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 selvarajmurugesan90/ops-engineering-skills --skill rag-pipeline-designgit clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-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/selvarajmurugesan90/ops-engineering-skills/rag-pipeline-design)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/rag-pipeline-design"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/rag-pipeline-design/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/selvarajmurugesan90/ops-engineering-skills/rag-pipeline-design"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/rag-pipeline-design.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 | $0.00105 | $0.02807 |
| Opus 5 | $0.00053 | $0.01404 |
| Sonnet 5 | $0.00021 | $0.00561 |
| Haiku 4.5 | $0.00011 | $0.00281 |
Grade A, and why
rag-pipeline-design 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Pipeline Design
Purpose
Retrieval-augmented generation grounds an LLM's output in specific, retrievable content — internal documentation, a codebase, a knowledge base — rather than relying solely on the model's training data, which is untraceable, can be stale, and cannot contain private or proprietary information. A RAG pipeline has real design surface at every stage (chunking, embedding, indexing, retrieval, re-ranking, and how retrieved content is presented to the model), and weaknesses at any stage show up as the same symptom to an end user — a wrong or missing answer — even though the root cause and fix differ entirely by stage. This skill covers the full pipeline and, critically, the fact that retrieved content is untrusted input to the model just like any other tool output, not a safe substitute for user-supplied instructions.
When to use
- Building a new pipeline to ground agent answers in internal documents, a codebase, tickets, or any private corpus.
- The agent gives confident but wrong answers about content that exists in your knowledge base ("hallucinates facts it should know").
- Retrieval returns technically related but unhelpful chunks for a significant fraction of queries ("relevance drift"), producing weak answers.
- Deciding chunk size, overlap, or embedding model choice for a new corpus.
- Documents in the retrieval corpus are user-editable or come from an external/untrusted source, and you need to reason about injection risk.
- Debugging why retrieval quality degraded after adding new documents to the index.
Prerequisites & environment
- An embedding model and a vector index/database (managed service or self-hosted); exact choice affects latency and cost but not the design principles below.
- A document ingestion pipeline that can re-run on a schedule or on document change (stale indexes are a common, avoidable failure mode).
- A way to evaluate retrieval quality independent of end-to-end answer quality — at minimum a labeled set of (query, expected source document) pairs (see agent-evaluation-and-guardrails).
- Clarity on the trust level of the corpus: fully internal and access-controlled vs. containing user-submitted or external content that could carry adversarial text.
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 · 267 lines · 105 tokens per session scan A 080d93e313c0
rag-pipeline-design is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (38 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 105 tokens to every session and 2,807 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 2026-08-30.
Other skills, from other repositories
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Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
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azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
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similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.