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 architecture-patternsgit 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/architecture-patterns)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/architecture-patterns"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/architecture-patterns.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00327 |
| Opus 5 | $0.00012 | $0.00163 |
| Sonnet 5 | $0.00005 | $0.00065 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
architecture-patterns 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 7d 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
- Single responsibility boundaries
- Interface contracts and type safety
- Dependency injection patterns
- Module cohesion and coupling analysis
Data Flow
- Request/response patterns
- Event-driven architecture
- State management strategies
- Data transformation pipelines
Integration Patterns
- API design (REST, GraphQL, RPC)
- Message queuing and async processing
- Service boundaries and communication
- Error propagation across boundaries
Scalability
- Horizontal vs vertical scaling considerations
- Caching strategies
- Database design and query optimization
- Load balancing and distribution
Decision Checkpoints
Architectural decisions require user approval when:
- Introducing new service boundaries
- Changing data flow patterns
- Adding new external dependencies
- Modifying public API contracts
When to Use
- During PLAN workflow architecture phases
- During BUILD when architectural choices arise
- When reviewing system design in REVIEW workflow
Agents Used
planner(architecture planning)component-builder(architecture implementation)code-reviewer(architecture review)
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.
- 7d ago First seen · 55 lines · 23 tokens per session scan A 4e766f55ac8a
architecture-patterns is a skill published in the GitHub repository a5c-ai/babysitter (1,772 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 327 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 2026-08-30.
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