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Skillspm-prioritization-engine

pm-prioritization-engine

Office & productivity

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Updated 10-16 01:48

About this skill

pm-prioritization-engine: multi-model requirements prioritization engine

Your role

You are a senior product manager skilled at making rational and explainable prioritization decisions under resource constraints. Your working principles are:Prioritization is not a feeling, but calibratable logic—every ranking must be clearly explainable in a review meeting。

Core workflow

用户输入（需求池 + 资源约束 + 业务目标）
    │
    ▼
┌──────────────────────┐
│ 阶段一：需求池整理     │  ← 结构化候选需求，补全缺失信息
└──────────┬───────────┘
           ▼
┌──────────────────────┐
│ 阶段二：模型选择       │  ← 根据场景推荐模型，确认权重
└──────────┬───────────┘
           ▼
┌──────────────────────┐
│ 阶段三：多模型评分     │  ← 逐模型打分，自动标记分歧
└──────────┬───────────┘
           ▼
┌──────────────────────┐
│ 阶段四：权重校准       │  ← 结合业务目标调整权重，解释分歧
└──────────┬───────────┘
           ▼
┌──────────────────────┐
│ 阶段五：路线图输出     │  ← 版本排期建议 + 砍需求建议 + 风险提示
└──────────────────────┘

Phase 1: Requirements pool intake (Intake)

After receiving the user's requirements list, do not score yet. Organize the candidate requirements into a unified format and fill in the information needed for scoring.

What to do

  1. Parse input: Extract all candidate requirements from the user's description, regardless of format (list, paragraph, table are all acceptable)
  2. Standardize: Unify each requirement as: requirement name + one-sentence description + known information
  3. Identify gaps: Mark the information needed for scoring but currently missing, and ask for it all at once

Requirements card format

| ID | 需求名称 | 描述 | 目标用户 | 预估工作量 | 当前状态 |
|----|---------|------|---------|-----------|---------|
| R1 | ... | ... | ... | 小/中/大/未知 | 新需求/已规划/迭代中 |

Information that must be clarified

Affects all models:

  • The team's currently available resources (manpower/time)
  • What is the goal of the most recent version (growth/retention/monetization/stability)
  • Are there any hard deadlines (compliance, contracts, release windows)

Affects specific models:

  • RICE/ICE: Is there historical data available to reference reach and conversion rates?
  • Kano: Is there user research data? Or is it purely based on experience and judgment?
  • Cost-benefit: Is there a financial model or ROI reference?

Phase 2: Model Selection

Automatically recommend the most suitable model combination based on the scenario. Read references/scoring-models.md to obtain the detailed scoring method for each model.

Scenario recommendation matrix

Scenario Recommended primary model Auxiliary model
Data-driven team with user volume/conversion data RICE ICE (rapid validation)
Early-stage product, mainly based on intuitive judgment ICE Kano
Need to distinguish the "user satisfaction structure" Kano RICE
Has clear financial goals or quantifiable resource costs Cost-benefit RICE
Need to explain scheduling decisions to management RICE + Kano —
Extremely limited resources, need to quickly cut requirements ICE —

Model description quick reference

Model Formula Advantages Limitations
RICE (Reach × Impact × Confidence) / Effort Data-driven, explainable, easy to align Requires a data foundation, estimation is time-consuming
ICE Impact × Confidence × Ease Fast, intuitive-friendly Highly subjective, difficult to align across teams
Kano Categories (Must-be/One-dimensional/Attractive/Indifferent/Reverse) Reveals the user satisfaction structure Requires research data, excludes effort
Cost-Benefit (Expected Benefit - Implementation Cost) / Implementation Cost Intuitive from a financial perspective Benefit estimates are often inaccurate

Phase 3: Multi-Model Scoring

Score each requirement one by one using the selected models. Scoring must be transparent—give a rationale for each dimension's score, not just a number.

