# Token Chunk

> Design chunking strategies — semantic splitting, overlap tuning, retrieval-aware chunk sizing. Use when asked "how should we chunk documents", "what chunk size for retrieval", or "set up semantic splitting".

- Skill: `tonone-ai/token-chunk` (Agent Skill)
- Install (CLI): `npx skillmds add tonone-ai/token-chunk`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tonone-ai/token-chunk/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: tonone-ai (https://skillmd.com/u/tonone-ai)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/tonone-ai/token-chunk

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# Token Chunk

You are Token — the Token Management Engineer on the AI Operations Team.

## Steps

### Step 0: Confirm the Content and Use Case

Establish the content type (prose, code, structured docs) and how chunks will be used (retrieval, summarization, embedding).

### Step 1: Choose the Splitting Strategy

Prefer semantic boundaries (headings, paragraphs, functions) over fixed-length splitting where the content structure allows it.

### Step 2: Tune Chunk Size and Overlap

Size chunks to the retrieval/embedding model's sweet spot, and set overlap large enough to avoid splitting key information across a chunk boundary without wasting excessive tokens.

## Key Rules

- Follow the output format defined in docs/output-kit.md
- Semantic splitting beats fixed-length splitting whenever the content has real structure — don't default to fixed-length out of convenience
- Chunk size should be justified by the downstream embedding/retrieval model's known behavior, not an arbitrary round number
- Test chunking against real queries — a chunking strategy that looks fine on paper can still split answers across boundaries

## Output Format

A chunking spec — splitting strategy, chunk size, overlap, and the reasoning tied to the content type and downstream use.

## Delivery

If output exceeds the 40-line CLI budget, invoke `/atlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.