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Agent Skill · 前端设计

generate-tasks(生成任务)

generate-tasks 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。

generate-tasks 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 8,606。

总安装 272周安装 11GitHub Stars 8下载 85核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:generate-tasks(生成任务)
来源仓库:https://github.com/ar4mirez/samuel
仓库路径:skills/generate-tasks
安装命令:
npx skills add https://github.com/ar4mirez/samuel --skill generate-tasks
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/ar4mirez/samuel --skill generate-tasks

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

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 中围绕仓库状态或代码变更时使用。通过 GitHub 安装,使用 npx skills add 命令添加技能。需确认权限范围和维护状态,注意是否触发联网或文件读写。

generate-tasks 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/generate-tasks

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/ar4mirez/samuel --skill generate-tasks;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

generate-tasks 怎么安装?

在终端执行 npx skills add https://github.com/ar4mirez/samuel --skill generate-tasks。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

generate-tasks 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

generate-tasks 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

generate-tasks 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

generate-tasks 用的人多吗?

公开数据是累计安装 272 次,近一周安装 11 次,下载 85 次,GitHub 8 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

generate-tasks 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
generate-tasks

生成任务 · 本页

2728接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Skill: Generate Task List from PRD

Overview

Guide AI in creating detailed, step-by-step task list in Markdown format based on existing PRD. The task list should guide a developer through implementation with built-in quality checkpoints.

Use When: PRD created, ready to plan implementation 4D Phase: Develop (planning implementation steps)

Prerequisites

✅ Before using this skill:

  • PRD exists in .claude/tasks/NNNN-prd-feature-name.md
  • PRD reviewed and approved by user
  • CLAUDE.md exists (or will be created during this process)
  • Current codebase reviewed for context

Process Overview

Two-Phase Approach

Phase 1: Generate parent tasks → Present to user → Wait for "Go" Phase 2: Generate detailed sub-tasks → Save file → Inform user

This ensures high-level plan approved before diving into details.

Phase 1: Analysis & High-Level Planning

Step 1: Receive PRD Reference

User points AI to specific PRD file.

Example:

"Generate tasks for @.claude/tasks/0001-prd-user-authentication.md using @.claude/skills/generate-tasks/SKILL.md"

Step 2: Read PRD

AI reads and analyzes:

  • Functional requirements
  • User stories
  • Technical considerations
  • Guardrails affected
  • Success metrics

Step 3: Assess Current State

CRITICAL: Review existing codebase before planning.

AI must:

  1. Check CLAUDE.md for:

- Tech stack and framework choices - Existing architecture patterns - Testing framework and conventions - Deployment configuration

  1. Scan codebase for:

- Existing directory structure - Similar features (auth, API routes, etc.) - Naming conventions - Test file patterns - Component organization

  1. Identify reusable components:

- Existing utilities that can be leveraged - UI components from design system - Database connection patterns - Error handling patterns - Validation utilities

  1. Check guardrails compliance:

- Current file lengths (don't add to files approaching limits) - Existing test coverage patterns - Security patterns in use - Performance considerations

Output: Brief summary for user review before generating tasks.

Example:

## Current State Assessment

**Existing Infrastructure:**
- Express.js backend with TypeScript
- Prisma ORM with PostgreSQL
- Jest for testing (78% coverage currently)
- React frontend with Tailwind CSS

**Reusable Components:**
- `lib/validation.ts` - Input validation utilities (use Zod)
- `components/Form.tsx` - Form component (reuse for auth forms)
- `lib/errors.ts` - Centralized error handling (extend for auth errors)

**Patterns to Follow:**
- API routes in `src/api/<feature>/` structure
- Tests alongside code (`*.test.ts`)
- Repository pattern for data access
- Environment variables via `.env` with fallbacks

**Guardrails Note:**
- `src/api/user/user.controller.ts` is 285 lines (near 300 limit - don't modify)
- Create new auth module instead of extending user module

Step 4: Generate Parent Tasks

Based on PRD and current state, create high-level tasks.