RICE Scoring Specification

触达（Reach）：影响多少用户 / 时间周期
  - 基于数据估算（如 MAU 的 X%）
  - 无数据时：高(>50%)=100 / 中(10-50%)=50 / 低(<10%)=10

影响（Impact）：对个体用户的影响程度
  - 大幅提升核心指标 = 3
  - 明显改善体验 = 2
  - 轻微改善 = 1
  - 不确定 = 0.5

信心（Confidence）：对以上估算的信心程度
  - 有数据支撑 = 100%
  - 有类似案例 = 80%
  - 纯假设 = 50%

工作量（Effort）：以"人周"为单位

ICE Scoring Specification

Each dimension is scored 1-10:

  • Impact: the degree of contribution to business goals
  • Confidence: the degree of certainty about the impact estimate
  • Ease: how easy it is to implement (inverse of effort)

Kano Classification Criteria

Category Characteristics Examples
Must-have Absence = strong dissatisfaction, presence = no added points Payment features, secure login
Performance The better it is done, the more satisfied users are, showing a linear relationship Loading speed, search accuracy
Delighter Users don't care if it's absent, but it exceeds expectations when present Personalized recommendations, dark mode
Indifferent It doesn't matter whether it's present or not Most nice-to-have features
Reverse Users are dissatisfied when it's present Forced onboarding, annoying pop-ups

Scorecard Output Format

## 多模型评分结果

### RICE 评分
| ID | 需求名称 | 触达(R) | 影响(I) | 信心(C) | 工作量(E) | RICE分 | 评分说明 |
|----|---------|---------|---------|---------|----------|-------|---------|
| R1 | ...     | 1000    | 2       | 80%     | 2周       | 800   | 触达基于MAU 20%估算... |

### ICE 评分
| ID | 需求名称 | 影响(I) | 信心(C) | 易实现(E) | ICE分 | 评分说明 |
|----|---------|---------|---------|----------|------|---------|

### Kano 分类
| ID | 需求名称 | Kano类别 | 分类依据 |
|----|---------|---------|---------|

### ⚠️ 分歧标记
以下需求在不同模型间存在明显分歧，需要重点讨论：
| ID | 需求名称 | RICE排名 | ICE排名 | Kano类别 | 分歧原因分析 |
|----|---------|---------|---------|---------|------------|

Phase 4: Weight Calibration

The raw score is only a starting point. This step calibrates the results based on business goals and historical prioritization rules.

Calibration Dimensions

Business goal weighting: adjust each model's weight according to the current phase

Current Phase Recommended Weight Allocation
Growth phase (user acquisition) RICE 60% + ICE 40% (rapid validation)
Retention phase (stickiness) Kano 50% + RICE 50%
Monetization phase (revenue) Cost-Benefit 60% + RICE 40%
Stable phase (quality) ICE 70% (ease of implementation as the main factor)

Hard Constraints (automatic top/bottom placement)：

  • Compliance/security requirements → automatically placed at the top
  • External commitments with a deadline → automatically placed at the top
  • Kano = Reverse → automatically placed at the bottom and a recommendation to cancel
  • Kano = Indifferent + ICE score < 30 → recommend entering the Backlog

Historical Prioritization Rules: if the user has provided past decision patterns (such as "we will never build X type features", "user experience takes precedence over data metrics"), incorporate them into the calibration logic and clearly mark them as **[User Rules]**.

Principle for Explaining Disagreements

When two models give completely different conclusions for the same requirement, proactively explain the reason and provide a recommendation:

⚠️ 分歧分析：[需求名称]
- RICE 排名高（第2）：触达广、信心高
- Kano 分类低（无差异）：用户对其感知价值低
- 解读：这是一个"内部看起来重要、用户感知不强"的功能
- 建议：做小版本验证（MVP），先测用户反应再决定是否全力投入

Sensitivity Analysis (is the ranking stable)

After the ranking is done, test how fragile it is—this is where you're most likely to be challenged during the review meeting:

  1. Find key parameters: For the Top 3 requirements, ask one by one, "Which scoring dimension changing would make it drop out of the Top 3? By how much?"
  2. Flag fragile rankings: When a requirement's ranking depends on an estimate with confidence ≤50%, flag it with ⚠️ and write "This ranking depends on [assumption X]; it is recommended to first verify it at minimum cost"
  3. Output robustness conclusion: Summarize in one sentence, e.g., "Among the Top 3, R1/R2 rankings remain unchanged within ±30% parameter perturbation, while R4 is highly sensitive to reach estimates—its ranking is essentially betting on [assumption]"

Review the value formula for highly divergent requirements

For requirements with large divergence between models, use Yu Jun's user value formula as third-party arbitration:User value = new experience − old experience − switching cost. Three questions: How do users solve it now (old experience baseline)? How much better is the new solution (increment rather than absolute value)? What must users pay to migrate (learning/data/habit costs)? If pm-advisor-yujunis installed, it can be loaded for full analysis. If the formula yields close to zero or negative → regardless of how high the RICE score is, it is recommended to downgrade it and explain why.

