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task-decomposition任务分解

Agent Skill

task-decomposition 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

9,949

周安装

423

GitHub Stars

69

下载量

3,486
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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

3

许可证

MIT

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:task-decomposition(任务分解)
来源仓库:https://github.com/jwynia/agent-skills
仓库路径:skills/task-decomposition
安装命令:
npx skills add https://github.com/jwynia/agent-skills --skill task-decomposition
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/jwynia/agent-skills --skill task-decomposition

简介

task-decomposition 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于根据关键词、任务场景或来源线索进行信息筛选。
  • 通过 npx skills add 命令从 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态及是否触发联网或文件操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Task Decomposition Diagnostic

Transform overwhelming development tasks into manageable units by respecting cognitive limits, creating clear boundaries, and enabling parallel work. Tasks properly decomposed achieve 3x higher completion rates and 60% fewer defects.

When to Use This Skill

Use this skill when:

  • A task feels too big to estimate
  • Unsure where to start
  • Blocked by dependencies
  • Task keeps growing (scope creep)
  • Need to break down an epic or feature

Do NOT use this skill when:

  • Task is already small and clear
  • Doing implementation work
  • Architecture decisions needed (use system-design)

Core Principle

The goal isn't more tasks—it's the right tasks. Tasks small enough to understand completely, large enough to deliver value, independent enough to avoid blocking.

Quick Reference: Cognitive Limits

LimitThresholdImplication
Working memory7±2 itemsMax concepts per task
Context switch recovery23 minutesMinimize task switching
Files examined15-20 maxBound task scope
Days before completion drops2-3 daysKeep tasks under this

Task Duration Success Rates

DurationCompletion Rate
< 2 hours95%
2-4 hours90%
4-8 hours (1 day)80%
2-3 days60%
1 week35%
> 2 weeks<10%

Diagnostic States

TD1: Too Big to Understand

Symptoms: Estimates range wildly, can't hold all requirements in mind, more than 7 concepts to track

Interventions:

  • Apply INVEST criteria: Independent, Negotiable, Valuable, Estimable, Small, Testable
  • Use vertical slicing (each slice is independently deployable)
  • Apply walking skeleton (minimal end-to-end first)

TD2: No Clear Entry Point

Symptoms: Multiple valid starting points, paralysis, everything seems connected

Interventions:

  • Front-load risk: start with highest-uncertainty items
  • Tracer bullet: minimal proof of concept
  • Find the walking skeleton: thinnest slice through all layers

TD3: Dependency Problems

Symptoms: "Blocked on X", diamond dependencies, coordination overhead

Interventions:

  • Interface contracts: define API, mock while implementing
  • Feature flags: deploy independently, enable when ready
  • Branch by abstraction: create layer, swap implementations

TD4: No Clear Done Criteria

Symptoms: "Almost done" forever, no way to verify completion

Interventions:

  • Define acceptance criteria (Given/When/Then)
  • Time-box to force prioritization
  • Define explicit out-of-scope items

TD5: Scope Creep

Symptoms: Task keeps growing, "while we're here" additions

Interventions:

  • Freeze scope, spawn new tasks for additions
  • Define minimum viable version
  • Ship smallest version that solves the problem

TD6: Need Spike First

Symptoms: Estimate variance > 4x, new technology, multiple approaches

Interventions:

  • Time-boxed spike (8 hours max)
  • Deliverables: options, POC, trade-offs, revised estimate
  • Spike then implement pattern

Decomposition Patterns

Vertical Slicing (Preferred for Features)

Feature: User Profile Management

Slice 1: View basic profile (4h)
  - UI: Profile display
  - API: GET /profile
  - DB: Read profile

Slice 2: Edit profile name (6h)
  - UI: Edit dialog
  - API: PATCH /profile/name
  - DB: Update profile

Each slice is independently deployable

Walking Skeleton (For New Systems)

Minimal end-to-end first:
1. Hello World page
2. One GET endpoint
3. Single table
4. Basic deploy

Then flesh out incrementally

Tracer Bullet (Validate Architecture)

Step 1: Minimal Service A (1h) - Hardcoded response
Step 2: Minimal Service B (1h) - Simple transformation
Step 3: Integrate (2h) - Prove they communicate

Total: 4 hours to decision point

Estimation Techniques

Complexity Sizing (Fibonacci)

PointsMeaning
1Trivial, < 1 hour
2Simple, 1-2 hours
3Standard, 2-4 hours
5Moderate, 4-8 hours
8Complex, 1-2 days
13Very complex, 2-3 days
21Too large, must decompose

Three-Point Estimation

O = Optimistic (everything perfect)
L = Likely (normal case)
P = Pessimistic (major issues)

PERT estimate: (O + 4L + P) / 6

Anti-Patterns

Big Bang Delivery

Building complete system before any delivery. Fix: Vertical slices, incremental value.

Technical Tasks Without Value

"Set up database," "Create service layer." Fix: Include in feature tasks: "User can view products (includes DB)."

Research Forever

Unbounded investigation. Fix: Time-boxed spikes with deliverables.

Perfect Decomposition

Over-analyzing before starting. Fix: Decompose next 2 weeks. Details for later work emerge.

Decomposition Checklist

Before starting any task:

  • Can hold all requirements in working memory?
  • Duration under 2-3 days?
  • Clear acceptance criteria exist?
  • Dependencies identified and broken where possible?
  • Can be completed independently?
  • Delivers verifiable value?
  • Estimate confidence is high?

If any "no" → further decomposition needed.

Related Skills

  • github-agile - Track decomposed work as issues
  • system-design - Understand architectural boundaries
  • requirements-analysis - Clarify unclear requirements
  • code-review - Review after implementation

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

29.04%
按下载量换算1,012

OpenCode

24.5%
按下载量换算854

Gemini CLI

15.76%
按下载量换算549

Cursor

12.5%
按下载量换算436

Antigravity

7.08%
按下载量换算247

Codex

2.98%
按下载量换算104

安全审计

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/jwynia/agent-skills --skill task-decomposition;npx skills add jwynia/agent-skills --skill "task-decomposition" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

来源信息

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