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writing-plans

在你有一个多步骤任务的规范或需求时使用,这要在接触代码之前

来源平台:ModelScope
来源标识:ModelScope/obra/writing-plans
源文件:原始说明
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在你有一个多步骤任务的规范或需求时使用,这要在接触代码之前

最后校验2026-05-18
来源平台ModelScope
安全提示
下载副本ZIP 可用

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  • 按 ModelScope 收录说明完成平台、开发或工作流任务。
  • 通过下载包离线保存 Skill 内容。
  • 结合下载量、访问量和喜欢数评估优先级。

输入与输出

输入:任务目标、上下文材料、平台信息、文件路径、约束条件或需要处理的内容。

输出:按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。

示例任务

  • 使用 writing-plans 帮我完成当前任务,并先确认必要上下文。
  • 根据 writing-plans 的说明,列出操作步骤和风险检查点。

安装方式

  1. 下载本站提供的 Skill ZIP 并解压。
  2. 把解压后的 Skill 目录放入当前 AI 工具支持的 skills 目录。
  3. 如需在线查看原始内容,可打开 GitHub 的 SKILL.md

在线原始地址:modelscope-obra-writing-plans/SKILL.md

风险边界

使用前请检查权限、外部依赖和要处理的数据类型。第三方平台数据、支付、部署、账号和密钥相关内容应先核对官方说明。

SKILL.md 文档介绍

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

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
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