Borrowing it
Nothing to install: this file belongs to TakaGoto/rag-learning-academy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TakaGoto/rag-learning-academy/main/.claude/skills/evaluate/SKILL.mdgit clone --depth 1 https://github.com/TakaGoto/rag-learning-academyWrote 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/takagoto/rag-learning-academy/evaluate)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/evaluate"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/evaluate.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 | $0.00009 | $0.01013 |
| Opus 5 | $0.00005 | $0.00507 |
| Sonnet 5 | $0.00002 | $0.00203 |
| Haiku 4.5 | $0.00001 | $0.00101 |
Grade A, and why
evaluate 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 7d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate: Measure Your RAG Pipeline's Quality
Scope: This skill focuses on answer quality metrics (faithfulness, relevancy, correctness) against a labeled test set. For operational performance metrics (latency, throughput, cost), use
/benchmark.
Run a structured evaluation of the learner's RAG pipeline using established metrics. This skill teaches evaluation methodology while generating actionable results.
Step 1: Identify the Pipeline
Welcome! Let's see how your RAG pipeline is performing.
First, check whether the learner has existing work to evaluate:
-
Look for a learner profile at
progress/learner-profile.mdand for code insrc/andprojects/. -
If pipeline code exists in
projects/, great — proceed. If multiple pipelines exist, ask which one to evaluate. -
If no pipeline or RAG code exists anywhere in
projects/orsrc/, guide them warmly:"It looks like you haven't built a pipeline yet — that's totally fine! Let's get you set up first. Run
/buildto create your first RAG pipeline, and then come back here to see how it scores. It only takes a few minutes to get something running!"Stop here — do not continue to Step 2.
-
If a pipeline is found, verify it has the minimum components: a retriever and a generator.
Step 2: Explain the Evaluation Framework
Before running metrics, teach the learner what they are measuring and why:
Core RAG Metrics
- Faithfulness: Does the generated answer stick to the retrieved context? (measures hallucination)
- Answer Relevancy: Is the answer actually relevant to the question asked?
- Context Precision: Are the retrieved documents relevant to the question?
- Context Recall: Did the retriever find all the relevant information?
Additional Metrics (if applicable)
- Answer Correctness: How close is the answer to a ground truth answer?
- Latency: How long does each pipeline stage take?
- Token Usage: How many tokens are consumed per query?
Explain each metric with a simple analogy so the learner builds intuition.
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.
- 7d ago First seen · 105 lines · 9 tokens per session scan A 570ced70af39
evaluate is a skill published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 9 tokens to every session and 1,013 once invoked, about $0.0000 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.
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