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spec-builder规格构建者

Agent Skill

spec-builder 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

549

周安装

22

GitHub Stars

公开资料未说明

下载量

178
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:spec-builder(规格构建者)
来源仓库:https://github.com/ilamanov/skills
仓库路径:skills/spec-builder
安装命令:
npx skills add https://github.com/ilamanov/skills --skill spec-builder
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/ilamanov/skills --skill spec-builder

简介

spec-builder 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 中围绕仓库状态、代码变更或协作事项进行整理。
  • 可通过 npx skills add 命令从指定仓库安装并使用该技能。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网或文件读写操作。
  • 可结合来源仓库和原始 README 进一步核验具体用法和功能边界。

SKILL.md

Spec Builder

Transform vague ideas into concrete, actionable spec documents through structured interviews.

Workflow

Phase 1: Gather Initial Context

First, prompt for the idea:

What's the idea you'd like to turn into a spec?
Describe it however it exists in your head right now - it can be vague.

Then ask background questions to calibrate the interview depth:

AskUserQuestion:
1. "What's your background?"
   - Technical (developer/engineer)
   - Semi-technical (PM, designer, technical founder)
   - Non-technical (business, creative, general user)

2. "What's the goal for this spec?"
   - MVP/prototype to test the idea
   - Full product spec for development
   - Implementation plan for an AI coding agent
   - Pitch document for stakeholders
   - Personal reference to clarify my thinking

Phase 2: Deep Interview

Conduct the interview using AskUserQuestion with batched questions (2-4 related questions per batch).

Rules:

  • Never assume - if something is ambiguous, ask
  • Provide sensible default options for every question
  • Add "(Recommended)" to the best default option
  • Anticipate needs the user hasn't considered
  • Adapt question depth based on user's technical level

Question calibration by user background:

TopicTechnical UserNon-Technical User
Architecture"REST vs GraphQL vs tRPC?"Skip - decide yourself
Data storage"SQL vs NoSQL? Which DB?""Does it need to remember data between sessions?"
UI framework"React, Vue, or Svelte?"Skip - decide yourself
Hosting"Serverless, containers, or VMs?"Skip - decide yourself
Auth"OAuth, magic links, or password?""How should users log in?" (plain language options)
Scale"Expected concurrent users?""How many people might use this at once?"

Prioritize load-bearing decisions:

Not all decisions carry equal weight. Some are cheap to revisit later; others are foundational — they shape the core architecture, UX, or physical design and are expensive or painful to redo. The interview must identify which decisions are load-bearing for *this specific product*, surface them, and get explicit user approval before moving on. This applies to all product types.

The test: "If we change this after launch/production, does it require significant rework, data migration, retooling, or redesign?" If yes, it's load-bearing and must be discussed upfront. If not, it can be iterated on later and doesn't need deep interview time.

What counts as load-bearing varies by product. A few examples to calibrate your judgment:

  • Storage & data architecture (software) — where content lives, when uploads happen, what's the source of truth
  • Async processing patterns (software) — how long-running operations are tracked, who polls, how progress is communicated
  • Concurrency model (software) — whether the system is designed for parallel workloads from the start, since retrofitting this is a rewrite
  • Core mechanism & form factor (hardware) — how the thing fundamentally works, its dimensions, power source
  • Manufacturing process & materials (hardware) — injection molding vs CNC vs 3D print changes cost, tolerances, and lead times
  • Regulatory & certification requirements (hardware) — designing for compliance after the fact means costly redesigns
  • UI/UX model (any) — sometimes trivial to change (colors, copy), sometimes load-bearing (e.g., a real-time collaborative interface vs. async workflow, or a feed-based layout that the entire data model is built around)
  • Third-party dependencies (any) — switching providers often means rewriting integrations and handling different data formats
  • Auth & identity model (software/services) — migrating users between auth systems is extremely disruptive

These are examples, not a checklist. For each product, think from first principles about what's "poured in concrete" vs. "rearranging furniture," and focus the interview accordingly.

For technical users, ask about load-bearing decisions directly. For non-technical users, simplify the concept but still get their approval — e.g., instead of "Should the frontend poll via request IDs or use server-sent events?", ask "When a generation takes 30 seconds, should the user see a live progress update or just get notified when it's done?"

Spend interview time proportionally to how hard a decision is to reverse. Don't deep-dive on aspects that are trivially changeable for this product unless the user raises them.