Guidelines:

  • Typically 4-7 parent tasks
  • Each parent = logical phase of implementation
  • Order by dependency (database → backend → frontend → tests)
  • Include setup, implementation, testing, documentation

Save to: .claude/tasks/tasks-NNNN-prd-feature-name.md

Present to user in this format:

## High-Level Tasks

- [ ] 1.0 Database Schema & Migrations
- [ ] 2.0 Backend Authentication Service
- [ ] 3.0 API Routes & Middleware
- [ ] 4.0 Frontend Auth Components
- [ ] 5.0 OAuth Integration
- [ ] 6.0 Testing & Validation
- [ ] 7.0 Documentation & Deployment

**Relevant Files** (identified):
- `src/db/schemas/user.schema.ts` - Create
- `src/auth/auth.service.ts` - Create
- `src/api/auth/auth.controller.ts` - Create
... (full list in task file)

Then inform user:

"High-level tasks generated. Review the task structure above.
Ready to generate detailed sub-tasks? Respond with 'Go' to proceed."

Step 5: Wait for User Confirmation

PAUSE - Do not proceed until user responds with "Go" or equivalent.

This checkpoint ensures high-level plan aligns with expectations before diving into details.


Phase 2: Detailed Task Breakdown

Step 6: Generate Sub-Tasks

Once user confirms "Go", break down each parent task into actionable sub-tasks.

Sub-task Guidelines:

  1. Granularity: Each sub-task = 30-60 min of work (3,000-6,000 tokens)
  2. Atomicity: Complete in one session, one commit
  3. Testability: Each sub-task includes verification step
  4. Dependency: Clear which sub-tasks depend on others
  5. Guardrails: Each task validates against relevant guardrails

Sub-task Naming Convention:

  • Use present tense verbs: "Create", "Implement", "Add", "Update", "Test"
  • Be specific: "Create user schema" not "Setup database"
  • Include success criterion: "Create user schema (with email, password_hash, created_at fields)"

Example Parent → Sub-tasks:

- [ ] 1.0 Database Schema & Migrations
  - [ ] 1.1 Create user schema with Prisma (email, password_hash, oauth_provider, created_at, updated_at)
  - [ ] 1.2 Create session schema with Prisma (user_id FK, token, expires_at, created_at)
  - [ ] 1.3 Generate and run Prisma migration for user and session tables
  - [ ] 1.4 Verify schema in PostgreSQL (check indexes, constraints)
  - [ ] 1.5 Update .env.example with DATABASE_URL placeholder

Step 7: Add Guardrail Validation Per Task

For each sub-task, identify which guardrails must be validated.

Format: Add guardrail checklist as comment under relevant tasks.

Example:

- [ ] 2.3 Implement password hashing service (bcrypt, cost factor 12)
  <!-- Guardrails:
    ✓ All environment variables have secure defaults (BCRYPT_ROUNDS=12)
    ✓ All exported functions have type signatures and JSDoc
    ✓ Function ≤50 lines (hash, verify, validate strength as separate functions)
    ✓ Edge cases tested (null, empty, too short, invalid chars)
  -->

Step 8: Add Complexity Estimates

For each sub-task, estimate complexity in tokens (not time).

Complexity Levels:

  • Simple: <2,000 tokens (single function, straightforward logic)
  • Medium: 2,000-5,000 tokens (multiple functions, some complexity)
  • Complex: 5,000-10,000 tokens (multiple files, integration, edge cases)

Example:

- [ ] 2.3 Implement password hashing service [~3,000 tokens - Medium]

Step 9: Identify Relevant Files

List all files that will be created or modified, including tests.

Format: See references/process.md for detailed file listing format.

Step 10: Add Implementation Notes

Provide guidance for each task section.

Include:

  • Testing requirements specific to this feature
  • Performance considerations
  • Security reminders
  • Integration points
  • Common pitfalls to avoid

Example:

## Implementation Notes

### Testing Requirements
- All auth logic must have >95% coverage (business-critical)
- Test files alongside code (e.g., `auth.service.ts` + `auth.service.test.ts`)
- Use `npm test src/auth` to run auth module tests only
- Integration tests must test actual JWT validation, not mocks

### Security Checklist (verify before each commit)
- [ ] All passwords hashed (never store plain text)
- [ ] All JWT secrets from environment variables
- [ ] All inputs validated with Zod schemas
- [ ] All SQL queries parameterized (use Prisma)
- [ ] Rate limiting on login/register endpoints
- [ ] Authentication events logged

### Performance Targets
- Login/register: <200ms (p95)
- JWT validation: <50ms (p95)
- Password hashing: 100-300ms (bcrypt cost 12)

### Common Pitfalls
- ❌ Don't validate JWT in database query (use middleware)
- ❌ Don't store tokens in localStorage (use httpOnly cookies)
- ❌ Don't return sensitive data in error messages
- ❌ Don't skip rate limiting (prevents brute force)

Step 11: Generate Final Output

Combine all sections into final task list structure.

