plan-writing
Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.
Best use case
plan-writing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.
Teams using plan-writing should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/plan-writing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How plan-writing Compares
| Feature / Agent | plan-writing | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Plan Writing ## Overview Convert research findings into actionable implementation plans. Scales planning rigor to stakes level. Every code-changing task specifies tests before implementation. ## When to Use - After research phase identifies what needs to change - Before implementing any medium or high stakes changes - When requirements are clear and codebase is understood ## Process 1. **Load research** - Find `*-<topic>-research.md` in `docs/plans/` 2. **Classify stakes** - Low (isolated, reversible), Medium (multiple files), High (architectural) 3. **Define success criteria** - Functional, non-functional, and acceptance criteria 4. **Decompose tasks** - Granular steps with file paths, line references, verification methods 5. **Plan tests** - Test specification as first sub-step per task (test-first) 6. **Assess risks** - Breaking changes, performance, security, dependencies, rollback strategy 7. **Write plan document** - `docs/plans/YYYY-MM-DD-<topic>-plan.md` 8. **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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