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writing-plans

将规格或需求整理成实施计划,明确要修改的文件、函数定义、测试和验证步骤。在编写代码前拆分工作,形成可交给其他人员或代理执行的任务。

原文查看原文说明

Use when you have a spec or requirements for a multi-step task, before touching code

AI 评分100分/ 100查看安装方法

适用场景

  • 编写代码前规划功能实施
  • 将需求拆成可测试的任务
  • 向其他人员或代理交接实施工作
  • 检查计划中的遗漏与定义矛盾

中文介绍

功能

writing-plans 根据规格或需求,编制实施人员可以照着执行的具体计划。它列出需要创建或修改的文件及各自职责,将工作拆成能够独立测试的任务。每项任务写明函数名、参数、返回值、测试内容、执行命令和预期结果,并用复选框列出步骤:编写测试、确认失败、实施修改、确认测试通过,最后提交更改。

适用场景

适合在编写代码前梳理包含多个阶段的功能,也适合向尚未阅读代码或规格的人员交接工作。计划完成后,会自行检查需求是否遗漏、任务之间的名称和类型是否一致,以及哪些输入和失败条件需要测试。

使用示例

  • “请根据这份规格制定实施计划,包含修改文件和测试步骤。”
  • “请明确各任务之间的函数名和类型,方便交给其他人员实施。”
  • “请核对计划是否满足规格,检查遗漏和矛盾。”

注意事项

需要已有规格或需求。默认保存位置为 docs/superpowers/plans/YYYY-MM-DD-<feature-name>.md。计划编制完成后会等待用户审阅,实施时使用用户选定的另一项执行技能。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
A良好明确用于已有多步骤需求时,在编写代码前制定实施计划。
步骤具体性步骤、输入输出和示例是否具体
A良好计划模板包含文件、函数类型、测试、执行命令和预期结果。
安全性发送数据、删除文件、执行命令等是否有说明
A良好明确计划保存位置,测试和提交命令作为实施计划中的步骤列出。
安装难易度所需工具和 API 密钥是否少且有说明
A良好以已有规格或需求为前提,未指定编制计划需额外工具或 API 密钥。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

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包含的文件(1)

  • SKILL.md10.1 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Writing Plans

Overview

Write implementation plans for an engineer who has not seen this codebase or this spec. Assume they write idiomatic code in the project's language once they know the exact interface and the exact test, and that they will make a reasonable choice wherever the plan leaves one open. What they cannot know is what you decided: which files, which names and signatures, which values from the spec, which tests prove each task. Document those. Give them the whole plan as bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.

Announce at start: "I'm using the writing-plans skill to create the implementation plan."

Context: If working in an isolated worktree, it should have been created via the superpowers:using-git-worktrees skill at execution time.

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.

Task Right-Sizing

A task is the smallest unit that carries its own test cycle and is worth a fresh reviewer's gate. When drawing task boundaries: fold setup, configuration, scaffolding, and documentation steps into the task whose deliverable needs them; split only where a reviewer could meaningfully reject one task while approving its neighbor. Each task ends with an independently testable deliverable.

Step Granularity

Each step is one action with a checkable result:

  • "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 SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.

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

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

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

**Spec:** [path to the spec/design doc this plan implements — the plan
argues from the spec, so the spec travels with it; executors read both]

## Global Constraints

[The spec's project-wide requirements — version floors, dependency limits,
naming and copy rules, platform requirements — one line each, with exact
values copied verbatim from the spec. Every task's requirements implicitly
include this section.]

## Review Focus

[The five input classes or failure modes the spec implies but no task's
tests exercise that are most likely to bite a person using this software
— one line each, naming the input or condition and the behavior a
reasonable person would expect, most likely first. The spec is a vision
document: it says what the software must do, not everything it will
meet, and its silence on an input is not permission for that input to
break the program. Write the list here, once, with the spec in front of
you. Then, for each line, add the test that pins it to the task that
owns the code, in that task's own step style.]

