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 nimadorostkar/Claude-Skills-collection --skill raggit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/rag)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/rag"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/rag/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/nimadorostkar/claude-skills-collection/rag"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/rag.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 | $0.00042 | $0.01449 |
| Opus 5 | $0.00021 | $0.00724 |
| Sonnet 5 | $0.00008 | $0.00290 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
rag 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 12d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retrieval-Augmented Generation
Purpose
Build a RAG system whose answers are grounded in retrieved evidence, and be able to tell — when an answer is wrong — whether the retriever failed to find the right document or the generator failed to use it.
When to Use
- Building question answering over a document corpus.
- A RAG system that returns confident, wrong answers.
- Choosing chunking, embedding, and retrieval strategy.
- Adding citation and grounding to a generative feature.
Capabilities
- Chunking strategies and their trade-offs.
- Embedding selection and hybrid (dense + sparse) retrieval.
- Reranking and query rewriting.
- Grounding, citation, and refusal when evidence is absent.
- Component-wise evaluation.
Inputs
- The corpus: its size, structure, and update frequency.
- The query patterns: factual lookup, comparison, summarization, multi-hop.
- Accuracy requirements and the cost of a wrong answer.
Outputs
- A retrieval pipeline with a measured recall figure.
- Answers with citations to the retrieved passages.
- Separate evaluation of retrieval quality and generation quality.
Workflow
- Evaluate retrieval separately — Before touching the prompt. If the correct passage is not in the top-k, no amount of prompt engineering will produce a correct answer. Measure recall@k first.
- Chunk on semantic boundaries — Sections, paragraphs, or logical units. Fixed-size chunking splits a table in half and produces two useless chunks. Add overlap so a fact spanning a boundary is not lost.
- Retrieve hybrid — Dense embeddings find semantic matches; BM25 finds exact terms, product codes, and names. Neither alone is sufficient; the combination materially outperforms both.
- Rerank the candidates — Retrieve 50, rerank with a cross-encoder, pass the top 5. Reranking is the single highest-value addition to a naive RAG pipeline.
- Ground the generation — Instruct the model to answer only from the provided context, to cite the passage for each claim, and to say it does not know when the context does not contain the answer.
- Diagnose failures by component — For each wrong answer: was the right passage retrieved? If no, it is a retrieval problem. If yes, it is a generation problem. These have completely different fixes.
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.
- 12d ago First seen · 131 lines · 42 tokens per session scan A 1951b5c8b310
rag is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 24d ago), licensed MIT. It adds 42 tokens to every session and 1,449 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.
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