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Agent Skill · 研究检索

prioritization-methods(优先排序方法)

prioritization-methods 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。

prioritization-methods 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 56,324。

总安装 456周安装 19GitHub Stars 61下载 152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
MIT

安装方式

通过对话安装

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请帮我安装这个 Agent Skill:prioritization-methods(优先排序方法)
来源仓库:https://github.com/melodic-software/claude-code-plugins
仓库路径:skills/prioritization-methods
安装命令:
npx skills add https://github.com/melodic-software/claude-code-plugins --skill prioritization-methods
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/melodic-software/claude-code-plugins --skill prioritization-methods

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

prioritization-methods 用于查找、检索和筛选相关信息,适合优先级相关方法的研究。适用于需要快速获取候选资料以支持决策的场景,可按关键词或主题进行筛选。通过 npx skills add 命令从指定 GitHub 仓库安装,建议查阅原始文档了解覆盖范围。

使用前应评估权限与网络访问需求,避免误用或越权操作。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/prioritization-methods

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/melodic-software/claude-code-plugins --skill p…;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

prioritization-methods 怎么安装?

在终端执行 npx skills add https://github.com/melodic-software/claude-code-plugins --skill prioritizat…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

prioritization-methods 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。

prioritization-methods 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。

prioritization-methods 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

prioritization-methods 用的人多吗?

公开数据是累计安装 456 次,近一周安装 19 次,下载 152 次,GitHub 61 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。

prioritization-methods 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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 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

  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
prioritization-methods

优先排序方法 · 本页

45661只读MIT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

Requirements Prioritization Methods

Comprehensive guide to prioritization techniques for requirements and features.

When to Use This Skill

Keywords: prioritization, priority, MoSCoW, Kano model, WSJF, weighted shortest job first, value cost risk, Wiegers, priority matrix, must have should have, delighters, basic needs, performance features, cost of delay

Use this skill when:

  • Ranking requirements by business value
  • Choosing between competing features
  • Allocating limited development capacity
  • Communicating priority decisions to stakeholders
  • Balancing customer satisfaction vs implementation effort
  • Making trade-off decisions in product planning

Prioritization Methods Overview

MethodBest ForComplexityStakeholder Input
MoSCoWQuick categorization, MVP scopeLowLow
Kano ModelCustomer satisfaction analysisMediumHigh (surveys)
WSJFAgile/SAFe environments, flow optimizationMediumMedium
Wiegers' MatrixQuantitative value/cost analysisHighMedium
Opportunity ScoringJTBD-aligned prioritizationMediumHigh (surveys)

MoSCoW Method

Categories

moscow:
  must:
    definition: "Non-negotiable for this release"
    criteria:
      - "System won't work without it"
      - "Legal/regulatory requirement"
      - "Core to value proposition"
    typical_percentage: "60% of effort"

  should:
    definition: "Important but not critical"
    criteria:
      - "Significant value, but workarounds exist"
      - "Key stakeholder expectations"
      - "Competitive parity"
    typical_percentage: "20% of effort"

  could:
    definition: "Nice to have if time permits"
    criteria:
      - "Enhances user experience"
      - "Low effort, incremental value"
      - "Differentiator but not essential"
    typical_percentage: "20% of effort"

  wont:
    definition: "Explicitly out of scope for now"
    criteria:
      - "Agreed to defer, not rejected"
      - "Future consideration"
      - "Resource constraints"
    note: "Document for future reference"

Application

moscow_process:
  1. List all requirements
  2. Start with MUST - be strict (if everything is MUST, nothing is)
  3. Move to WON'T - explicitly exclude
  4. Distribute remaining between SHOULD and COULD
  5. Validate: MUSTs should be ~60% of capacity

  warning_signs:
    - "All requirements are MUST" → Be stricter
    - "No WON'Ts" → You're avoiding hard decisions
    - "MUSTs exceed capacity" → Re-evaluate or reduce scope

