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Agent Skill · 研究检索

prioritization-advisor(优先顺序顾问)

prioritization-advisor 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-advisor 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,权限为需要联网;许可证信息还不明确,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 6,295。

总安装 22,668周安装 926GitHub Stars 3,917下载 7,334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:prioritization-advisor(优先顺序顾问)
来源仓库:https://github.com/deanpeters/product-manager-skills
仓库路径:skills/prioritization-advisor
安装命令:
npx skills add https://github.com/deanpeters/product-manager-skills --skill prioritization-advisor
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/deanpeters/product-manager-skills --skill prioritization-advisor

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。

将优先级框架与您的产品阶段、团队环境和决策需求相匹配。

指导您回答有关产品成熟度、团队结构、利益相关者一致性和数据可用性的四个自适应问题,以推荐正确的框架(RICE、ICE、价值/努力、Kano 或其他) 解释每个框架何时表现出色,何时失败,以及实施步骤和评分模板 通过将方法与上下文相匹配,而不是应用一刀切的解决方案,有助于避免“框架鞭打” 包括常见陷阱(过度努力、信心膨胀、单独 PM 评分)以及何时切换框架的重新评估触发器

来源仓库内路径:skills/prioritization-advisor

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/deanpeters/product-manager-skills --skill prio…;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

prioritization-advisor 怎么安装?

在终端执行 npx skills add https://github.com/deanpeters/product-manager-skills --skill prioritization…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

prioritization-advisor 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-advisor 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

prioritization-advisor 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。

prioritization-advisor 用的人多吗?

公开数据是累计安装 22,668 次,近一周安装 926 次,下载 7,334 次,GitHub 3,917 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-advisor 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。
  • 来源仓库有 3,917 星,维护活跃度相对可查。

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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

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。

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-advisor

优先顺序顾问 · 本页

22,6683,917需要联网未明确通过

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

Purpose

Guide product managers in choosing the right prioritization framework by asking adaptive questions about product stage, team context, decision-making needs, and stakeholder dynamics. Use this to avoid "framework whiplash" (switching frameworks constantly) or applying the wrong framework (e.g., using RICE for strategic bets or ICE for data-driven decisions). Outputs a recommended framework with implementation guidance tailored to your context.

This is not a scoring calculator—it's a decision guide that matches prioritization frameworks to your specific situation.

Key Concepts

The Prioritization Framework Landscape

Common frameworks and when to use them:

Scoring frameworks:

  • RICE (Reach, Impact, Confidence, Effort) — Data-driven, requires metrics
  • ICE (Impact, Confidence, Ease) — Lightweight, gut-check scoring
  • Value vs. Effort (2x2 matrix) — Quick wins vs. strategic bets
  • Weighted Scoring — Custom criteria with stakeholder input

Strategic frameworks:

  • Kano Model — Classify features by customer delight (basic, performance, delight)
  • Opportunity Scoring — Rate importance vs. satisfaction gap
  • Buy-a-Feature — Customer budget allocation exercise
  • Moscow (Must, Should, Could, Won't) — Forcing function for hard choices

Contextual frameworks:

  • Cost of Delay — Urgency-based (time-sensitive features)
  • Impact Mapping — Goal-driven (tie features to outcomes)
  • Story Mapping — User journey-based (narrative flow)

Why This Works

  • Context-aware: Matches framework to product stage, team maturity, data availability
  • Anti-dogmatic: No single "best" framework—it depends on your situation
  • Actionable: Provides implementation steps, not just framework names

Anti-Patterns (What This Is NOT)

  • Not a universal ranking: Frameworks aren't "better" or "worse"—they fit different contexts
  • Not a replacement for strategy: Frameworks execute strategy; they don't create it
  • Not set-it-and-forget-it: Reassess frameworks as your product matures

When to Use This

  • Choosing a prioritization framework for the first time
  • Switching frameworks (current one isn't working)
  • Aligning stakeholders on prioritization process
  • Onboarding new PMs to team practices

When NOT to Use This

  • When you already have a working framework (don't fix what isn't broken)
  • For one-off decisions (frameworks are for recurring prioritization)
  • As a substitute for strategic vision (frameworks can't tell you what to build)

Facilitation Source of Truth

Use workshop-facilitation as the default interaction protocol for this skill.