---

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`

**Interfaces:**
- Consumes: [what this task uses from earlier tasks — exact signatures]
- Produces: [what later tasks rely on — exact function names, parameter
  and return types. A task's implementer sees only their own task; this
  block is how they learn the names and types neighboring tasks use.]

- [ ] **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: Implement `function(input: InputType) -> ResultType` in `exact/path/to/file.py`**

One line on the approach when the signature and the test leave a choice
(which library call, which data structure); a code block only for an
algorithm they do not determine.

- [ ] **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"
```

What a Step Contains

A step is done when the implementer can write exactly one reasonable thing from it. That is the whole requirement: unambiguous, not complete. Each kind of step carries what makes it unambiguous and nothing more:

  • A test step: the test's name and its assertions, as code, with the spec's exact values in them.
  • A code step: the exact signature (name, parameters, return type), the file it lives in, and the specific values the spec pins. The implementer writes the body. A body appears only for an algorithm the signature and tests do not determine, or for exact copy the spec fixes.
  • A verification step: the command to run and the output that means it passed.
  • A reference to another task: that task's Interfaces block says what to use; the plan does not repeat that task's code.

A plan is the set of decisions the implementer cannot make alone. A plan longer than the code it describes has written the code instead. Lines that decide nothing ("TBD", "handle edge cases", "add appropriate validation", "write tests for the above", a type or function no task defines) are the opposite failure, and the self-review catches both.

Self-Review

After writing the complete plan, look at the spec with fresh eyes and check the plan against it. This is a checklist you run yourself — not a subagent dispatch.

1. Spec coverage: Skim each section/requirement in the spec. Can you point to a task that implements it? List any gaps.

2. Step scan: Every step must let the implementer write exactly one reasonable thing, and no step may carry more than that: a line that decides nothing is a gap, a function body the signature and tests already determine is a transcript. Fix both.

3. Type consistency: Do the types, method signatures, and property names you used in later tasks match what you defined in earlier tasks? A function called clearLayers() in Task 3 but clearFullLayers() in Task 7 is a bug.

4. Review Focus: For each input class or failure mode the spec implies, is there a task whose tests exercise it? The five uncovered ones most likely to bite a person go in the Review Focus section, and each line there gets its test added to the owning task. An empty section means you checked and found none, not that you skipped the check.

5. Proportion: Compare the plan's length to the spec's. A plan several times longer than the spec it implements is a transcript of the program, not a plan. If code blocks are most of the document, replace bodies with signatures, test names and assertions, and check that each step is still unambiguous.

If you find issues, fix them inline. No need to re-review — just fix and move on. If you find a spec requirement with no task, add the task.

Execution Handoff

After saving and self-reviewing the plan, link it for your human partner to read. If they have already explicitly supplied an execution method, ask them to review the plan and confirm it captures what they want; wait for that review before implementation, then use the preserved method. Otherwise, ask them to review the plan and choose an execution method before implementation.

When no execution method has already been supplied:

"Plan complete and saved to docs/superpowers/plans/<filename>.md. Please review the plan. Which execution approach would you prefer?

  • Subagent-driven - A fresh subagent implements each task and a fresh reviewer checks it before the next one starts, then a whole-branch review at the end. Most thorough; costs a fresh context per task and per review.
  • Native - I implement every task myself in this session, the way this harness runs work, then one fresh reviewer on the most capable model checks the whole branch. Cheapest and fastest; no independent review until the end. Runs well with a mid-tier session model, since the plan carries the design.

For this plan I recommend <one of the two>, because <one sentence from the plan: how much the tasks depend on each other's interfaces, how many there are, what a shipped mistake would cost>. Does the plan capture what you want, and which approach should we use?"

When an execution method has already been supplied:

"Plan complete and saved to docs/superpowers/plans/<filename>.md. Please review the plan. Does it capture what you want?"

If Subagent-driven chosen:

  • REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development

If Native chosen:

  • REQUIRED SUB-SKILL: Use superpowers:executing-plans

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