home / skills / bobmatnyc / claude-mpm-skills / writing-plans

This skill creates comprehensive, bite-sized implementation plans for zero-context engineers, detailing exact files, code, tests, and steps to execute.

npx playbooks add skill bobmatnyc/claude-mpm-skills --skill writing-plans

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SKILL.md
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---
name: Writing Plans
description: Create detailed implementation plans with bite-sized tasks for engineers with zero codebase context
when_to_use: when design is complete and you need detailed implementation tasks for engineers with zero codebase context
version: 2.2.0
progressive_disclosure:
  level: 1
  references:
    - path: references/plan-structure-templates.md
      title: Plan Structure & Templates
      description: Standard headers, task templates, and structure examples
    - path: references/best-practices.md
      title: Best Practices & Guidelines
      description: Writing for zero-context engineers, code completeness, test design patterns
---

# Writing Plans

## Overview

Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it. Give them the whole plan as bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.

Assume they are a skilled developer, but know almost nothing about our toolset or problem domain. Assume they don't know good test design very well.

**Announce at start:** "I'm using the Writing Plans skill to create the implementation plan."

**Context:** This should be run in a dedicated worktree (created by brainstorming skill).

**Save plans to:** `docs/plans/YYYY-MM-DD-<feature-name>.md`

## Quick Reference

**Plan header template:** See [Plan Structure & Templates](references/plan-structure-templates.md#standard-plan-document-header)

**Task template:** See [Plan Structure & Templates](references/plan-structure-templates.md#task-template-structure)

**Granularity guide:** Each step = 2-5 minutes. See [Best Practices](references/best-practices.md#bite-sized-task-granularity)

## Core Principles

- **Exact file paths always** - Not "in the user module" but "`src/models/user.py`"
- **Complete code in plan** - Not "add validation" but show the validation code
- **Exact commands with expected output** - "`pytest tests/file.py -v`" with what you'll see
- **Reference relevant skills** - Use @ syntax: `@skills/category/skill-name`
- **DRY, YAGNI, TDD, frequent commits** - Every task follows this pattern

For detailed guidance: [Best Practices & Guidelines](references/best-practices.md)

## Execution Handoff

After saving the plan, offer execution choice:

**"Plan complete and saved to `docs/plans/<filename>.md`. Two execution options:**

**1. Subagent-Driven (this session)** - I dispatch fresh subagent per task, review between tasks, fast iteration

**2. Parallel Session (separate)** - Open new session with executing-plans, batch execution with checkpoints

**Which approach?"**

**If Subagent-Driven chosen:**
- Use @skills/collaboration/subagent-driven-development
- Stay in this session
- Fresh subagent per task + code review

**If Parallel Session chosen:**
- Guide them to open new session in worktree
- New session uses @skills/collaboration/executing-plans

## Remember

- Write for zero-context engineers (specify everything)
- Complete code blocks, not instructions
- Exact commands with expected output
- Test first, then implement, then commit
- Reference existing patterns in codebase
- Keep tasks bite-sized (2-5 minutes each)

**Need examples?** See [Plan Structure & Templates](references/plan-structure-templates.md) for complete task examples.

**Need patterns?** See [Best Practices](references/best-practices.md) for error handling, logging, test design, and more.

Overview

This skill creates detailed implementation plans for engineers who have zero context about the codebase. It produces bite-sized, test-first tasks with exact file paths, full code snippets, exact commands and expected output, and a clear save location for the plan.

How this skill works

I announce the use of the Writing Plans skill at the start, then generate a structured plan saved to docs/plans/YYYY-MM-DD-<feature-name>.md. Each task is TDD-first, includes exact file edits, full code to paste, test commands with expected output, commit messages, and links to relevant subskills like @skills/collaboration/subagent-driven-development. The plan assumes the engineer knows little about the toolchain and provides explicit instructions for a dedicated worktree.

When to use it

  • You need an implementation plan for engineers unfamiliar with the codebase or domain
  • Onboarding new contributors who must make safe, test-covered changes
  • Breaking a feature into 2–5 minute, reviewable tasks
  • When you want repeatable, audit-ready task lists with exact commands
  • Before running automated execution via a subagent or parallel session

Best practices

  • Save plans under docs/plans/YYYY-MM-DD-<feature-name>.md and reference the filename in handoffs
  • Keep tasks 2–5 minutes: single purpose, repeatable, and test-first
  • Always include exact file paths and full code blocks, not abstract descriptions
  • Include the exact shell commands and their expected outputs for tests and linters
  • Follow DRY, YAGNI, TDD and make frequent, small commits with clear messages

Example use cases

  • Add a new API endpoint: include tests in tests/test_api_new_endpoint.py, implementation in src/api/new_endpoint.py, and docs update in docs/api.md
  • Fix a bug in user serialization: tests in tests/test_user_serializer.py, code changes in src/models/user.py, run pytest and show expected failures and passes
  • Migrate a small module: tasks to create a worktree, update imports in src/utils/legacy.py, add adapter code in src/utils/adapter.py, and end-to-end tests in tests/test_migration.py
  • Introduce a low-risk optimization: microbenchmarks in tests/bench/test_algo.py, implementation in src/algorithms/fast_algo.py, and expected benchmark outputs

FAQ

How is the plan delivered and where is it saved?

The plan is saved to docs/plans/YYYY-MM-DD-<feature-name>.md and the plan header states the file path; I also report the saved filename in the final message.

What execution modes are available after the plan is ready?

Two options: Subagent-Driven (stay in this session and dispatch a fresh subagent per task via @skills/collaboration/subagent-driven-development) or Parallel Session (open a separate session using @skills/collaboration/executing-plans).