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 jnPiyush/AgentX --skill rag-pipelinesgit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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/jnpiyush/agentx/rag-pipelines)<a href="https://agentmods.dev/skills/jnpiyush/agentx/rag-pipelines"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/rag-pipelines/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/jnpiyush/agentx/rag-pipelines"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/rag-pipelines.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 | $0.00046 | $0.02759 |
| Opus 5.5 | $0.00018 | $0.01104 |
| Sonnet 5.5 | $0.00009 | $0.00552 |
| Haiku 4.5 | $0.00005 | $0.00276 |
Grade A, and why
rag-pipelines 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 1mo 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 — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Pipelines
Purpose: Build production-grade Retrieval-Augmented Generation systems that ground LLM responses in authoritative knowledge.
When to Use This Skill
- Building document Q&A systems grounded in enterprise knowledge
- Implementing semantic search over unstructured documents
- Designing chunking strategies for different document types
- Selecting and configuring vector databases for retrieval
- Implementing hybrid search (keyword + semantic) and reranking
- Optimizing retrieval quality (precision, recall, faithfulness)
Prerequisites
- Document corpus to index
- Embedding model access (OpenAI, Azure OpenAI, or open-source)
- Vector store or search service
- LLM for generation
Decision Tree
Building a RAG system?
+- What type of documents?
| +- Short, structured (FAQ, KB articles)?
| -> Small chunks (256-512 tokens), simple splitting
| +- Long, unstructured (reports, papers)?
| -> Semantic chunking, hierarchical retrieval
| +- Code repositories?
| -> AST-aware chunking, function-level
| +- Multi-modal (PDFs with tables/images)?
| -> Document intelligence + specialized parsers
+- What query patterns?
| +- Exact lookups? -> Keyword search (BM25)
| +- Semantic similarity? -> Vector search
| +- Both? -> Hybrid search (recommended default)
+- Need high precision?
| +- Add reranking (cross-encoder or Cohere Rerank)
| +- Add metadata filtering (date, source, category)
+- Scale considerations?
+- <100K docs -> Single vector store, simple pipeline
+- 100K-10M docs -> Hybrid search + reranking + caching
+- >10M docs -> Distributed index, tiered retrieval, pre-filtering
RAG Architecture
Standard Pipeline
[Documents] -> [Parser/Loader] -> [Chunker] -> [Embedder] -> [Vector Store]
|
[User Query] -> [Embedder] -> [Retriever] -> [Reranker] -> [Top-K Contexts]
|
[Prompt Builder] <- [System Prompt + Template]
|
[LLM Generation]
|
[Response + Citations]
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.
- 1mo ago Changed c1d080a58f61
- 1mo ago First seen · 304 lines · 46 tokens per session scan A f8054efc76f8
rag-pipelines is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 46 tokens to every session and 2,759 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
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.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.