writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code
Best use case
writing-plans is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when you have a spec or requirements for a multi-step task, before touching code
Teams using writing-plans 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/a3b4c5d6-e7f8-4a5b-9c8d-3e4f5a6b7c8d/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How writing-plans Compares
| Feature / Agent | writing-plans | 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?
Use when you have a spec or requirements for a multi-step task, before touching code
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
# 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`
## 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 Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
**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
## 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:**
- **REQUIRED SUB-SKILL:** Use superpowers: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
- **REQUIRED SUB-SKILL:** New session uses superpowers:executing-plansRelated Skills
adr-writing
Write Architectural Decision Records following MADR template. Applies Definition of Done criteria, marks gaps for later completion. Use when generating ADR documents from extracted decisions.
writing-ad-copy
Creates platform-specific ad copy for paid campaigns with A/B variants. Use when the user asks about ad copy, PPC ads, Google Ads, Facebook ads, LinkedIn ads, or paid campaign copy.
acceptance-test-writing
Guide for writing high-quality acceptance criteria and acceptance tests using industry-standard BDD (Behavior-Driven Development) and ATDD (Acceptance Test-Driven Development) practices. Use this skill when creating acceptance criteria for user stories, writing Gherkin scenarios, or implementing acceptance test specifications following Given-When-Then format.
academic-writing-style
Personalized academic writing assistant for university assignments in Chinese and English. Use when users need help writing/revising academic reports, project docs, technical analyses, research reviews, or case studies. Produces natural prose avoiding AI markers. Triggers: academic writing, assignment, report, technical analysis, research review, case study. | 个性化学术写作助手，适用于中英文大学作业。触发词：学术写作、作业、报告、技术分析、研究综述、案例研究、项目文档。
academic-writing-standards
Expert knowledge of academic writing standards for peer-reviewed papers, including citation integrity, style compliance, clarity, and scientific writing best practices. Use when reviewing or editing academic manuscripts, papers, or research documentation.
academic-writing-cs
Comprehensive toolkit for writing high-quality computer science research papers (conference, journal, thesis). Provides narrative construction guidance, sentence-level clarity principles (Gopen & Swan), academic phrasebank, CS-specific conventions, and section-by-section quality checklists. Use when assisting with academic paper writing, revision, or structure planning across all stages from drafting to submission.
academic-research-writing
Use when writing CS research papers (conference, journal, thesis), reviewing scientific manuscripts, improving academic writing clarity, or preparing IEEE/ACM submissions. Invoke when user mentions paper, manuscript, research writing, journal submission, or needs help with academic structure, formatting, or revision.
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
grail-miner
This skill assists in setting up, managing, and optimizing Grail miners on Bittensor Subnet 81, handling tasks like environment configuration, R2 storage, model checkpoint management, and performance tuning.
vly-money
Generate crypto payment links for supported tokens and networks, manage access to X402 payment-protected content, and provide direct access to the vly.money wallet interface.
lets-go-rss
A lightweight, full-platform RSS subscription manager that aggregates content from YouTube, Vimeo, Behance, Twitter/X, and Chinese platforms like Bilibili, Weibo, and Douyin, featuring deduplication and AI smart classification.
whisper-transcribe
Transcribes audio and video files to text using OpenAI's Whisper CLI, enhanced with contextual grounding from local markdown files for improved accuracy.