---
name: writing-plans-36f2e3326b92
description: Use when you have a spec or requirements for a multi-step task, before touching code
---

# 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/superpowers/plans/YYYY-MM-DD-<feature-name>.md`
- (User preferences for plan location override this default)

## Scope Check

If the spec covers multiple independent subsystems, it should have been broken into sub-project specs during brainstorming. If it wasn't, suggest breaking this into separate plans — one per subsystem. Each plan should produce working, testable software on its own.

## File Structure

Before defining tasks, map out which files will be created or modified and what each one is responsible for. This is where decomposition decisions get locked in.

- Design units with clear boundaries and well-defined interfaces. Each file should have one clear responsibility.
- You reason best about code you can hold in context at once, and your edits are more reliable when files are focused. Prefer smaller, focused files over large ones that do too much.
- Files that change together should live together. Split by responsibility, not by technical layer.
- In existing codebases, follow established patterns. If the codebase uses large files, don't unilaterally restructure - but if a file you're modifying has grown unwieldy, including a split in the plan is reasonable.

This structure informs the task decomposition. Each task should produce self-contained changes that make sense independently.

## Bite-Sized Task Granularity

**Each step is one action (2-5 minutes):**
- "Write the failing test" - step
- "Run it to make sure it fails" - step
- "Implement the minimal code to make the test pass" - step
- "Run the tests and make sure they pass" - step
- "Commit" - step

## Plan Document Header

**Every plan MUST start with this header:**

```markdown
# [Feature Name] Implementation Plan

> **For agentic workers:** REQUIRED: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. Steps use checkbox (`- [ ]`) syntax for tracking.

**Goal:** [One sentence describing what this builds]

**Architecture:** [2-3 sentences about approach]

**Tech Stack:** [Key technologies/libraries]

---
```

## Task Structure

````markdown
### Task N: [Component Name]

**Files:**
- Create: `exact/path/to/file.py`
- Modify: `exact/path/to/existing.py:123-145`
- Test: `tests/exact/path/to/test.py`

- [ ] **Step 1: Write the failing test**

```python
def test_specific_behavior():
    result = function(input)
    assert result == expected
```

- [ ] **Step 2: Run test to verify it fails**

Run: `pytest tests/path/test.py::test_name -v`
Expected: FAIL with "function not defined"

- [ ] **Step 3: Write minimal implementation**

```python
def function(input):
    return expected
```

- [ ] **Step 4: Run test to verify it passes**

Run: `pytest tests/path/test.py::test_name -v`
Expected: PASS

- [ ] **Step 5: Commit**

```bash
git add tests/path/test.py src/path/file.py
git commit -m "feat: add specific feature"
```
````

## Remember
- Exact file paths always
- Complete code in plan (not "add validation")
- Exact commands with expected output
- Reference relevant skills with @ syntax
- DRY, YAGNI, TDD, frequent commits

## Plan Review Loop

After completing each chunk of the plan:

1. Dispatch plan-document-reviewer subagent (see plan-document-reviewer-prompt.md) for the current chunk
   - Provide: chunk content, path to spec document
2. If ❌ Issues Found:
   - Fix the issues in the chunk
   - Re-dispatch reviewer for that chunk
   - Repeat until ✅ Approved
3. If ✅ Approved: proceed to next chunk (or execution handoff if last chunk)

**Chunk boundaries:** Use `## Chunk N: <name>` headings to delimit chunks. Each chunk should be ≤1000 lines and logically self-contained.

**Review loop guidance:**
- Same agent that wrote the plan fixes it (preserves context)
- If loop exceeds 5 iterations, surface to human for guidance
- Reviewers are advisory - explain disagreements if you believe feedback is incorrect

## Execution Handoff

After saving the plan:

**"Plan complete and saved to `docs/superpowers/plans/<filename>.md`. Ready to execute?"**

**Execution path depends on harness capabilities:**

**If harness has subagents (Claude Code, etc.):**
- **REQUIRED:** Use superpowers:subagent-driven-development
- Do NOT offer a choice - subagent-driven is the standard approach
- Fresh subagent per task + two-stage review

**If harness does NOT have subagents:**
- Execute plan in current session using superpowers:executing-plans
- Batch execution with checkpoints for review


---
Source attribution: AgentTeam-TaichuAI
Original project: https://github.com/AgentTeam-TaichuAI/ScienceClaw
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: AgentTeam-TaichuAI
Original project: https://github.com/AgentTeam-TaichuAI/ScienceClaw
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Development & Engineering, Product Management, Project & Program Management
Roles: Backend Developer, Frontend Developer, Full-Stack Developer, Product Manager, Project Manager
Specialties: Cross Platform
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 75e7f0da6a32cddbdd2cb85c3dc0fd09a9b9e9d12711ce697498096bf8d489ec