Kano Model

Feature Categories

kano_categories:
  basic:
    name: "Basic (Must-Be)"
    description: "Expected features - absence causes dissatisfaction"
    examples:
      - "Website loads without errors"
      - "Login works correctly"
      - "Data is saved reliably"
    satisfaction_curve: "Only prevents dissatisfaction, doesn't create satisfaction"

  performance:
    name: "Performance (One-Dimensional)"
    description: "More is better - linear satisfaction"
    examples:
      - "Page load speed"
      - "Storage capacity"
      - "Number of integrations"
    satisfaction_curve: "Linear relationship to investment"

  excitement:
    name: "Excitement (Delighters)"
    description: "Unexpected features that create delight"
    examples:
      - "AI-powered suggestions"
      - "Personalized experience"
      - "Innovative shortcuts"
    satisfaction_curve: "High satisfaction, low expectation"

  indifferent:
    name: "Indifferent"
    description: "Features users don't care about"
    examples:
      - "Technical implementation details"
      - "Over-engineered features"
    action: "Deprioritize or remove"

  reverse:
    name: "Reverse"
    description: "Features some users actively dislike"
    examples:
      - "Mandatory tutorials"
      - "Intrusive notifications"
    action: "Make optional or remove"

Kano Survey Method

kano_survey:
  question_pair:
    functional: "How would you feel if [feature] was present?"
    dysfunctional: "How would you feel if [feature] was absent?"

  answer_options:
    - "I would like it"
    - "I expect it"
    - "I'm neutral"
    - "I can tolerate it"
    - "I dislike it"

  interpretation_matrix:
    # Functional → Dysfunctional → Category
    "Like → Dislike": "Excitement"
    "Like → Neutral": "Excitement"
    "Expect → Dislike": "Basic"
    "Neutral → Neutral": "Indifferent"
    "Like → Like": "Questionable (inconsistent)"

Kano Visualization

SATISFACTION
    ↑
    │           ╱ Excitement (Delighters)
    │         ╱
    │       ╱
    │     ╱
────┼─────────────── Performance
    │     ╲
    │       ╲
    │         ╲
    │           ╲ Basic (Must-Be)
    │
    └─────────────────────────────→ FULFILLMENT
         Not implemented    Fully implemented

WSJF (Weighted Shortest Job First)

Formula

wsjf:
  formula: "WSJF = Cost of Delay / Job Size"

  cost_of_delay:
    components:
      user_value: "Value to end users/customers"
      time_criticality: "How much value decays with time"
      risk_reduction: "Risk/opportunity enabled by feature"

    formula: "CoD = User Value + Time Criticality + Risk Reduction"

  job_size:
    definition: "Relative effort to implement"
    scale: "Fibonacci (1, 2, 3, 5, 8, 13)"

WSJF Scoring Template

wsjf_example:
  feature: "Mobile App Offline Mode"

  cost_of_delay:
    user_value:
      score: 8
      rationale: "Highly requested by field workers"
    time_criticality:
      score: 5
      rationale: "Competitor launching similar feature in Q2"
    risk_reduction:
      score: 3
      rationale: "Reduces support tickets for connectivity issues"
    total_cod: 16

  job_size:
    score: 5
    rationale: "Moderate complexity, known patterns"

  wsjf_score: 3.2  # 16 / 5

  interpretation: "High priority - good value for effort"

WSJF Comparison Table

FeatureUser ValueTime CritRisk ReductionCoDSizeWSJFRank
Offline Mode8531653.21
Dark Theme511723.52
Export PDF6321181.43

Wiegers' Value/Cost/Risk Method

Scoring Dimensions

wiegers_method:
  dimensions:
    value:
      question: "What relative benefit does this provide?"
      scale: 1-9
      perspective: "Customer/business value"

    penalty:
      question: "What's the relative penalty of NOT having this?"
      scale: 1-9
      perspective: "Risk of omission"

    cost:
      question: "What's the relative cost to implement?"
      scale: 1-9
      perspective: "Development effort"

    risk:
      question: "What's the relative technical risk?"
      scale: 1-9
      perspective: "Uncertainty, complexity"

  formula: |
    Priority = (Value × ValueWeight + Penalty × PenaltyWeight) /
               (Cost × CostWeight + Risk × RiskWeight)

  default_weights:
    value: 2
    penalty: 1
    cost: 1
    risk: 0.5

Wiegers Template

wiegers_example:
  feature: "Two-Factor Authentication"

  scores:
    value: 7      # High security value
    penalty: 9    # Major penalty if missing (compliance risk)
    cost: 5       # Moderate implementation effort
    risk: 3       # Known patterns, low risk

  calculation:
    numerator: (7 × 2) + (9 × 1) = 23
    denominator: (5 × 1) + (3 × 0.5) = 6.5
    priority: 3.54

  interpretation: "High priority due to strong penalty for omission"

