Provide implementations of advanced data structures
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a5c-ai/babysitter1.9KUpdated Sep 17, 2026
FreePublished on GitHub
plan-writing
Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.
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See how to installFiles included (2)
- SKILL.md1.7 KB
- README.md663 B
SKILL.md (original)
Review the instructions the agent will receive before you install.
- Before implementing any medium or high stakes changes
- When requirements are clear and codebase is understood
Process
- Load research - Find
*-<topic>-research.mdindocs/plans/ - Classify stakes - Low (isolated, reversible), Medium (multiple files), High (architectural)
- Define success criteria - Functional, non-functional, and acceptance criteria
- Decompose tasks - Granular steps with file paths, line references, verification methods
- Plan tests - Test specification as first sub-step per task (test-first)
- Assess risks - Breaking changes, performance, security, dependencies, rollback strategy
- Write plan document -
docs/plans/YYYY-MM-DD-<topic>-plan.md - Approval gate - Human approves, requests changes, or returns to research
Anti-Patterns to Avoid
- Vague task descriptions without specific file references
- Missing verification criteria for any step
- Combining test writing and implementation into single steps
- Planning rigor mismatched to stakes level
- Proceeding without explicit user approval
Tool Use
Invoke via babysitter process: methodologies/rpikit/rpikit-plan
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