plan-writing
SkillProductivityLets your agent turn research findings into step-by-step implementation plans with tests and small tasks.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the plan-writing skill
About this capability
Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/methodologies/rpikit/skills/plan-writing/SKILL.md and read by ahel’s review.
- 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
Signals
- GitHub stars
- 2k
- Forks
- 106
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
plan-writing- Source
- github.com/a5c-ai/babysitter