It defines:

  • session heads-up + entry mode (Guided, Context dump, Best guess)
  • one-question turns with plain-language prompts
  • progress labels (for example, Context Qx/8 and Scoring Qx/5)
  • interruption handling and pause/resume behavior
  • numbered recommendations at decision points
  • quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.

Application

This interactive skill asks up to 4 adaptive questions, offering 3-4 enumerated options at each step.


Question 1: Product Stage

Agent asks: "What stage is your product in?"

Offer 4 enumerated options:

  1. Pre-product/market fit — "Searching for PMF; experimenting rapidly; unclear what customers want" (High uncertainty, need speed)
  2. Early PMF, scaling — "Found initial PMF; growing fast; adding features to retain/expand" (Moderate uncertainty, balancing speed + quality)
  3. Mature product, optimization — "Established market; incremental improvements; competing on quality/features" (Low uncertainty, data-driven decisions)
  4. Multiple products/platform — "Portfolio of products; cross-product dependencies; complex stakeholder needs" (Coordination complexity)

Or describe your product stage (new idea, growth mode, established, etc.).

User response: [Selection or custom]


Question 2: Team Context

Agent asks: "What's your team and stakeholder environment like?"

Offer 4 enumerated options:

  1. Small team, limited resources — "3-5 engineers, 1 PM, need to focus ruthlessly" (Need simple, fast framework)
  2. Cross-functional team, aligned — "Product, design, engineering aligned; clear goals; good collaboration" (Can use data-driven frameworks)
  3. Multiple stakeholders, misaligned — "Execs, sales, customers all have opinions; need transparent process" (Need consensus-building framework)
  4. Large org, complex dependencies — "Multiple teams, shared roadmap, cross-team dependencies" (Need coordination framework)

Or describe your team/stakeholder context.

User response: [Selection or custom]


Question 3: Decision-Making Needs

Agent asks: "What's the primary challenge you're trying to solve with prioritization?"

Offer 4 enumerated options:

  1. Too many ideas, unclear which to pursue — "Backlog is 100+ items; need to narrow to top 10" (Need filtering framework)
  2. Stakeholders disagree on priorities — "Sales wants features, execs want strategic bets, engineering wants tech debt" (Need alignment framework)
  3. Lack of data-driven decisions — "Prioritizing by gut feel; want metrics-based process" (Need scoring framework)
  4. Hard tradeoffs between strategic bets vs. quick wins — "Balancing long-term vision vs. short-term customer needs" (Need value/effort framework)

Or describe your specific challenge.

User response: [Selection or custom]


Question 4: Data Availability

Agent asks: "How much data do you have to inform prioritization?"

Offer 3 enumerated options:

  1. Minimal data — "New product, no usage metrics, few customers to survey" (Gut-based frameworks)
  2. Some data — "Basic analytics, customer feedback, but no rigorous data collection" (Lightweight scoring frameworks)
  3. Rich data — "Usage metrics, A/B tests, customer surveys, clear success metrics" (Data-driven frameworks)

Or describe your data situation.

User response: [Selection or custom]


Output: Recommend Prioritization Framework

After collecting responses, the agent recommends a framework:

# Prioritization Framework Recommendation

**Based on your context:**
- **Product Stage:** [From Q1]
- **Team Context:** [From Q2]
- **Decision-Making Need:** [From Q3]
- **Data Availability:** [From Q4]

---

## Recommended Framework: [Framework Name]

**Why this framework fits:**
- [Rationale 1 based on Q1-Q4]
- [Rationale 2]
- [Rationale 3]

**When to use it:**
- [Context where this framework excels]

**When NOT to use it:**
- [Limitations or contexts where it fails]

---

## How to Implement

### Step 1: [First implementation step]
- [Detailed guidance]
- [Example: "Define scoring criteria: Reach, Impact, Confidence, Effort"]