Opportunity Scoring (JTBD)

Importance vs Satisfaction

From the JTBD framework:

opportunity_scoring:
  formula: "Opportunity = Importance + (Importance - Satisfaction)"

  data_collection:
    survey_questions:
      importance: "How important is it to [outcome]? (1-10)"
      satisfaction: "How satisfied are you with current ability? (1-10)"

  interpretation:
    score_15_20: "High opportunity - underserved"
    score_10_15: "Moderate opportunity"
    score_5_10: "Low opportunity - well served"
    score_below_5: "Over-served - deprioritize"

  example:
    outcome: "Quickly find relevant products"
    importance: 9
    satisfaction: 4
    score: 14  # 9 + (9-4)
    interpretation: "Moderate-high opportunity"

Choosing a Method

method_selection:
  use_moscow_when:
    - "Quick decisions needed"
    - "Stakeholders familiar with method"
    - "Defining MVP scope"
    - "Binary in/out decisions"

  use_kano_when:
    - "Understanding customer satisfaction drivers"
    - "Product differentiation focus"
    - "Have access to customer surveys"
    - "Balancing basic vs delighter features"

  use_wsjf_when:
    - "Agile/SAFe environment"
    - "Flow-based delivery"
    - "Economic decision-making"
    - "Comparing features of varying sizes"

  use_wiegers_when:
    - "Need quantitative justification"
    - "Multiple stakeholder perspectives"
    - "High-stakes prioritization decisions"
    - "Want to weight multiple factors"

  use_opportunity_when:
    - "JTBD-based product development"
    - "Customer research available"
    - "Finding underserved needs"
    - "Outcome-focused roadmap"

Combining Methods

combined_approach:
  step_1: "Use MoSCoW for initial categorization"
  step_2: "Apply Kano to understand SHOULD/COULD items better"
  step_3: "Use WSJF or Wiegers to rank within categories"
  step_4: "Validate with Opportunity Scoring against customer data"

  example_workflow:
    - "All MUSTs go to backlog (non-negotiable)"
    - "Kano analysis on SHOULDs reveals 3 delighters"
    - "WSJF ranking of delighters prioritizes by value/effort"
    - "Final backlog ordered with business justification"

Output Format

Priority Report Template

priority_report:
  domain: "{domain}"
  method: "{method used}"
  date: "{ISO-8601}"

  summary:
    total_requirements: 25
    high_priority: 8
    medium_priority: 10
    low_priority: 7

  detailed_ranking:
    - rank: 1
      requirement_id: "REQ-001"
      title: "Two-Factor Authentication"
      method_score: 3.54
      rationale: "High penalty for omission, compliance requirement"

    - rank: 2
      requirement_id: "REQ-015"
      title: "Mobile Offline Mode"
      method_score: 3.2
      rationale: "High user value, competitive pressure"

  visualization: |
    [Mermaid or ASCII chart showing priority distribution]

  recommendations:
    - "Immediate focus on top 5 high-priority items"
    - "Consider deferring low-priority items with high cost"
    - "Re-evaluate in 2 weeks based on new information"

Related Commands

  • /prioritize - Apply prioritization methods to requirements
  • /gaps - Identify missing requirements before prioritizing
  • /export - Export prioritized requirements

References

For detailed guidance:

External:

  • Karl Wiegers' "Software Requirements"
  • SAFe Framework WSJF guidance
  • Noriaki Kano's research on customer satisfaction

Version History

  • v1.0.0 (2025-12-26): Initial release - Prioritization Methods skill

Last Updated: 2025-12-26

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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。

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