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/benchmark/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/benchmark)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/benchmark"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/benchmark.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 | $0.01118 |
| Opus 5 | $0.00005 | $0.00559 |
| Sonnet 5 | $0.00002 | $0.00224 |
| Haiku 4.5 | $0.00001 | $0.00112 |
Grade A, and why
benchmark 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark: Measure Your RAG Pipeline's Performance
Scope: This skill focuses on operational performance (latency, throughput, token costs, memory). For answer quality metrics (faithfulness, relevancy, correctness), use
/evaluate.
Set up and run comprehensive benchmarks on the learner's RAG pipeline to identify bottlenecks and establish performance baselines.
Step 1: Identify the Pipeline
Welcome! Let's measure how your RAG pipeline performs under the hood.
First, check whether the learner has existing work to benchmark:
-
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 to benchmark. -
If no pipeline or RAG code exists anywhere in
projects/orsrc/, guide them warmly:"Looks like you don't have a pipeline to benchmark yet — no worries! Let's get you set up first. Run
/buildto create your first RAG pipeline, and once it's running you can come back here to see exactly where the time and tokens go. It won't take long!"Stop here — do not continue to Step 2.
-
If a pipeline is found, catalog the pipeline components: loader, chunker, embedder, vector store, retriever, generator.
Step 2: Define Benchmark Scope
Ask the learner what they want to measure, or suggest a comprehensive benchmark covering:
Latency Metrics
- End-to-end latency: Total time from query to answer
- Retrieval latency: Time to embed the query and fetch results from the vector store
- Generation latency: Time for the LLM to produce the answer
- Embedding latency: Time to embed a single query or a batch of documents
Throughput Metrics
- Queries per second: How many queries can the pipeline handle?
- Indexing throughput: How fast can documents be chunked, embedded, and stored?
Quality Metrics
- Retrieval accuracy: Precision@k, Recall@k, MRR (Mean Reciprocal Rank)
- Answer quality: Faithfulness, relevancy (via RAGAS or custom evaluators)
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 · 123 lines · 9 tokens per session scan A db216767c207
benchmark 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 1,118 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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