plan-writing

Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.

509 stars

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

$curl -o ~/.claude/skills/plan-writing/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/methodologies/rpikit/skills/plan-writing/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/plan-writing/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How plan-writing Compares

Feature / Agentplan-writingStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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