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 systematic-debugginggit 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/systematic-debugging)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/systematic-debugging"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/systematic-debugging.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 | $0.00026 | $0.00416 |
| Opus 5 | $0.00013 | $0.00208 |
| Sonnet 5 | $0.00005 | $0.00083 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
systematic-debugging 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 3d 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
- Unexpected behavior discovered during testing
- Bug reports require investigation
- Performance issues need root cause analysis
Process
- Reproduce - Confirm the defect with a minimal reproduction
- Hypothesize - Form theories about the root cause
- Investigate - Systematically test hypotheses (logs, breakpoints, bisection)
- Isolate - Narrow to the specific component/line
- Fix - Apply targeted fix addressing root cause
- Verify - Confirm fix resolves the issue without regression
Key Rules
- Never apply fixes without understanding the root cause
- For Strike-3/post-instrumentation handoffs, do not apply a source-code fix
until you enumerate at least 3 candidate root-cause hypotheses, give each
hypothesis a falsifying log line or observation, and cite concrete log
evidence for the selected fix. Use seq number when present; otherwise cite
timestamp, log-id, or artifact path plus the exact log line. If no proposed
fix cites a specific log line or log record, mark
needs-more-data. - Use web-researcher agent for unfamiliar error patterns
- Document the investigation path for future reference
- Verify that the fix does not introduce regressions
Tool Use
Integrated into methodologies/rpikit/rpikit-implement (failure handling)
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.
- 3d ago First seen · 42 lines · 26 tokens per session scan A c502e5497ab7
systematic-debugging is a skill published in the GitHub repository a5c-ai/babysitter (1,772 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 416 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.
Other skills, from other repositories
rn-debugging
This skill should be used when the user asks to "debug the app", "fix a crash", "diagnose a blank screen", "read error logs", "troubleshoot CDP connection", "check Metro status", "find native crashes", "inspect network failures", "the app crashed", "I see a blank screen", "the screen is white", "something broke", "app…
goga-change-investigator
Evidence-driven root cause investigation.
goga-change-tracer
Semantic behavior reconstruction via tracing.
test-driven-bug-fix
Defines reproduce-red-green-refactor bug fixes. Load when correcting a defect with a regression test.
shipit
Lands a reviewed pull request. Trigger on "ship it", "land the PR", "land this", or "/shipit" only; never infer ship intent from approval, green CI, or completion.
systematic-debugging
Defines reproduce, hypothesize, isolate, and fix workflow. Load when diagnosing a defect, failed test, or unexplained behavior.