Interview domains to cover (adapt based on product type):

For software products:

  • Core functionality (what does it do?)
  • User types and permissions
  • Key user flows (step by step)
  • Data model (what entities exist, how they relate)
  • Integrations (external services, APIs, fallback strategies)
  • Edge cases and error handling
  • Security and privacy requirements
  • Platform (web, mobile, desktop, CLI)

For physical products:

  • Core functionality
  • User interaction (how do you use it?)
  • Manufacturing considerations
  • Safety requirements
  • Packaging and delivery

For all products:

  • Load-bearing decisions identified for this specific product (see above)

For all products:

  • Success criteria (how do we know it works?)
  • Constraints (budget, timeline, must-haves)
  • Anti-goals (what it explicitly should NOT do)

Example batched question:

AskUserQuestion (batch):
1. "Who are the main users of this product?"
   - Single user type (just me / general public)
   - Two distinct roles (e.g., admin + regular user)
   - Multiple user types (need to define each)

2. "Do users need accounts?"
   - No accounts needed (Recommended for MVP)
   - Simple accounts (email/password)
   - Social login (Google, GitHub, etc.)
   - Enterprise SSO

Phase 3: Draft Review

After covering core questions (~10-15 batches), present a draft outline:

Here's the spec outline based on our conversation so far:

## [Product Name] - Spec Outline

**Overview:** [1-2 sentences]

**Core Features:**
1. [Feature A]
2. [Feature B]
...

**User Types:** [list]

**Key Flows:** [list main workflows]

**Technical Approach:** [high-level decisions]

**Open Questions:** [things still unclear]

---

Does this capture your vision? What's missing or wrong?

Then use AskUserQuestion to get feedback:

1. "How does this outline look?"
   - Looks good, continue with details
   - Missing something important (I'll explain)
   - Some parts are wrong (I'll clarify)
   - Let's pivot direction

Phase 4: Deep Dive

Based on feedback, ask detailed follow-up questions on:

  • Unclear areas from the outline
  • Edge cases and error states
  • Specific UI/UX details
  • Technical constraints
  • Anything marked as "Open Questions"

Continue until confident all ambiguity is resolved.

Phase 5: Write Spec

Write the final spec to spec-<product-name>.md in the current directory.

Spec format principles:

  • Detailed enough for an AI coding agent to implement
  • Skimmable for human review (use headers, bullets, tables)
  • No vague language - every requirement must be concrete
  • Include explicit anti-goals and out-of-scope items
  • Write as a standalone document - never mention the interview process or reference "our conversation"

Spec structure (adapt sections based on product type):

# [Product Name] Spec

## Overview

[2-3 sentences: what it is, who it's for, core value prop]

## Goals & Non-Goals

### Goals

- [Concrete goal 1]
- [Concrete goal 2]

### Non-Goals (explicitly out of scope)

- [What this product will NOT do]

## User Types

[Table or list of user types and their permissions/capabilities]

## Core Features

### Feature 1: [Name]

**Purpose:** [Why this feature exists]
**Behavior:**

- [Specific behavior 1]
- [Specific behavior 2]
  **UI:** [Description or wireframe reference]

[Repeat for each feature]

## User Flows

### Flow 1: [Name]

1. User does X
2. System responds with Y
3. User sees Z
   ...

[Repeat for key flows]

## Data Model

[Tables, entities, relationships - for software]
[Components, materials - for physical products]

## Technical Decisions

[Architecture choices, technologies, integrations]
[Explicitly document every load-bearing decision (storage, async patterns, concurrency, etc.) and note that it was discussed and approved during the interview]
[For non-technical users, describe these in plain language; for physical products, skip]

## Edge Cases & Error Handling

- When X happens, the system should Y
- If Z fails, show error message: "..."

## Open Questions

[Anything that still needs resolution before building]

## Future Considerations

[Ideas mentioned but explicitly deferred]

Key Principles

  1. Load-bearing decisions first - Identify and resolve architectural decisions that are hard to undo before spending time on details that are easy to change later. The interview's primary job is to surface these and get explicit approval.
  2. Be thorough - Ask many questions. 10-20 batches is normal for a complex product.
  3. Never assume - If two interpretations are possible, ask which one.
  4. Provide defaults - Every question should have reasonable options with a recommended choice.
  5. Adapt depth - Technical users get technical questions; non-technical users get plain language. But even non-technical users must approve load-bearing decisions — just explain them simply.
  6. Surface unknowns - Ask about things the user probably hasn't considered yet.
  7. Stay concrete - The final spec should have zero vague requirements.

Self-Note

Before finalizing the spec, ask yourself:

  • "What are the load-bearing decisions I haven't surfaced yet? Is there anything in this spec that would be painful to change once built — and that I haven't explicitly confirmed with the user?"
  • "What are the edge cases I didn't consider?"

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

37.84%
按下载量换算67

Claude

28.05%
按下载量换算50

Cursor

18.65%
按下载量换算33

Gemini CLI

8.63%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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