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 giuseppe-trisciuoglio/developer-kit --skill chunking-strategygit clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kitWrote 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/giuseppe-trisciuoglio/developer-kit/chunking-strategy)<a href="https://agentmods.dev/skills/giuseppe-trisciuoglio/developer-kit/chunking-strategy"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/chunking-strategy/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/giuseppe-trisciuoglio/developer-kit/chunking-strategy"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/chunking-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk pass
- 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 | $0.00069 | $0.01469 |
| Opus 5.5 | $0.00028 | $0.00588 |
| Sonnet 5.5 | $0.00014 | $0.00294 |
| Haiku 4.5 | $0.00007 | $0.00147 |
Grade A, and why
chunking-strategy 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
-
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
-
Recursive Character Chunking (Level 2)
- Use for documents with structural boundaries
- Hierarchical separators: paragraphs → sentences → words
- Customize for document types (HTML, Markdown, JSON)
-
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
-
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
-
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.
Implement Chunking Pipeline
-
Pre-process documents
- Analyze structure, content types, information density
- Identify multi-modal content (tables, images, code)
-
Select parameters
- Chunk size: embedding model context window / 4
- Overlap: 10-20% for most cases
- Strategy-specific settings
-
Process and validate
- Apply chunking strategy
- Validate coherence: run
evaluate_chunks.py --coherence(see below) - Test with representative documents
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 196 lines · 69 tokens per session scan A 50e95b88a2e0
chunking-strategy is a skill published in the GitHub repository giuseppe-trisciuoglio/developer-kit (363 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 1,469 once invoked, about $0.0003 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 2026-08-30.
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