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 spec-creationgit 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/spec-creation)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/spec-creation"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/spec-creation.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 | $0.00029 | $0.00338 |
| Opus 5 | $0.00015 | $0.00169 |
| Sonnet 5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
spec-creation 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
- Identify existing patterns and conventions
- Map dependencies and integration points
- Review existing tests for testing patterns
- Document technical constraints
Specification Components
Scope and Non-Goals
Clear boundaries on what the feature does and does not include.
Functional Requirements
Detailed requirements with unique identifiers for tracking.
Acceptance Criteria
Testable, measurable criteria for each requirement.
Architecture Decisions
Decision records with rationale and alternatives considered.
Implementation Plan
Phased approach ordered by dependency, not priority.
Risk Analysis
Identified risks with probability, impact, and mitigation strategies.
API Contracts and Data Models
Interface definitions and data model schemas.
Test Strategy
Mapping of unit, integration, and E2E tests to requirements.
Output
Specifications are saved to docs/specs/{feature}.md for reference by the execution workflow.
When to Use
/spec:create [feature]slash command- Before starting a new feature implementation
- When planning complex multi-module changes
Processes Used By
claudekit-spec-workflow(create mode)
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 · 57 lines · 29 tokens per session scan A 86e03ccf0516
spec-creation is a skill published in the GitHub repository a5c-ai/babysitter (1,777 stars, last pushed 2d ago), licensed MIT. It adds 29 tokens to every session and 338 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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