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specvibespecvibe 开发

Agent Skill

specvibe 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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

12,617

周安装

531

GitHub Stars

公开资料未说明

下载量

4,418
OpenClaw

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

MIT-0

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:specvibe(specvibe 开发)
来源仓库:https://github.com/badideal-2046/specvibe
安装命令:
openclaw skills install specvibe
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install specvibe

简介

构建生产级AI原生应用的规范驱动开发完整解决方案。

  • 内置最佳实践模板确保代码质量与架构可扩展性。specvibe 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 适用于从零开始的新项目开发全过程指导与约束管理。
  • 在OpenClaw中直接调用生成标准化前端组件与样式规范。
  • 使用前请仔细阅读文档确认是否符合团队技术栈要求。

SKILL.md

name
SpecVibe
description
A world-class, spec-driven development framework for building production-ready, AI-native applications. Use for any new project to ensure adherence to the most advanced 2026 best practices in architecture, security, testing, and deployment.

SpecVibe: The AI-Native Development Framework

This skill provides a universal, seven-stage framework for developing production-ready, AI-native applications. It enforces a "Specification-as-Source-of-Truth" mindset, ensuring that every aspect of the project is defined, testable, secure, and documented before and during implementation, following the most advanced 2026 community best practices from Google, GitHub, and Thoughtworks.

Core Philosophy

  • Intent is the Source of Truth: The specification (spec.md) is the primary artifact. Code is the last-mile implementation of that intent.
  • Human-AI Collaboration: Follow the Delegate/Review/Own model at every stage to maximize efficiency and maintain quality.
  • Iterate in Small, Validated Chunks: Break down work into the smallest possible units, test them, and commit frequently. Never let the AI generate large, monolithic blocks of code.
  • Automate Everything: Use tests, linters, CI/CD, and automated documentation to build a robust quality assurance system.

The Seven Stages of AI-Native Development

Follow these stages sequentially. Each stage has a Quality Gate—a set of questions you must answer before proceeding—and a clear Delegate/Review/Own model for human-AI collaboration.

StageFocusKey ActivitiesReference Guides
1. SpecifyUser Journey & RequirementsCreate spec.md defining user stories, goals, and non-functional requirements.references/00-specvibe.md
2. PlanTechnical ArchitectureCreate PLAN.md, select tech stack, define architecture, and break down the spec into tasks.references/02-backend.md, references/03-frontend.md
3. TestBehavior-Driven DefinitionWrite failing unit, integration, and E2E tests based on the spec and plan.references/05-testing.md
4. ImplementCode Generation & RefinementWrite (or generate) code to make the tests pass, following a chunked iteration strategy.references/08-ai-collaboration.md
5. ReviewQuality & Security AssuranceConduct automated and human code reviews, focusing on security, logic, and maintainability.references/04-security.md
6. DocumentKnowledge CaptureAutomatically generate and manually refine user and developer documentation.references/09-documentation.md
7. DeployCI/CD & ObservabilityContainerize, set up CI/CD pipelines, and implement full observability.references/06-devops.md, references/07-error-handling.md

Stage 1: Specify - The Intent

Goal: Define *what* to build and *why* in a structured spec.md.

  • Delegate: Ask the AI to interview you about the project goals and generate a draft spec.md using the templates/spec-template.md.
  • Review: Check if the spec accurately captures all user stories, edge cases, and success metrics.
  • Own: The final approval of the user requirements and business goals.

Quality Gate 1: Specification Review

  • *Does the spec.md clearly define the user, their problem, and the proposed solution?*
  • *Are non-functional requirements (performance, security, accessibility) listed?*
  • *Is the scope well-defined and unambiguous for an AI to understand?*

Stage 2: Plan - The Blueprint

Goal: Translate the spec.md into a concrete technical plan.

  • Delegate: Feed spec.md to the AI and ask it to generate a PLAN.md detailing the architecture, data models (using references/01-schema-and-types.md), API contracts (using templates/openapi-template.yaml), and a task breakdown.
  • Review: Assess the proposed tech stack, architecture, and task list for feasibility and alignment with best practices.
  • Own: The final architectural decisions and technology choices.

Quality Gate 2: Plan Review

  • *Is the chosen architecture appropriate for the project's scale and requirements?*
  • *Is the API contract complete and consistent with the data models?*
  • *Are the tasks small, independent, and logically sequenced?*

Stage 3: Test - The Safety Net

Goal: Define the application's behavior through a comprehensive, failing test suite.

  • Delegate: Ask the AI to generate a full suite of tests (unit, integration, E2E) based on spec.md and PLAN.md. Refer to references/05-testing.md.
  • Review: Ensure tests cover all user stories, API endpoints, and critical business logic. Check for meaningful assertions.
  • Own: The definition of "done" for each feature, as represented by the tests.

Quality Gate 3: Test Suite Review

  • *Does every feature in the spec have corresponding tests?*
  • *Do all tests currently fail for the correct reasons?*

Stage 4: Implement - The Engine Room

Goal: Write clean, efficient code that makes all tests pass.

  • Delegate: Instruct the AI to implement one task at a time, feeding it the relevant spec, plan, and failing test. Use the "chunked iteration" strategy from references/08-ai-collaboration.md.
  • Review: After each small chunk, review the generated code for correctness and style. Do not wait for the entire feature to be complete.
  • Own: The responsibility for committing each validated chunk of code to version control.

Quality Gate 4: Implementation Review

  • *Do all tests for the implemented task now pass?*
  • *Is the code clean, readable, and consistent with the project's style guide?*
  • *Has the change been committed to Git with a clear message?*

Stage 5: Review - The Quality Shield

Goal: Ensure the implemented code is secure, robust, and maintainable.

  • Delegate: Automate security scans (SAST, DAST, dependency checking) in CI. Use an AI agent to perform a preliminary code review based on references/04-security.md (OWASP 2025).
  • Review: A human developer must perform a final review, focusing on logic, architecture, and subtle bugs that AI might miss.
  • Own: The final approval (LGTM) to merge the code into the main branch.

Quality Gate 5: Code Review

  • *Does the code pass all automated security and quality checks?*
  • *Has a human engineer reviewed and approved the changes?*

Stage 6: Document - The Knowledge Base

Goal: Create clear, comprehensive documentation for both users and developers.

  • Delegate: Use AI to generate initial drafts of API documentation from the OpenAPI spec, and user guides from the spec.md. Refer to references/09-documentation.md.
  • Review: Edit the AI-generated content for clarity, accuracy, and tone. Add diagrams and examples.
  • Own: The final, published documentation that serves as the official source of information.

Quality Gate 6: Documentation Review

  • *Is the API documentation accurate and complete?*
  • *Is the user guide easy for a non-technical person to understand?*

Stage 7: Deploy - The Launchpad

Goal: Automate deployment and ensure the application is observable and reliable in production.

  • Delegate: Ask the AI to generate Dockerfiles, CI/CD pipeline configurations (e.g., GitHub Actions), and infrastructure-as-code scripts. Refer to references/06-devops.md.
  • Review: Verify the deployment scripts, container configurations, and monitoring setup (references/07-error-handling.md).
  • Own: The production environment and the ultimate responsibility for uptime and reliability.

Quality Gate 7: Production Readiness Review

  • *Can the application be deployed and rolled back with a single command?*
  • *Is comprehensive, structured logging (OpenTelemetry) in place?*
  • *Are alerting and monitoring configured for key performance indicators?*

适合场景

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

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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

OpenClaw

81.94%
按下载量换算3,620

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通过

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