# chunking-strategy

Configures document chunking workflows and overlap for RAG pipelines and vector search. It validates semantic coherence and tunes retrieval precision.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3800 installs · ★ 363
- Author: giuseppe-trisciuoglio
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-ai/skills/chunking-strategy/SKILL.md
- Updated: 2026-09-10
- HTML: https://affpapa.org/en/nevibecode/skills/chunking-strategy

## What it does

The skill organizes document splitting into five tiers, moving from fixed-size chunks to semantic boundary detection. Simple texts start around 512 tokens with 10–20% overlap. Factoid queries run better at 256 tokens. Analytical tasks require up to 1024 tokens. For Python code, Markdown, and tables, structure-aware splitting preserves logical units like functions or classes using AST parsing.

The workflow enforces automated checks against retrieval metrics. If retrieval precision drops below 0.7, chunk size is reduced by 25%. If recall falls below 0.6, overlap increases by 10%. Running semantic coherence checks relies on `sentence-transformers` and models like `all-MiniLM-L6-v2`. Semantic splits introduce heavy computational overhead. That extra load increases latency during batch processing.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill chunking-strategy -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/chunking-strategy/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/chunking-strategy/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill chunking-strategy -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/chunking-strategy/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill chunking-strategy -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/chunking-strategy/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install giuseppe-trisciuoglio/developer-kit chunking-strategy
```

2. **Manually: put the files in place**
- File: `.github/skills/chunking-strategy/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill chunking-strategy -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/giuseppe-trisciuoglio/developer-kit --path plugins/developer-kit-ai/skills/chunking-strategy
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/chunking-strategy/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill chunking-strategy -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-ai/skills/chunking-strategy/SKILL.md

## When to use it
- configure token chunk sizes and overlap ratios for vector databases
- split Python code along function and class boundaries via AST
- evaluate retrieval precision and recall metrics after splitting
- set up semantic boundary detection based on cosine similarity thresholds

## Example prompts
- Recommend a chunking strategy for technical Markdown docs and Python files
- Check semantic coherence for my chunks using sentence-transformers
- How should I adjust chunk overlap if my RAG recall is below 0.6?

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: d0b87c6a2710f44a.

BEGIN THIRD-PARTY FILE d0b87c6a2710f44a
```text
---
name: chunking-strategy
description: Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
allowed-tools: Read, Write, Bash
---

# Chunking Strategy for RAG Systems

## Overview

Provides chunking strategies for RAG systems, vector databases, and document processing. Recommends chunk sizes, overlap percentages, and boundary detection methods; validates semantic coherence; evaluates retrieval metrics.

## When to Use

Use when building or optimizing RAG systems, vector search pipelines, document chunking workflows, or performance-tuning existing systems with poor retrieval quality.

## Instructions

### Choose Chunking Strategy

Select based on document type and use case:

1. **Fixed-Size Chunking** (Level 1)
   - Use for simple documents without clear structure
   - Start with 512 tokens and 10-20% overlap
   - Adjust: 256 for factoid queries, 1024 for analytical

2. **Recursive Character Chunking** (Level 2)
   - Use for documents with structural boundaries
   - Hierarchical separators: paragraphs → sentences → words
   - Customize for document types (HTML, Markdown, JSON)

3. **Structure-Aware Chunking** (Level 3)
   - Use for structured content (Markdown, code, tables, PDFs)
   - Preserve semantic units: functions, sections, table blocks
   - Validate structure preservation post-split

4. **Semantic Chunking** (Level 4)
   - Use for complex documents with thematic shifts
   - Embedding-based boundary detection with 0.8 similarity threshold
   - Buffer size: 3-5 sentences

5. **Advanced Methods** (Level 5)
   - Late Chunking for long-context models
   - Contextual Retrieval for high-precision requirements
   - Monitor computational cost vs. retrieval gain

Reference: [references/strategies.md](references/strategies.md).

### Implement Chunking Pipeline

1. **Pre-process documents**
   - Analyze structure, content types, information density
   - Identify multi-modal content (tables, images, code)

2. **Select parameters**
   - Chunk size: embedding model context window / 4
   - Overlap: 10-20% for most cases
   - Strategy-specific settings

3. **Process and validate**
   - Apply chunking strategy
   - Validate coherence: run `evaluate_chunks.py --coherence` (see below)
   - Test with representative documents

4. **Evaluate and iterate**
   - Measure precision and recall
```
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