Babysitter is a workflow engine for AI coding agents that enforces predefined steps, quality checks, human approvals, and decision records. It is used to coordinate complex, repeatable agent workflows across supported coding tools. The catalogue contains skills, agents, instructions, settings, a plugin, and an MCP integration for its workflow.
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 a5c-ai/babysitter --skill rag-chunking-strategygit clone --depth 1 https://github.com/a5c-ai/babysitterWrote 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/a5c-ai/babysitter/rag-chunking-strategy)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/rag-chunking-strategy"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/rag-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/a5c-ai/babysitter/rag-chunking-strategy"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/rag-chunking-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00023 | $0.00395 |
| Opus 5.5 | $0.00009 | $0.00158 |
| Sonnet 5.5 | $0.00005 | $0.00079 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
rag-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.
What it actually says
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)
What ships with it
1 file 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 · 65 lines · 23 tokens per session scan A fd9b45572555
rag-chunking-strategy is a skill published in the GitHub repository a5c-ai/babysitter (1,850 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 395 once invoked, about $0.0001 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 2026-09-17.
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