session ID (Claude Code transcript, skill-studio session, or a Fathom meeting), run the deterministic ingest first — no LLM calls. This extracts only the useful signals (models tried, prompt variants, cost events, eval results) as compact JSON, so the rest of the skill works off a tiny structured bundle instead of a long raw transcript.
What ships with it
1 file 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.
- 8d ago First seen · 96 lines · 106 tokens per session scan A 7374ab664deb
rag-eval is a skill published in the GitHub repository glebis/claude-skills (375 stars, last pushed 10d ago), licensed MIT. It adds 106 tokens to every session and 1,457 once invoked, about $0.0005 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 2026-09-03.
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