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RAG Chunking Strategy Skill — measured AI agent skill | DecimalAI

a5c-ai/rag-chunking-strategy

SkillScore9Maintained

Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

by a5c-ai

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

AI Engineer pack

decimalai skills pull a5c-ai-rag-chunking-strategy

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/a5c-ai-rag-chunking-strategy/SKILL.md

Raw file: /s/a5c-ai-rag-chunking-strategy/SKILL.md ↗ · machine-readable summary at /s/a5c-ai-rag-chunking-strategy.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

Runs

—

no usage data yet

Used when offered

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offered 349 · activation not measurable for prompt-injection usage

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 9d ago

Yes — +5% lift+2% tokens0% turns9% of whole cases pass with skill (2/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	-19%	0%
case-05	✓→✓	＝ Same ✓	-2%	0%
case-02	✗→✗	＝ Same ✗	-21%	0%
case-03	✗→✗	＝ Same ✗	-11%	0%
case-04	✗→✗	＝ Same ✗	13%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/31/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

a5c-ai / babysitter

library/specializations/ai-agents-conversational/skills/rag-chunking-strategy/SKILL.md

View on GitHub ↗

License MITImported 7/31/2026Version v1

Skill contents

SKILL.md

RAG Chunking Strategy Skill

Capabilities

Implement multiple document chunking strategies
Configure semantic chunking based on content boundaries
Set up recursive character text splitting
Design fixed-size chunking with overlap
Implement document-aware chunking (markdown, code, etc.)
Optimize chunk sizes for retrieval quality

Target Processes

rag-pipeline-implementation
chunking-strategy-design

Implementation Details

Chunking Strategies

RecursiveCharacterTextSplitter: Hierarchical splitting with separators
SemanticChunker: Embedding-based semantic boundaries
TokenTextSplitter: Token-aware splitting
MarkdownHeaderTextSplitter: Structure-aware markdown splitting
CodeSplitter: Language-aware code chunking

Configuration Options

Chunk size (characters or tokens)
Chunk overlap percentage
Separator hierarchy
Embedding model for semantic chunking
Document type detection

Best Practices

Match chunk size to embedding model limits
Use appropriate overlap for context preservation
Test retrieval quality with different strategies
Consider document structure in strategy selection

Dependencies

langchain-text-splitters
sentence-transformers (for semantic chunking)

Want to see if a5c-ai/rag-chunking-strategy would activate on your phrasing? Try it before you install — no LLM call, no signup.

Other measured skills in the registry, with their headline benchmark lift.