Agent skill

babysitter:yolo

Start babysitting in non-interactive mode — no user interaction or breakpoints, fully autonomous execution.

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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/yolo-a5c-ai-babysitter

SKILL.md

babysitter:yolo

Identical to /babysitter:call but runs in non-interactive mode:

  • Skip the interview phase — parse intent directly from the user's prompt
  • Auto-approve all breakpoints — never pause for human approval
  • No user questions — proceed autonomously through the entire orchestration loop

Workflow

  1. Parse the initial prompt to extract intent, scope, and requirements
  2. Research the repo structure to understand the codebase
  3. Search the process library for relevant specializations/methodologies
  4. Create the process .js file and inputs
  5. Create the run:
bash
babysitter run:create \
  --process-id <id> \
  --entry <path>#<export> \
  --inputs <inputs-file> \
  --prompt "$PROMPT" \
  --harness codex \
  --session-id "${CODEX_THREAD_ID:-$CODEX_SESSION_ID}" \
  --plugin-root "$CODEX_PLUGIN_ROOT" \
  --json
  1. Iterate until completion — auto-resolve all breakpoints:
bash
babysitter run:iterate .a5c/runs/<runId> --json --iteration <n>

For breakpoint effects, immediately post approval:

bash
echo '{"approved":true,"response":"Auto-approved (yolo mode)"}' > tasks/<effectId>/output.json
babysitter task:post .a5c/runs/<runId> <effectId> --status ok --value tasks/<effectId>/output.json --json
  1. When completionProof is emitted, return it wrapped in <promise>PROOF</promise>

Key Difference from /babysitter:call

The ONLY difference is that breakpoints are auto-approved and no user questions are asked. The orchestration loop, effect handling, and result posting are identical.

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