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 weekly-reflectiongit 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/weekly-reflection)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/weekly-reflection"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/weekly-reflection/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/weekly-reflection"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/weekly-reflection.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 | $0.00020 | $0.00343 |
| Opus 5.5 | $0.00008 | $0.00137 |
| Sonnet 5.5 | $0.00004 | $0.00069 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
cog-weekly-reflection 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
- Identify patterns in personal domain (energy, mood, productivity)
- Identify patterns in professional domain (skills, career, industry)
- Identify patterns in project domain (velocity, blockers, progress)
- Synthesize cross-domain connections
- Generate actionable insights with confidence levels
- Quality-gated reflection with iterative refinement
Tool Use Instructions
- Use
file-readto load entries from 01-daily, 02-personal, 03-professional, 04-projects - Use
file-searchto find related entries across sections - Analyze patterns within each domain independently
- Synthesize cross-domain connections
- Use
file-writeto create weekly check-in in 01-daily - Add cross-references to identified patterns
- Use
git-committo commit reflection
Examples
{
"vaultPath": "./cog-vault",
"mode": "weekly-checkin",
"userName": "Alex",
"rolePack": "engineer",
"targetQuality": 80
}
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 · 45 lines · 20 tokens per session scan A 1adfec130f66
cog-weekly-reflection is a skill published in the GitHub repository a5c-ai/babysitter (1,868 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 343 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 2026-09-03.
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