一键导入
writing-plans
This skill should be used when the user has a spec or requirements for a multi-step task before touching code.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
This skill should be used when the user has a spec or requirements for a multi-step task before touching code.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
This skill should be used when the user needs deep, multi-step web research with source synthesis, citations, skeptical evidence evaluation, confidence/gap analysis, and optional dense/frontier research using parallel agents.
This skill should be used when the user wants to see all installed plugins, agents, skills, and MCP servers, and also inspect the current repository for local agents, skills, and MCP configuration. Scans the environment and presents a structured catalog of available resources. Works across all AI CLI platforms (Claude Code, GitHub Copilot, Gemini CLI, OpenCode, OpenAI Codex).
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
This skill should be used when the user needs to transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.
Relentless Socratic interview that walks down every branch of a plan or design, resolving decisions one-by-one. Trigger ONLY when the user explicitly says "grill me" or the PT equivalent "me grille". Do NOT trigger for stress-test, poke holes, devil's advocate, challenge, review, or feedback requests.
This skill should be used when the user needs structured strategic analysis and high-impact executive recommendations for complex business problems.
| name | writing-plans |
| description | This skill should be used when the user has a spec or requirements for a multi-step task before touching code. |
| license | MIT |
Create executable, low-ambiguity implementation plans that another engineer can run task-by-task with predictable outcomes.
Use this skill when:
Display progress at each planning phase:
[████░░░░░░░░░░░░░░░░] 25% — Phase 1/4: Gathering Context & Constraints
[████████░░░░░░░░░░░░] 50% — Phase 2/4: Decomposing Into Tasks
[████████████░░░░░░░░] 75% — Phase 3/4: Specifying Files & Commands
[████████████████████] 100% — Phase 4/4: Writing & Saving Plan
executing-plansWrite 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
Each step is one action (2-5 minutes):
Every plan MUST start with this header:
# [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 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"
```
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:
If Parallel Session chosen: