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 few-shot-example-gengit 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/few-shot-example-gen)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/few-shot-example-gen"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/few-shot-example-gen/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/few-shot-example-gen"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/few-shot-example-gen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00016 | $0.00350 |
| Opus 5 | $0.00008 | $0.00175 |
| Sonnet 5 | $0.00003 | $0.00070 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
few-shot-example-gen 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 4d 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
Few-Shot Example Generation Skill
Capabilities
- Generate diverse few-shot examples
- Implement example selection strategies
- Optimize example ordering for performance
- Create dynamic example retrieval
- Design example formats for specific tasks
- Implement example quality validation
Target Processes
- prompt-engineering-workflow
- intent-classification-system
Implementation Details
Example Selection Strategies
- Semantic Similarity: Select similar examples
- MMR Selection: Diverse example selection
- N-Gram Overlap: Lexical similarity
- Random Sampling: Baseline selection
- Length-Based: Control example sizes
Configuration Options
- Number of examples
- Selection algorithm
- Example format (input/output structure)
- Max token limits
- Example store backend
Best Practices
- Cover edge cases in examples
- Balance example diversity
- Optimize example ordering
- Test with varied inputs
- Monitor token usage
Dependencies
- langchain
- sentence-transformers (for semantic selection)
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.
- 4d ago First seen · 66 lines · 16 tokens per session scan A ca54685a69d7
few-shot-example-gen is a skill published in the GitHub repository a5c-ai/babysitter (1,783 stars, last pushed 4d ago), licensed MIT. It adds 16 tokens to every session and 350 once invoked, about $0.0001 per session on Opus 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 2026-09-05.
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