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task-scaler任务缩放器

Agent Skill

task-scaler 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

259

周安装

11

GitHub Stars

2

下载量

91
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-scaler(任务缩放器)
来源仓库:https://github.com/masanao-ohba/claude-manifests
仓库路径:skills/task-scaler
安装命令:
npx skills add https://github.com/masanao-ohba/claude-manifests --skill task-scaler
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/masanao-ohba/claude-manifests --skill task-scaler

简介

task-scaler 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。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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 Scaler

A technology-agnostic skill for evaluating task complexity and determining appropriate workflow scale.

Core Purpose

Classify tasks into appropriate scale categories to optimize:

  • Agent utilization
  • Workflow complexity
  • Resource allocation
  • Token consumption

Scale Classification

Categories

trivial:
  description: "Minimal changes with obvious implementation"
  characteristics:
    - Single-line or few-line changes
    - Typo fixes, whitespace corrections
    - Simple variable renames
    - Clear, obvious modifications
  metrics:
    lines_of_change: "< 3"
    files_affected: 1
    complexity_score: "< 5"
  workflow:
    direct_execution: true
    agents_required: 0
    rai_required: false
    deliverable_evaluation: false

small:
  description: "Single-component changes with clear scope"
  characteristics:
    - Single function implementation
    - Bug fix in one file
    - Simple feature addition
    - Minor refactoring
  metrics:
    lines_of_change: "3-50"
    files_affected: "1-3"
    complexity_score: "5-14"
  workflow:
    direct_execution: false
    agents_required: "1-2"
    rai_required: false
    deliverable_evaluation: true

medium:
  description: "Multi-component changes requiring coordination"
  characteristics:
    - Multiple function implementations
    - Cross-file changes
    - Feature with multiple components
    - Significant refactoring
  metrics:
    lines_of_change: "50-200"
    files_affected: "3-10"
    complexity_score: "15-29"
  workflow:
    direct_execution: false
    agents_required: "3-4"
    rai_required: true
    deliverable_evaluation: true

large:
  description: "Architectural changes with system-wide impact"
  characteristics:
    - Architecture modifications
    - New module or service
    - Multi-tenant considerations
    - System-wide impact
  metrics:
    lines_of_change: "200+"
    files_affected: "10+"
    complexity_score: "30+"
  workflow:
    direct_execution: false
    agents_required: "minimum needed"
    rai_required: true
    deliverable_evaluation: true
    full_workflow: true

Complexity Scoring

Scoring Factors

factors:
  file_count:
    weight: 2
    calculation: "2 points per affected file"

  dependency_depth:
    weight: 3
    calculation: "3 points per dependency level"

  test_requirement:
    weight: 5
    calculation: "5 points if tests needed"

  user_interaction:
    weight: 3
    calculation: "3 points if user input needed"

  integration_complexity:
    weight: 4
    calculation: "4 points per external integration"

  database_changes:
    weight: 5
    calculation: "5 points if schema changes"

Score Thresholds

thresholds:
  trivial: "score < 5"
  small: "5 <= score < 15"
  medium: "15 <= score < 30"
  large: "score >= 30"

Classification Algorithm

Step 1: Initial Classification

Parse user request for indicators:

trivial_indicators:
  keywords:
    - "typo", "fix typo", "correct spelling"
    - "whitespace", "formatting"
    - "single line", "one line"
  patterns:
    - Change target is explicit and simple
    - No logic changes required

small_indicators:
  keywords:
    - "add function", "implement method"
    - "fix bug", "resolve issue"
    - "single component", "one file"
  patterns:
    - Single component scope
    - Clear implementation path

medium_indicators:
  keywords:
    - "add feature", "implement"
    - "multiple components", "create tests"
    - "refactor"
  patterns:
    - Multiple files affected
    - Testing required

large_indicators:
  keywords:
    - "architecture", "system"
    - "new module", "new service"
    - "multi-tenant", "system-wide"
  patterns:
    - Architectural decisions needed
    - Broad impact scope

Step 2: Complexity Analysis

Analyze for complexity factors:
  1. Count estimated files affected
  2. Assess dependency depth
  3. Determine test requirements
  4. Identify user interaction needs
  5. Check for external integrations
  6. Evaluate database impact
  7. Calculate total score

Step 3: Context Adjustments

adjustments:
  scale_up_if:
    - "High integration with existing code"
    - "Ambiguous requirements"
    - "Multiple valid approaches"
    - "Security implications"

  scale_down_if:
    - "Established pattern/template exists"
    - "User provided detailed instructions"
    - "Similar change done recently"
    - "Well-documented requirements"

Output Format

scale_evaluation:
  task_scale: trivial|small|medium|large
  complexity_score: <number>

  factors:
    file_count: <number>
    dependency_depth: <number>
    test_required: true|false
    user_interaction: true|false
    integrations: <number>
    database_changes: true|false

  reasoning:
    initial_classification: "<based on keywords/patterns>"
    complexity_analysis: "<factor breakdown>"
    context_adjustments: "<any scale changes>"

  workflow_recommendation:
    agents_required: <number or range>
    rai_required: true|false
    parallel_possible: true|false
    estimated_iterations: <number>

Anti-Fragmentation Principles

Minimal Agent Usage

principle: "Use minimum agents needed for task"

by_scale:
  trivial: "Direct execution, no agents"
  small: "1-2 agents maximum"
  medium: "3-4 agents, batch similar work"
  large: "Minimum needed, maximize parallel"

anti_pattern: "Using 5+ agents for every task"

Purposeful Delegation

valid_delegation_reasons:
  - "Specialized skill required"
  - "Session isolation needed"
  - "Parallel processing benefit"

invalid_delegation_reasons:
  - "Just in case"
  - "For confirmation"
  - "Protocol says so"

Batch Similar Tasks

batching_principle: "Group similar tasks for single agent"

example:
  bad: "3 file fixes → 3 separate agents"
  good: "3 file fixes → 1 agent batch"

Integration

Used By

primary_users:
  - task-scale-evaluator: "Core skill for scale assessment"
  - "/dev-workflow command": "Workflow routing decisions"

Best Practices

  1. Be Conservative: When uncertain, scale up
  2. Consider Context: Same task varies by codebase familiarity
  3. Avoid Over-Engineering: Match workflow to actual complexity
  4. Review History: Similar tasks inform classification
  5. Account for Risk: Security/data tasks scale up

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

35.05%
按下载量换算32

Claude

28.52%
按下载量换算26

Cursor

18.44%
按下载量换算17

Gemini CLI

9.94%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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