بنقرة واحدة
writing-plans
制定多步骤任务的详细执行计划,将复杂任务拆解为可操作步骤。适用于编码项目、咨询方案规划、研究项目设计、工作流设计等。触发词:帮我规划、制定计划、拆解任务、制定方案步骤、怎么推进、任务拆解、项目计划。
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
制定多步骤任务的详细执行计划,将复杂任务拆解为可操作步骤。适用于编码项目、咨询方案规划、研究项目设计、工作流设计等。触发词:帮我规划、制定计划、拆解任务、制定方案步骤、怎么推进、任务拆解、项目计划。
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
通过持续、逐条追问对齐方案/决策的每个关键节点,走遍决策树的每一个分支,直到达成共识后再动手。适用于开始执行前先把问题定义清楚。触发词:grill me、追问我、盘问我、帮我理一下这个方案、我们先对齐一下、深度盘问。
Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面", "视频封面", "video thumbnail", "video cover image", or provides a YouTube URL and wants the transcript/subtitle text or cover image extracted.
生成高质量 draw.io 图表并导出 PNG/SVG/PDF,含自检循环(最多5轮)和321个 AI/LLM 品牌 Logo。触发词:drawio图、导出PNG、导出PDF、AI架构图(带品牌Logo)、ML模型图、神经网络图、Transformer图、CNN图、代码结构图、代码转图、自定义样式图、精品图。注意:普通架构图/流程图/时序图请优先用 uml skill;网络拓扑→network;云架构→cloud。需要 draw.io 桌面版(brew install --cask drawio)。
【备用搜索】多引擎联合搜索,无需 API Key,支持17个引擎(Google、Bing、Scholar、知乎等)。Tavily 未配置时的降级方案。触发词:搜索、查一下、帮我找、search、找资料、查数据、查文献。注意:Tavily skill 配置后请优先用 tavily。
【首选搜索工具】Tavily AI 搜索,专为 AI Agent 优化,返回高质量摘要和来源,搜索结果比 web-search/multi-search-engine 更准确、更快。触发词:搜索、查一下、帮我找、search、找资料、查数据、查文献、最新消息、新闻、调研、research、查询、检索。注意:需要 API Key(tvly-xxxx)。未配置时降级使用 multi-search-engine。
用 PlantUML 生成云架构图,含 AWS/Azure/GCP/阿里云官方服务图标。触发词:云架构图、云架构、AWS架构、Azure架构、GCP架构、阿里云架构、云服务图、云迁移图、cloud architecture、cloud diagram。
| name | writing-plans |
| description | 制定多步骤任务的详细执行计划,将复杂任务拆解为可操作步骤。适用于编码项目、咨询方案规划、研究项目设计、工作流设计等。触发词:帮我规划、制定计划、拆解任务、制定方案步骤、怎么推进、任务拆解、项目计划。 |
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
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.
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.
This structure informs the task decomposition. Each task should produce self-contained changes that make sense independently.
Each step is one action (2-5 minutes):
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]
---
### 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"
```
Every step must contain the actual content an engineer needs. These are plan failures — never write them:
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. Placeholder scan: Search your plan for red flags — any of the patterns from the "No Placeholders" section above. Fix them.
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.
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.
After saving the plan, offer execution choice:
"Plan complete and saved to docs/superpowers/plans/<filename>.md. Two execution options:
1. Subagent-Driven (recommended) - I dispatch a fresh subagent per task, review between tasks, fast iteration
2. Inline Execution - Execute tasks in this session using executing-plans, batch execution with checkpoints
Which approach?"
If Subagent-Driven chosen:
If Inline Execution chosen: