writing-plans

작성자: openai

멀티스텝 작업을 위한 명세나 요구사항이 있을 때, 코드를 건드리기 전에 사용하세요.

npx skills add https://github.com/openai/plugins --skill writing-plans

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: 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.

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 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 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**

```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"
```

No Placeholders

Every step must contain the actual content an engineer needs. These are plan failures — never write them:

  • "TBD", "TODO", "implement later", "fill in details"
  • "Add appropriate error handling" / "add validation" / "handle edge cases"
  • "Write tests for the above" (without actual test code)
  • "Similar to Task N" (repeat the code — the engineer may be reading tasks out of order)
  • Steps that describe what to do without showing how (code blocks required for code steps)
  • References to types, functions, or methods not defined in any task

Remember

  • Exact file paths always
  • Complete code in every step — if a step changes code, show the code
  • Exact commands with expected output
  • DRY, YAGNI, TDD, frequent commits

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. 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.

Execution Handoff

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:

  • REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development
  • Fresh subagent per task + two-stage review

If Inline Execution chosen:

  • REQUIRED SUB-SKILL: Use superpowers:executing-plans
  • Batch execution with checkpoints for review

openai의 다른 스킬

user-context
openai
데이터 분석 플러그인의 지속적인 소스 라우팅 기본 설정, 온보딩 로직, 설정 진행 상황 및 의미 계층 레지스트리를 로드하거나 관리합니다.
official
notion-research-documentation
openai
Notion 콘텐츠를 조사하고 인용문과 함께 구조화된 브리핑, 보고서 또는 비교 자료로 종합합니다. 대상 질의를 사용해 Notion 페이지를 검색하고 가져온 후, 인라인 출처 인용과 참고 문헌 섹션을 포함해 주제별로 결과를 정리합니다. 범위와 사용자 목표에 따라 네 가지 출력 형식(빠른 브리핑, 연구 요약, 비교, 종합 보고서) 중에서 선택합니다. 내장 템플릿을 사용해 Notion 페이지를 생성 및 업데이트하고, 새 정보가 도착하면 출처를 직접 연결하고 변경 사항을 추적합니다...
official
rcsb-pdb-skill
openai
핵심 메타데이터, Search API 쿼리 및 FASTA 다운로드를 위한 간결한 RCSB PDB 요청을 제출합니다. 사용자가 간결한 RCSB 요약을 원할 때 사용하며, 원시 JSON 또는…을 저장합니다.
official
pdf
openai
PDF 읽기, 생성 및 검증 기능을 제공하며, 시각적 렌더링과 프로그래매틱 생성을 지원합니다. Poppler(pdftoppm)를 사용하여 PDF 페이지를 PNG로 렌더링하여 레이아웃, 간격, 타이포그래피를 시각적으로 검사할 수 있습니다. reportlab을 사용하여 프로그래매틱 방식으로 PDF를 생성하여 안정적인 포맷을 보장하며, pdfplumber 또는 pypdf를 통해 텍스트와 메타데이터를 추출합니다. 품질 기준을 준수합니다: 잘린 텍스트, 겹치는 요소, 깨진 표, 렌더링 아티팩트가 없어야 하며, ASCII 하이픈만 사용하고 사람이 읽을 수 있는 인용을 사용합니다.
official
test-coverage-improver
openai
Improve test coverage in the OpenAI Agents JS monorepo: run `pnpm test:coverage`, inspect coverage artifacts, identify low-coverage files and branches, propose…
official
playwright
openai
터미널 기반 브라우저 자동화로 요소 스냅샷 및 대화형 UI 워크플로우 지원. playwright-cli 래퍼 스크립트를 통해 작동하며(npx 필요), 헤드리스 및 헤드 모드 모두 지원하여 시각적 디버깅 가능. 핵심 워크플로우: 페이지 열기, 안정적인 요소 참조를 위한 스냅샷 생성, 참조를 사용한 상호작용, 탐색 또는 DOM 변경 후 재스냅샷. 양식 작성, 클릭, 타이핑, 다중 탭 관리, 스크린샷/PDF 캡처, 흐름 디버깅을 위한 트레이스 기록 포함. 요소 참조(예: e3, e15)...
official
ukb-topmed-phewas-skill
openai
단일 변이에 대한 간결한 UKB-TOPMed PheWAS 요약을 가져오며, rsID, GRCh37 또는 GRCh38 입력을 받아 필요한 GRCh38 쿼리로 변환합니다. 다음과 같은 경우에 사용하세요…
official
code-review-context
openai
모델 가시 컨텍스트
official