How do I install the Execution RAG Pipeline skill?

Run npx skillmds@latest add deankelly751/execution-rag-pipeline in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Execution RAG Pipeline safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Execution RAG Pipeline?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Execution RAG Pipeline free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Execution RAG Pipeline?

DeanKelly751 (@deankelly751) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Execution RAG Pipeline by deankelly751

Name: Execution RAG Pipeline
Author: DeanKelly751

Execution RAG Pipeline

Build a retrieval-augmented generation pipeline. Step-by-step procedure covering document ingestion, chunking, embedding, retrieval, and generation with citations. Use when implementing RAG systems, improving retrieval quality, or designing knowledge-augmented AI features.

DeanKelly751 Updated Sep 21, 2026

File contents

RAG Pipeline

Purpose

Step-by-step procedure for building effective retrieval-augmented generation systems.

Workflow

Step 1: Document Ingestion

Identify source documents (docs, code, data)
Extract text (handle PDF, HTML, markdown)
Preserve metadata (source, date, section, author)
Handle updates (incremental vs full re-index)

Step 2: Chunking Strategy

Strategy	Best for	Chunk size
Fixed size	Simple documents	512-1024 tokens
Semantic (paragraph/section)	Structured docs	Variable
Sliding window with overlap	Dense technical content	512 tokens, 50-100 overlap
Recursive (split by hierarchy)	Documents with clear structure	Variable

Key: Chunks should be self-contained enough to be useful without surrounding context.

Step 3: Embedding

Choose embedding model appropriate for domain
Include metadata in embedding (title + content often works better than content alone)
Store embeddings in vector database
Track embedding model version (re-embed if model changes)

Step 4: Retrieval

Technique	When
Vector similarity (k-NN)	Default semantic search
Hybrid (vector + keyword)	When exact terms matter
Re-ranking	Quality-critical, can afford latency
Multi-query	Ambiguous queries (rephrase and merge results)

Retrieve more than you need, then filter/re-rank.

Step 5: Generation with Context

Inject retrieved chunks into prompt
Instruct model to cite sources
Include "if the context doesn't answer, say so"
Format citations for traceability

Step 6: Evaluate

Metric	Measures	How
Retrieval precision	Right docs found	Manual review of top-k
Answer faithfulness	Answer matches sources	Compare claims to chunks
Answer relevance	Answer addresses query	Human evaluation

Checklist

Chunking preserves meaningful context
Metadata stored with each chunk
Retrieval tested with representative queries
Generation cites sources
Graceful handling of "no relevant context found"
Evaluation pipeline for quality monitoring

Related Skills

ai-workflows/knowledge-agent-design — RAG as agent knowledge source
ai-workflows/knowledge-guardrails — validation of RAG outputs
ai-workflows/knowledge-prompt-engineering — prompting with retrieved context

---name: execution-rag-pipelinedescription: Build a retrieval-augmented generation pipeline. Step-by-step procedure covering document ingestion, chunking, embedding, retrieval, and generation with citations. Use when implementing RAG systems, improving retrieval quality, or designing knowledge-augmented AI features.---# RAG Pipeline## PurposeStep-by-step procedure for building effective retrieval-augmented generation systems.## Workflow### Step 1: Document Ingestion- Identify source documents (docs, code, data)- Extract text (handle PDF, HTML, markdown)- Preserve metadata (source, date, section, author)- Handle updates (incremental vs full re-index)### Step 2: Chunking Strategy| Strategy | Best for | Chunk size ||----------|----------|------------|| Fixed size | Simple documents | 512-1024 tokens || Semantic (paragraph/section) | Structured docs | Variable || Sliding window with overlap | Dense technical content | 512 tokens, 50-100 overlap || Recursive (split by hierarchy) | Documents with clear structure | Variable |**Key**: Chunks should be self-contained enough to be useful without surrounding context.### Step 3: Embedding- Choose embedding model appropriate for domain- Include metadata in embedding (title + content often works better than content alone)- Store embeddings in vector database- Track embedding model version (re-embed if model changes)### Step 4: Retrieval| Technique | When ||-----------|------|| Vector similarity (k-NN) | Default semantic search || Hybrid (vector + keyword) | When exact terms matter || Re-ranking | Quality-critical, can afford latency || Multi-query | Ambiguous queries (rephrase and merge results) |Retrieve more than you need, then filter/re-rank.### Step 5: Generation with Context- Inject retrieved chunks into prompt- Instruct model to cite sources- Include "if the context doesn't answer, say so"- Format citations for traceability### Step 6: Evaluate| Metric | Measures | How ||--------|----------|-----|| Retrieval precision | Right docs found | Manual review of top-k || Answer faithfulness | Answer matches sources | Compare claims to chunks || Answer relevance | Answer addresses query | Human evaluation |## Checklist- [ ] Chunking preserves meaningful context- [ ] Metadata stored with each chunk- [ ] Retrieval tested with representative queries- [ ] Generation cites sources- [ ] Graceful handling of "no relevant context found"- [ ] Evaluation pipeline for quality monitoring## Related Skills- `ai-workflows/knowledge-agent-design` — RAG as agent knowledge source- `ai-workflows/knowledge-guardrails` — validation of RAG outputs- `ai-workflows/knowledge-prompt-engineering` — prompting with retrieved context

Frequently asked questions