### Step 2: [Second step]
- [Detailed guidance]
- [Example: "Score each feature on 1-10 scale"]

### Step 3: [Third step]
- [Detailed guidance]
- [Example: "Calculate RICE score: (Reach × Impact × Confidence) / Effort"]

### Step 4: [Fourth step]
- [Detailed guidance]
- [Example: "Rank by score; review top 10 with stakeholders"]

---

## Example Scoring Template

[Provide a concrete example of how to use the framework]

**Example (if RICE):**

| Feature | Reach (users/month) | Impact (1-3) | Confidence (%) | Effort (person-months) | RICE Score |
|---------|---------------------|--------------|----------------|------------------------|------------|
| Feature A | 10,000 | 3 (massive) | 80% | 2 | 12,000 |
| Feature B | 5,000 | 2 (high) | 70% | 1 | 7,000 |
| Feature C | 2,000 | 1 (medium) | 50% | 0.5 | 2,000 |

**Priority:** Feature A > Feature B > Feature C

---

## Alternative Framework (Second Choice)

**If the recommended framework doesn't fit, consider:** [Alternative framework name]

**Why this might work:**
- [Rationale]

**Tradeoffs:**
- [What you gain vs. what you lose]

---

## Common Pitfalls with This Framework

1. **[Pitfall 1]** — [Description and how to avoid]
2. **[Pitfall 2]** — [Description and how to avoid]
3. **[Pitfall 3]** — [Description and how to avoid]

---

## Reassess When

- Product stage changes (e.g., PMF → scaling)
- Team grows or reorganizes
- Stakeholder dynamics shift
- Current framework feels broken (e.g., too slow, ignoring important factors)

---

**Would you like implementation templates or examples for this framework?**

Examples

Example 1: Good Framework Match (Early PMF, RICE)

Q1 Response: "Early PMF, scaling — Found initial PMF; growing fast; adding features to retain/expand"

Q2 Response: "Cross-functional team, aligned — Product, design, engineering aligned; clear goals"

Q3 Response: "Lack of data-driven decisions — Prioritizing by gut feel; want metrics-based process"

Q4 Response: "Some data — Basic analytics, customer feedback, but no rigorous data collection"


Recommended Framework: RICE (Reach, Impact, Confidence, Effort)

Why this fits:

  • You have some data (analytics, customer feedback) to estimate Reach and Impact
  • Cross-functional team alignment means you can agree on scoring criteria
  • Transitioning from gut feel to data-driven = RICE provides structure without overwhelming complexity
  • Early PMF stage = need speed, but also need to prioritize high-impact features for retention/expansion

When to use it:

  • Quarterly or monthly roadmap planning
  • When backlog exceeds 20-30 items
  • When stakeholders debate priorities

When NOT to use it:

  • For strategic, multi-quarter bets (RICE favors incremental wins)
  • When you lack basic metrics (Reach requires usage data)
  • For single-feature decisions (overkill)

Implementation:

Step 1: Define Scoring Criteria

  • Reach: How many users will this feature affect per month/quarter?
  • Impact: How much will it improve their experience? (1 = minimal, 2 = high, 3 = massive)
  • Confidence: How confident are you in your Reach/Impact estimates? (50% = low data, 80% = good data, 100% = certain)
  • Effort: How many person-months to build? (Include design, engineering, QA)

Step 2: Score Each Feature

  • Use a spreadsheet or Airtable
  • Involve PM, design, engineering in scoring (not just PM solo)
  • Be honest about Confidence (don't inflate scores)

Step 3: Calculate RICE Score

  • Formula: (Reach × Impact × Confidence) / Effort
  • Higher score = higher priority

Step 4: Review and Adjust

  • Sort by RICE score
  • Review top 10-20 with stakeholders
  • Adjust for strategic priorities (RICE doesn't capture everything)

Example Scoring:

FeatureReachImpactConfidenceEffortRICE Score
Email reminders5,000270%17,000
Mobile app10,000360%63,000
Dark mode8,000190%0.514,400

Priority: Dark mode > Email reminders > Mobile app (despite mobile app having high Reach/Impact, Effort is too high)


