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 frontmatter-parsinggit 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/frontmatter-parsing)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/frontmatter-parsing"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/frontmatter-parsing/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/frontmatter-parsing"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/frontmatter-parsing.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 | $0.00040 | $0.01951 |
| Opus 5 | $0.00020 | $0.00975 |
| Sonnet 5 | $0.00008 | $0.00390 |
| Haiku 4.5 | $0.00004 | $0.00195 |
Grade A, and why
frontmatter-parsing 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.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
status: in-progress phase: 72 wave: 1 depends_on: [] files_modified: ["src/auth/oauth.ts", "src/auth/tokens.ts"] created: 2026-03-02 updated: 2026-03-02
Plan content below...
## Capabilities
### 1. Parse Frontmatter
Extract frontmatter from a markdown file into structured data:
```yaml
# Input file: .planning/phase-72/PLAN-1.md
---
status: planned
phase: 72
plan_number: 1
wave: 1
depends_on: []
files_modified:
- src/auth/oauth.ts
- src/auth/tokens.ts
- src/middleware/auth.ts
task_count: 4
created: 2026-03-02
gap_closure: false
---
Parsed result:
{
"status": "planned",
"phase": 72,
"plan_number": 1,
"wave": 1,
"depends_on": [],
"files_modified": ["src/auth/oauth.ts", "src/auth/tokens.ts", "src/middleware/auth.ts"],
"task_count": 4,
"created": "2026-03-02",
"gap_closure": false
}
2. Extract Specific Fields
Read individual fields without parsing the entire frontmatter:
get_field(.planning/phase-72/PLAN-1.md, "wave") -> 1
get_field(.planning/phase-72/PLAN-1.md, "status") -> "planned"
get_field(.planning/phase-72/PLAN-1.md, "files_modified") -> ["src/auth/oauth.ts", ...]
3. Update Fields
Update individual frontmatter fields without modifying body content:
update_field(.planning/phase-72/PLAN-1.md, "status", "executed")
update_field(.planning/phase-72/PLAN-1.md, "wave", 2)
update_field(.planning/phase-72/PLAN-1.md, "updated", "2026-03-02")
Uses Edit tool to surgically replace only the target field line.
4. Add New Fields
Add fields to existing frontmatter:
add_field(.planning/phase-72/PLAN-1.md, "executed_at", "2026-03-02T14:30:00Z")
add_field(.planning/phase-72/PLAN-1.md, "executor_agent", "gsd-executor")
Inserts new field before the closing --- delimiter.
5. Remove Fields
Remove fields from frontmatter:
remove_field(.planning/phase-72/PLAN-1.md, "gap_closure")
6. Query Across Files
Find documents matching frontmatter criteria:
query(directory: ".planning/phase-72/", field: "wave", value: 1)
-> [".planning/phase-72/PLAN-1.md", ".planning/phase-72/PLAN-2.md"]
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 · 248 lines · 40 tokens per session scan A 8645d6d93d25
frontmatter-parsing is a skill published in the GitHub repository a5c-ai/babysitter (1,788 stars, last pushed 6d ago), licensed MIT. It adds 40 tokens to every session and 1,951 once invoked, about $0.0002 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
goga-review-plan
Verify execution plan completeness and correctness.
goga-brainstorm
Pipeline orchestrator for brainstorming the cells architecture.
goga-define-challenge
Challenge the complete product definition before the PRD is generated.
goga-define-experience
Define the user experience required to achieve the established product goals and solve the identified problem.
goga-define-goals
Define the product outcomes that should be achieved by solving the identified problem for the relevant users.
goga-brainstorm-cell-assembly
Assembling CODEMANIFEST and .usages per cell, with final approval.