Token导航 LogoToken导航TokenDH.com
研究检索权限需确认github未标认证来源可访问许可证需确认审计通过

task-decomposer任务分解器

Agent Skill

task-decomposer 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

1,435

周安装

61

GitHub Stars

98

下载量

503
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:task-decomposer(任务分解器)
来源仓库:https://github.com/erichowens/some_claude_skills
仓库路径:skills/task-decomposer
安装命令:
npx skills add https://github.com/erichowens/some_claude_skills --skill task-decomposer
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/erichowens/some_claude_skills --skill task-decomposer

简介

将自然语言问题分解为适合 DAG 节点的子任务的专业工具。

  • 识别任务阶段、依赖关系和并行化处理机会,优化执行顺序。
  • 区分具体与模糊子任务,为后续技能分配做准备。task-decomposer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 是构建执行流程图的第一步,确保复杂任务的可管理性。
  • 使用时需提供足够详细的问题描述,便于准确切分子任务边界。

SKILL.md

Task Decomposer

Breaks natural-language problems into sub-tasks suitable for DAG nodes. The first step of the meta-DAG: before you can build or execute a DAG, you need to understand what the pieces are.


When to Use

Use for:

  • Breaking a vague problem into concrete sub-tasks
  • Identifying phases, dependencies, and parallelization opportunities
  • Determining which sub-tasks are concrete vs. vague (pluripotent)
  • Selecting the appropriate domain meta-skill for decomposition

NOT for:

  • Building the DAG structure from sub-tasks (use dag-planner)
  • Executing the tasks (use dag-runtime)
  • Assigning skills to tasks (use dag-skills-matcher)

Decomposition Process

flowchart TD
  P[Problem description] --> M{Domain meta-skill available?}
  M -->|Yes| L[Load meta-skill phase pattern]
  M -->|No| R{Research needed?}
  R -->|Yes| RA[Research standard decomposition]
  R -->|No| Z[Zero-shot decomposition]

  L --> D[Apply phase pattern to problem]
  RA --> D
  Z --> D

  D --> C[Identify concrete sub-tasks]
  D --> V[Identify vague/pluripotent sub-tasks]
  D --> DEP[Map dependencies between sub-tasks]
  D --> PAR[Identify parallelization opportunities]

  C --> O[Ordered sub-task list with metadata]
  V --> O
  DEP --> O
  PAR --> O

Step 1: Domain Detection

Classify the problem into a domain to select the right meta-skill:

Domain SignalsMeta-Skill
"build", "implement", "code", "app", "website"software-project-decomposition
"research", "analyze", "report", "synthesize"research-synthesis-decomposition
"design", "UI", "wireframe", "prototype"product-design-decomposition
"strategy", "market", "business", "revenue"business-strategy-decomposition
"data", "model", "train", "predict"ml-project-decomposition

If no meta-skill matches, fall back to zero-shot decomposition.

Step 2: Phase Identification

Apply the meta-skill's phase pattern. Not all phases apply to every problem.

Decision: For each phase in the pattern, ask: "Does this problem need this phase?"

  • Yes, and I can specify it now → Concrete sub-task
  • Yes, but I can't specify it until prior phases complete → Vague/pluripotent node
  • No → Skip this phase

Step 3: Sub-Task Specification

For each concrete sub-task:

sub_task:
  id: unique-name
  description: "What this sub-task produces (1-2 sentences)"
  type: concrete | vague
  depends_on: [upstream-sub-task-ids]
  parallelizable_with: [sibling-sub-task-ids]
  estimated_complexity: simple | moderate | complex
  suggested_model_tier: 1 | 2 | 3
  suggested_skills: [skill-names if known]
  output_description: "What the output looks like"

For each vague/pluripotent sub-task:

sub_task:
  id: unique-name
  description: "What this phase will address (1-2 sentences)"
  type: vague
  depends_on: [upstream-sub-task-ids]
  potential_paths:
    - "Path A: [exciting possibility 1]"
    - "Path B: [exciting possibility 2]"
    - "Path C: [exciting possibility 3]"
  expansion_trigger: on_upstream_complete

Step 4: Dependency Mapping

For each pair of sub-tasks, determine:

  • Data dependency: Does B need A's output? → Edge from A to B
  • Knowledge dependency: Does B need to know what A discovered? → Edge from A to B
  • No dependency: A and B are independent → Parallelizable

Step 5: Output

Produce a structured decomposition:

decomposition:
  problem: "original problem description"
  domain: "detected domain"
  meta_skill_used: "meta-skill name or 'zero-shot'"
  phases:
    - phase: 1
      sub_tasks: [concrete tasks for this phase]
    - phase: 2
      sub_tasks: [mix of concrete and vague tasks]
  total_concrete: 5
  total_vague: 3
  estimated_waves: 4
  estimated_cost: "$0.08 - $0.25"

Decomposition Heuristics

Granularity

  • Too fine: "Step 1: Open the file. Step 2: Read line 1." → Merge into one node
  • Too coarse: "Step 1: Build the entire app." → Split into design, implement, test, deploy
  • Right: Each sub-task is completable by one agent with 1-3 skills in one LLM call

Dependency Minimization

Fewer dependencies = more parallelism = faster execution. Prefer:

  • Independent parallel tracks over long sequential chains
  • Fan-out patterns (one source, many consumers) over daisy chains
  • Late merging (combine results at the end, not incrementally)

Vagueness is OK

Don't force specificity where it doesn't exist yet. A vague node saying "Build the solution (details TBD after design phase)" is more honest and more useful than a fake-specific node that will be wrong.


Anti-Patterns

Premature Specificity

Wrong: Specifying exact implementation details for phases that depend on undone research. Right: Mark dependent phases as vague/pluripotent. Show potential paths.

Sequential Everything

Wrong: A linear chain of 10 tasks with no parallelism. Right: Look for independent tracks. Research and content writing can often happen in parallel.

Missing the Meta-Skill

Wrong: Decomposing a bridge design project like a software project. Right: Detect the domain, load the appropriate meta-skill, follow its phase pattern.

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

36.62%
按下载量换算184

Claude

28.6%
按下载量换算144

Cursor

18.19%
按下载量换算91

Gemini CLI

9.67%
按下载量换算49

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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

继续浏览同类 Skills