Chunking Strategy Optimizer
$1.99OfficialInvoke when building or tuning a RAG/semantic-search pipeline's chunking: pick strategy, chunk size, and overlap validated against retrieval metrics.
What you get
- ✓9-step procedure
- ✓Runnable Python included
- ✓8-point quality checklist
- ✓7 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 8 min read
- Requires
- Works with any modern AI assistant
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Invoke this skill when you must decide how to split a corpus into chunks for a retrieval-augmented (RAG) or semantic-search pipeline, and you want that decision backed by numbers instead of folklore. Reach for it when: retrieval quality is poor and you suspect chunk boundaries; you are onboarding a new corpus (docs, code, transcripts, tables) or switching embedding models; or someone asks "what chunk size and overlap should we use?" and you refuse to answer with a guess. Do not use it for a throwaway prototype over a handful of documents — fixed 512-token chunks are fine there. Use it the moment chunking choices affect answer quality, index cost, or latency at scale.
The d
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