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shapeshape 搜索

Agent Skill

shape 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

930

周安装

38

GitHub Stars

81

下载量

298
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:shape(shape 搜索)
来源仓库:https://github.com/thecraighewitt/skills
仓库路径:skills/shape
安装命令:
npx skills add https://github.com/thecraighewitt/skills --skill shape
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/thecraighewitt/skills --skill shape

简介

shape 用于处理 GitHub 仓库、Issue 和 Pull Request 协作信息,适合前端设计场景。

  • 它可围绕仓库状态和代码变更进行整理,支持多宿主环境集成。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。
  • 需确认权限范围和维护状态,避免触发不必要的命令执行或文件读写。
  • shape 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Shape — Auto-Grill to PRD

Take a rough product idea and turn it into a complete PRD in one shot. No interactive grilling — Claude walks the decision tree itself, answers each question with software-engineering best practices, streams the Q&A live so you can spot bad assumptions, and writes the PRD.

Use /shape when you trust Claude's judgment and want speed. Use /grill-me + /write-a-prd when you want hands-on control over every decision.

Pipeline Position

StepCommandWhat It Does
1a/grill-me + /write-a-prdManual path — interactive interview, then PRD
1b/shapeFast path — auto-grill + PRD in one shot
2/prd-to-issuesBreak the PRD into vertical-slice sub-issues
3/ralphImplement each sub-issue autonomously with TDD + code review

shape produces the same PRD format as write-a-prd, so /prd-to-issues and /ralph consume its output without changes.

Instructions

When the user invokes this skill:

1. Capture the idea

If the user passed an idea as an argument, use it. Otherwise ask once:

What do you want to build? (one paragraph is fine)

Then proceed without further interactive questions until step 8.

2. Explore the codebase

Before answering anything, ground your decisions in reality:

  • Read README.md, CLAUDE.md, and any architecture docs
  • Identify existing modules, conventions, test patterns, and prior art the feature should match
  • Verify any factual assertions in the user's idea — don't trust them, check
  • Note the language, framework, test runner, and directory layout

If there is no codebase (empty directory or greenfield), skip to step 3 and record this in the PRD's Further Notes.

3. Walk the decision tree

For each branch below, generate the questions a thorough engineer would ask, then answer each one yourself. Do not skip a branch even if it feels obvious — that is the entire point of this skill.

  • Actors & user stories — who uses this, what they want, what success looks like
  • Happy-path flow — primary interaction step by step
  • Edge cases — empty inputs, large inputs, concurrent access, partial failures, network errors, permission denied, missing data, unicode/encoding, time zones
  • Data model & schema — entities, relationships, indexes, migrations
  • Module boundaries — deep modules, public interfaces, what stays internal
  • API contracts — request/response shapes, error codes, idempotency, versioning
  • Testing strategy — what to test, what to mock (only at boundaries), prior art in the repo
  • Security — authn/authz, input validation, secrets, rate limiting
  • Observability — what to log, what to surface as metrics
  • Out of scope — explicit non-goals to prevent scope creep
  • Dependencies & blockers — what must exist first

4. Best-practice defaults

When self-answering, prefer:

  • Boring over clever — simple, well-understood patterns
  • Deep modules (Ousterhout) — wide functionality behind a simple, stable interface
  • Match the codebase over external standards — project conventions win
  • TDD-friendly design — testable through public interfaces, not internals
  • Validate at system boundaries — trust internal callers, fail loudly at the edge
  • YAGNI — no speculative abstractions, no features the user didn't ask for
  • Parameterized queries, never string concatenation
  • Rate limit auth endpoints
  • Never log secrets, tokens, or PII
  • Mock only at system boundaries (external APIs, DBs, time, randomness, filesystem) — never mock internal collaborators

Codebase facts always beat generic best practices. If the project already does X, the answer is X.

5. Stream the Q&A live

For every decision, emit a block in this exact format as you make the call — do not batch:

Q: <the question>
A: <the chosen answer>
Why: <one sentence — cite a codebase reference if relevant>

This is the user's chance to spot a bad assumption early.

6. Write the PRD

Use this template exactly. It matches /write-a-prd, so the rest of the pipeline accepts it unchanged.

## Problem Statement

The problem the user is facing, from the user's perspective.

## Solution

The solution, from the user's perspective.

## User Stories

A long, numbered list:
1. As a <actor>, I want a <feature>, so that <benefit>

Cover every aspect of the feature surfaced in your decision tree.

## Implementation Decisions

- Modules to build or modify
- Public interfaces of those modules
- Architectural decisions
- Schema changes
- API contracts
- Specific interactions

Do NOT include file paths or code snippets — they go stale fast.

## Testing Decisions

- What makes a good test here (test external behavior, never implementation details)
- Which modules will be tested
- Prior art for the tests (similar patterns already in the codebase)

## Out of Scope

Explicit non-goals.

## Further Notes

Anything else worth recording.

## Decisions Log

Every Q/A/Why block from step 5, in the order they were decided.

7. Save the PRD locally

  • Generate a kebab-case slug from the idea (e.g. "rate-limited /healthz endpoint" → rate-limited-healthz-endpoint)
  • Create ./prds/ if it doesn't exist
  • Write the PRD to ./prds/<slug>.md
  • Print the absolute path

8. Offer to push to GitHub

After saving, ask the user once:

Push this as a GitHub issue? [y/N]

On y, run:

gh issue create --title "<slug>" --body-file ./prds/<slug>.md

Print the issue URL.

On n or no answer, stop. The local file is enough — the user can push later.

Rules

  • Don't ask the user questions during the decision tree. The whole point is auto-answering. The only interactive moments are: capturing the idea (if not given) and the GitHub push prompt at the end.
  • Don't skip branches. Even trivial-feeling branches get walked — completeness is the value.
  • Codebase facts beat generic best practices. If the project already does X, X is the answer.
  • No speculative scope. If the user didn't ask for it and the codebase doesn't require it, it goes in Out of Scope.
  • The PRD template is fixed. It must match /write-a-prd exactly so /prd-to-issues and /ralph keep working.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.57%
按下载量换算100

Claude

30.1%
按下载量换算90

Cursor

17.95%
按下载量换算53

Gemini CLI

9.69%
按下载量换算29

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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