See references/process.md for complete output format template.


Output Location

File: .claude/tasks/tasks-NNNN-prd-feature-name.md Format: Markdown Naming: Match PRD filename (e.g., PRD is 0001-prd-user-auth.md → tasks are tasks-0001-prd-user-auth.md)


Implementation Workflow

During Implementation

User will say: "Start on task 1.1" or "Continue with next task"

AI should:

  1. Implement the specific sub-task
  2. Write tests alongside code
  3. Validate against guardrails in task comment
  4. Run tests
  5. Verify success criteria
  6. Commit with message: feat(auth): task 1.1 - create user schema
  7. Mark task complete in task list file
  8. Ask: "Task 1.1 complete. Review changes? Or continue with 1.2?"

Guardrails Integration

For each sub-task, identify which of CLAUDE.md's 30+ guardrails apply.

Common guardrails per task type:

Database Schema Tasks

  • ✓ All environment variables have secure defaults
  • ✓ Parameterized queries (Prisma enforces this)

Service/Logic Tasks

  • ✓ No function exceeds 50 lines
  • ✓ Cyclomatic complexity ≤10 per function
  • ✓ All exported functions have type signatures and JSDoc
  • ✓ All user inputs validated before processing

API/Route Tasks

  • ✓ All API boundaries have input validation (Zod schemas)
  • ✓ All async operations have timeout/cancellation
  • ✓ API responses <200ms for simple queries

Test Tasks

  • ✓ Coverage targets: >95% for auth (business-critical)
  • ✓ All public APIs have unit tests
  • ✓ Edge cases tested (null, empty, boundary values)
  • ✓ No test interdependencies

Security Tasks (Auth-related)

  • ✓ All user inputs validated before processing
  • ✓ All database queries parameterized
  • ✓ All environment variables have secure defaults
  • ✓ Dependencies checked for vulnerabilities

Final Instructions

For AI Assistant:

Phase 1 (High-Level):

  1. ✅ Read PRD thoroughly
  2. ✅ Assess current codebase (scan for existing patterns)
  3. ✅ Generate 4-7 parent tasks
  4. ✅ Identify all relevant files
  5. ✅ Present to user
  6. ❌ WAIT for "Go" before Phase 2

Phase 2 (Detailed):

  1. ✅ Break each parent into 3-8 sub-tasks
  2. ✅ Add guardrail validation per task
  3. ✅ Add complexity estimates
  4. ✅ Add implementation notes
  5. ✅ Save task list file
  6. ✅ Inform user: "Task list ready at [path]. Ready to start task 1.1?"

During Implementation:

  1. ✅ Implement one sub-task at a time
  2. ✅ Validate guardrails for that specific task
  3. ✅ Write tests alongside code
  4. ✅ Commit after each task (atomic commits)
  5. ✅ Update task list (mark completed)
  6. ✅ Ask user to review before continuing

Target Audience

Primary: Junior developer with AI assistance Assumption: Developer knows fundamentals but needs clear guidance on:

  • Where to create files
  • What patterns to follow
  • Which guardrails to validate
  • How to test properly

References

See references/process.md for:

  • Complete output format template
  • Detailed file listing examples
  • Extended implementation notes
  • Full task breakdown examples

Remember: Good task breakdown = clear roadmap. Each sub-task should be:

  • Atomic: Complete in one session
  • Testable: Has clear success criterion
  • Guarded: Validates specific guardrails
  • Estimated: Complexity known upfront
  • Dependent: Dependencies clear (order matters)

Autonomous Execution (Optional)

If the project uses autonomous AI coding loops (Ralph Wiggum methodology), the generated task list can be converted to machine-readable format for unattended execution.

After generating the markdown task list:

samuel auto init --prd .claude/tasks/NNNN-prd-feature.md

This converts the PRD and task list into .claude/auto/prd.json and generates the loop orchestration files. See .claude/skills/auto/SKILL.md for the full autonomous workflow.

平台分布

Codex

35.03%
按下载量换算30

Claude

31.2%
按下载量换算27

Cursor

19.66%
按下载量换算17

Gemini CLI

8.73%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。目前只有一个公开来源,建议同时核对源仓库。

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