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 finishing-workgit 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/finishing-work)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/finishing-work"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/finishing-work/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/finishing-work"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/finishing-work.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 | $0.00028 | $0.00280 |
| Opus 5 | $0.00014 | $0.00140 |
| Sonnet 5 | $0.00006 | $0.00056 |
| Haiku 4.5 | $0.00003 | $0.00028 |
Grade A, and why
finishing-work 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 9d 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
- When transitioning from implementation to done
- Final quality check before closing out work
Process
- Verify all plan steps marked complete
- Confirm all success criteria met
- Update plan document with final status
- Generate completion summary (steps, phases, files, tests, reviews)
- Confirm with user that work is complete
Summary Format
- Steps completed: N/M
- Phases completed: N/M
- Files changed: [list]
- Test status: passing/failing
- Code review: verdict
- Security review: passed/failed
- Plan document: path
- All success criteria met: yes/no
Tool Use
Integrated into methodologies/rpikit/rpikit-implement (completion summary)
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.
- 9d ago First seen · 38 lines · 28 tokens per session scan A e6214428c66e
finishing-work is a skill published in the GitHub repository a5c-ai/babysitter (1,788 stars, last pushed 7d ago), licensed MIT. It adds 28 tokens to every session and 280 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 2026-09-03.
Other skills, from other repositories
kano-backlog
Prioritize and refine a GitHub Issues backlog with the Kano model — categorize every open issue as Must-be, Performance, Attractive, Indifferent, or Reverse, apply Kano + priority labels back to GitHub automatically, and recommend the single best next issue to pick up. Use this whenever the user wants to triage…
team-structure
Breaks a reviewed design into verified slices. Trigger on "slice this up", "break the design into steps", or "/team-structure".
tracking-tickets
Defines tracker status transitions and closing rules. Load when a pipeline run is linked to a ticket.
team-pr
Opens a pull request after verification. Trigger on "open the PR", "open a draft PR", or "/team-pr" only; never infer the phase from passed verification.
pr-watch-mechanics
Bounded watch-loop mechanics for the pr-watch skills: cycle timing, soft cap, handoff. Load when running or authoring a PR watch loop.
team-question
Decomposes a feature into task and question artifacts. Trigger on "shape this idea", "decompose this task", or "/team-question".