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 agentmods add skills/chandrudp29/skillhub/rag-evaluatornpx skills add chandrudp29/skillhub --skill rag-evaluatorgit clone --depth 1 https://github.com/chandrudp29/skillhubWrote 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/chandrudp29/skillhub/rag-evaluator)<a href="https://agentmods.dev/skills/chandrudp29/skillhub/rag-evaluator"><img src="https://agentmods.dev/badge/skills/chandrudp29/skillhub/rag-evaluator.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.01167 |
| Opus 5 | $0.00022 | $0.00583 |
| Sonnet 5 | $0.00009 | $0.00233 |
| Haiku 4.5 | $0.00004 | $0.00117 |
Grade A, and why
rag-evaluator 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 6d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Evaluator
A systematic evaluation framework for Retrieval-Augmented Generation pipelines. Finds where your RAG breaks before your users do.
When to Use
- "Is my RAG system giving correct answers?"
- "My RAG is hallucinating — help me find where"
- "Evaluate my retrieval quality"
- "My RAG answers are vague / not using the documents"
- Before deploying a RAG system to production
The Three Failure Modes
Retrieval failure: Right question, wrong chunks retrieved. The answer exists in the corpus but wasn't found.
Generation failure: Right chunks retrieved, but the LLM ignored them, hallucinated, or answered a different question.
Context failure: Chunks retrieved but they're too long, poorly formatted, or overlap confusingly — LLM gets lost.
Most RAG problems are retrieval failures. Check retrieval first.
Evaluation Workflow
Step 1 — Build a test set
Minimum 20 question-answer pairs. Include:
- Questions whose answers are clearly in the corpus (should always pass)
- Questions whose answers are NOT in the corpus (should return "I don't know")
- Multi-hop questions requiring synthesis across chunks
- Edge cases: short answers, numerical facts, recent events
Without a test set, you're evaluating by vibes. Build it first.
Step 2 — Evaluate retrieval independently
For each test question:
- Run retrieval only (no generation)
- Check: is the answer in the top-k chunks?
- Check: is the most relevant chunk ranked #1?
Compute:
- Recall@k: % of questions where answer chunk is in top-k
- MRR (Mean Reciprocal Rank): average of 1/rank of first relevant chunk
Target: Recall@5 > 0.85 before touching generation.
Step 3 — Evaluate generation independently
Feed the oracle chunks (the correct ones) directly to the LLM. Ask it to answer using only those chunks. If it still fails, the problem is generation, not retrieval.
Step 4 — Run the full pipeline evaluation
For each question in test set:
Faithfulness — Does the answer only use information from the retrieved chunks?
- LLM-as-judge: "Given these chunks and this answer, does the answer contain any claims not supported by the chunks? Yes/No and cite specific claims."
- Target: > 0.90
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.
- 6d ago First seen · 124 lines · 44 tokens per session scan A 81183f91cc28
rag-evaluator is a skill published in the GitHub repository chandrudp29/skillhub (13 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 1,167 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 2026-08-30.
Other skills, from other repositories
rag-eval
NVIDIA RAG Blueprint evaluation guidance for measuring retrieval and answer quality with stable datasets, baselines, and reproducible scoring workflows.
rag-evaluation
Use this skill to evaluate the quality of a RAG pipeline on faithfulness, answer relevancy, context precision, context recall, and hallucination rate. Activates after a RAG system is implemented or when retrieval quality is in question. Produces a structured evaluation report with measurable results.
generate-rag-dataset
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
molecular-rag
Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).
golden-dataset
Golden dataset lifecycle patterns for curation, versioning, quality validation, and CI integration. Use when building evaluation datasets, managing dataset versions, validating quality scores, or integrating golden tests into pipelines.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.