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writing-plans写作计划

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

总安装

25

周安装

1

GitHub Stars

52

下载量

8
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:writing-plans(写作计划)
来源仓库:https://github.com/aaaaqwq/claude-code-skills
仓库路径:skills/writing-plans
安装命令:
npx skills add https://github.com/aaaaqwq/claude-code-skills --skill writing-plans
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aaaaqwq/claude-code-skills --skill writing-plans

简介

为开发者生成详细的代码实现计划与任务分解文档。

  • 假设工程师对项目零认知,提供文件路径、测试策略与执行步骤。
  • 输出保存至 docs/superpowers/plans/ 目录,按日期命名。
  • 建议配合 worktree 使用,确保上下文隔离与频繁提交。
  • 保留项目已有事实,避免将未确认信息写成确定结论。

SKILL.md

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:

# [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

### 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**

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**

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**

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) with precisely crafted review context — never your session history. This keeps the reviewer focused on the plan, not your thought process.

- Provide: chunk content, path to spec document

  1. If ❌ Issues Found:

- Fix the issues in the chunk - Re-dispatch reviewer for that chunk - Repeat until ✅ Approved

  1. 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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.27%
按下载量换算3

Claude

32.57%
按下载量换算3

Cursor

16.69%
按下载量换算1

Gemini CLI

9.12%
按下载量换算1

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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