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Agent Skill · 开发

task-scaler(任务缩放器)

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。

task-scaler 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 19,716。

总安装 259周安装 11GitHub Stars 2下载 91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: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

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

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。

来源仓库内路径:skills/task-scaler

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/masanao-ohba/claude-manifests --skill task-sca…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

task-scaler 怎么安装?

在终端执行 npx skills add https://github.com/masanao-ohba/claude-manifests --skill task-scaler。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

task-scaler 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。

task-scaler 需要什么权限?

权限级别是只读。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。

task-scaler 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。

task-scaler 用的人多吗?

公开数据是累计安装 259 次,近一周安装 11 次,下载 91 次,GitHub 2 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。

task-scaler 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
task-scaler

任务缩放器 · 本页

2592只读未明确通过

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

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

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。目前只有一个公开来源,建议同时核对源仓库。

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