Alternative Framework: ICE (Impact, Confidence, Ease)

Why this might work:

  • Simpler than RICE (no Reach calculation)
  • Faster to score (good if you need quick decisions)

Tradeoffs:

  • Less data-driven (no Reach metric = can't compare features affecting different user bases)
  • More subjective (Impact/Ease are gut-feel, not metrics)

Common Pitfalls:

  1. Overweighting Effort — Don't avoid hard problems just because they score low. Some strategic bets require high effort.
  2. Inflating Confidence — Be honest. 50% confidence is okay if data is scarce.
  3. Ignoring strategy — RICE doesn't capture strategic importance. Adjust for vision/goals.

Example 2: Bad Framework Match (Pre-PMF + RICE = Wrong Fit)

Q1 Response: "Pre-product/market fit — Searching for PMF; experimenting rapidly"

Q2 Response: "Small team, limited resources — 3 engineers, 1 PM"

Q3 Response: "Too many ideas, unclear which to pursue"

Q4 Response: "Minimal data — New product, no usage metrics"


Recommended Framework: ICE (Impact, Confidence, Ease) or Value/Effort Matrix

Why NOT RICE:

  • You don't have usage data to estimate Reach
  • Pre-PMF = you need speed, not rigorous scoring
  • Small team = overhead of RICE scoring is too heavy

Why ICE instead:

  • Lightweight, gut-check framework
  • Can score 20 ideas in 30 minutes
  • Good for rapid experimentation phase

Or Value/Effort Matrix:

  • Visual 2x2 matrix (high value/low effort = quick wins)
  • Even faster than ICE
  • Good for stakeholder alignment (visual, intuitive)

Common Pitfalls

Pitfall 1: Using the Wrong Framework for Your Stage

Symptom: Pre-PMF startup using weighted scoring with 10 criteria

Consequence: Overhead kills speed. You need experiments, not rigorous scoring.

Fix: Match framework to stage. Pre-PMF = ICE or Value/Effort. Scaling = RICE. Mature = Opportunity Scoring or Kano.


Pitfall 2: Framework Whiplash

Symptom: Switching frameworks every quarter

Consequence: Team confusion, lost time, no consistency.

Fix: Stick with one framework for 6-12 months. Reassess only when stage/context changes.


Pitfall 3: Treating Scores as Gospel

Symptom: "Feature A scored 8,000, Feature B scored 7,999, so A wins"

Consequence: Ignores strategic context, judgment, and vision.

Fix: Use frameworks as input, not automation. PM judgment overrides scores when needed.


Pitfall 4: Solo PM Scoring

Symptom: PM scores features alone, presents to team

Consequence: Lack of buy-in, engineering/design don't trust scores.

Fix: Collaborative scoring sessions. PM, design, engineering score together.


Pitfall 5: No Framework at All

Symptom: "We prioritize by who shouts loudest"

Consequence: HiPPO (Highest Paid Person's Opinion) wins, not data or strategy.

Fix: Pick *any* framework. Even imperfect structure beats chaos.


References

Related Skills

  • user-story.md — Prioritized features become user stories
  • epic-hypothesis.md — Prioritized epics validated with experiments
  • recommendation-canvas.md — Business outcomes inform prioritization

External Frameworks

  • Intercom, *RICE Prioritization* (2016) — Origin of RICE framework
  • Sean McBride, *ICE Scoring* (2012) — Lightweight prioritization
  • Luke Hohmann, *Innovation Games* (2006) — Buy-a-Feature and other collaborative methods
  • Noriaki Kano, *Kano Model* (1984) — Customer satisfaction framework

Dean's Work

  • [If Dean has prioritization resources, link here]

Skill type: Interactive Suggested filename: prioritization-advisor.md Suggested placement: /skills/interactive/ Dependencies: None (standalone, but informs roadmap and backlog decisions)

平台分布

Codex

36.64%
按下载量换算2,687

Claude

32.36%
按下载量换算2,373

Cursor

19.21%
按下载量换算1,409

Gemini CLI

9.51%
按下载量换算697

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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