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 eval-harnessgit 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/eval-harness)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/eval-harness"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/eval-harness.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.00472 |
| Opus 5 | $0.00012 | $0.00236 |
| Sonnet 5 | $0.00005 | $0.00094 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
eval-harness 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
- Define test cases with known-correct outputs
- Run agent against each test case
- Score: accuracy, completeness, relevance
- Compare against baseline performance
- Track performance over time
2. Skill Quality Testing
- Verify skill instructions produce expected outcomes
- Test edge cases and boundary conditions
- Measure consistency across multiple runs
- Check for harmful or incorrect outputs
- Validate against ground truth
3. Regression Suite
- Collection of previously-passing test cases
- Run after any agent/skill modification
- Flag regressions with before/after comparison
- Maintain pass rate threshold (>= 95%)
4. Process Verification
- End-to-end process execution with known inputs
- Verify each phase produces expected outputs
- Check task ordering and dependency satisfaction
- Measure total execution time
Quality Scoring
Accuracy Score (0-100)
- Correctness of output vs expected
- Partial credit for partially correct outputs
- Penalty for hallucinated or fabricated content
Completeness Score (0-100)
- Coverage of required output elements
- Missing sections flagged and scored
- Bonus for useful additional context
Consistency Score (0-100)
- Run same input 3 times
- Compare outputs for semantic similarity
- Flag inconsistencies
Composite Score
- (accuracy * 0.4 + completeness * 0.3 + consistency * 0.3)
- Threshold: 80 to pass
When to Use
- After creating new agents or skills
- After modifying existing agents or skills
- Periodic quality audits
- Before promoting skills to production
Agents Used
- Used by process-level evaluation orchestrators
- No specific agent dependency (evaluates other agents)
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 · 70 lines · 23 tokens per session scan A feafb468c646
eval-harness is a skill published in the GitHub repository a5c-ai/babysitter (1,777 stars, last pushed 2d ago), licensed MIT. It adds 23 tokens to every session and 472 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.
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