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 continuous-learninggit 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/continuous-learning)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/continuous-learning"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/continuous-learning/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/continuous-learning"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/continuous-learning.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 | $0.00025 | $0.00456 |
| Opus 5.5 | $0.00010 | $0.00182 |
| Sonnet 5.5 | $0.00005 | $0.00091 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
continuous-learning 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 1mo 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
- Analyze code changes and implementation approaches
- Identify recurring patterns and conventions
- Extract architectural decisions with rationale
- Capture error resolution strategies
- Record tool usage patterns
- Assign initial confidence scores (0-100)
2. Pattern Evaluation
- Score generalizability (0-100): cross-project applicability
- Score reliability (0-100): validation frequency
- Score impact (0-100): outcome improvement
- Composite: generalizability * 0.3 + reliability * 0.4 + impact * 0.3
- Filter below confidence threshold (default: 75)
- Merge similar patterns
3. Skill Creation
- Convert high-confidence patterns to SKILL.md format
- Write clear instructions with phases
- Include when-to-use and when-not-to-use sections
- Add usage examples and agent references
- Follow kebab-case naming convention
4. Organization
- Categorize: language-specific, domain, business, meta
- Resolve naming conflicts
- Update indexes and manifests
- Create dependency graphs
5. Version and Export
- Assign semantic versions by maturity
- Create portable export bundles
- Include usage examples and test cases
- Generate import instructions
Strategic Compaction
- Analyze context token usage
- Identify low-value context for compression
- Archive completed phases to memory files
- Calculate token savings per suggestion
When to Use
- End of development sessions
- After significant code reviews
- After debugging sessions
- Periodically during long sessions
Agents Used
continuous-learning(custom agent for this skill)context-engineering(compaction analysis)
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.
- 1mo ago First seen · 64 lines · 25 tokens per session scan A c3def4da77c8
continuous-learning is a skill published in the GitHub repository a5c-ai/babysitter (1,868 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 456 once invoked, about $0.0001 per session on Opus 5.5. 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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