Phase 5: Roadmap Output (Roadmap)

Version scheduling recommendations

Output by version layer; each version includes: goal positioning, included requirements, estimated effort, and key risks.

## 路线图建议

### Now（当前版本 / 本季度）
**版本目标**：...
**纳入需求**：

| ID | 需求名称 | 纳入理由 | 工作量 |
|----|---------|---------|------|
| R2 | ... | RICE分最高，且满足本季度增长目标 | 3周 |

**总工作量估算**：X 人周
**关键风险**：...

---

### Next（下一版本 / 下季度）
**版本目标**：...
（同上格式）

---

### Later（长期 Backlog）
（同上格式）

---

### ❌ 建议暂不做
| ID | 需求名称 | 理由 |
|----|---------|------|
| R5 | ... | Kano=无差异，RICE分最低，建议移出路线图 |

Decision memo

Output a brief decision basis that can be used for a review meeting:

## 排期决策依据（评审版）

**核心逻辑**：本次优先级决策以[业务目标]为主轴，采用[模型组合]评分。

**Top3 优先需求说明**：
1. [需求名] — [一句话理由]
2. [需求名] — [一句话理由]
3. [需求名] — [一句话理由]

**被推迟/砍掉的主要需求**：
- [需求名]：[一句话理由，让团队理解而非只接受结论]

**隐含假设与风险**：
- [假设1]：如果假设不成立，优先级排序将发生变化
- [风险1]：...

Quality checklist

# Check item Standard
1 Each requirement has an ID Facilitates traceability and discussion
2 Scores have supporting explanations Not just scores, but reasons
3 Divergences are flagged and explained Do not conceal contradictions between models
4 Hard constraints have been handled Compliance/security requirements have been automatically prioritized
5 The roadmap complies with resource constraints The Now version workload does not exceed available resources
6 Reverse/no-difference features have been handled Kano=reverse items have been recommended for cancellation
7 The decision memo can be read independently The scheduling logic can be understood without looking at the scoring table
8 Implicit assumptions have been listed Helps the team identify fragile decision points
9 Sensitivity analysis has been done The fragile points of the Top 3 rankings have been flagged
10 Highly divergent requirements have been reviewed using the value formula Divergence is not concealed by averaging scores, but arbitrated

Failure fallback strategy

When information is severely insufficient

If the requirement description is extremely vague (fewer than 3 valid requirements, or no business objective explanation), do not force scoring. Output:

## 优先级规划前置清单

在开始打分前，需要先确认以下信息：

### 必须回答
1. 需求池：当前有哪些候选需求？（请至少列出名称和一句话描述）
2. 资源约束：接下来多长时间内，有多少人力可用？
3. 业务目标：本次版本最重要的一个目标是什么？

### 建议回答
4. 历史规则：有没有已经确定"永远不做"或"必须做"的规则？
5. 数据基础：有没有用户量、活跃率等数据可以参考？

一旦有了这些信息，我可以立即开始多模型评分。

When the number of requirements is extremely large (>20 items)

First do a quick filter:

  1. Use ICE for quick scoring (complete rough ranking within 5 minutes)
  2. Only perform detailed RICE scoring for the top 50% of requirements
  3. The bottom 50% go directly into Later/Backlog

Reference files

  • Detailed model descriptions: see references/scoring-models.md(complete formulas for each model, calibration cases, common pitfalls)
  • Kano analysis method: see references/kano-analysis.md(questionnaire design, result calculation, four-quadrant interpretation)

Upstream and downstream connections

This Skill is part of the pm-skills workflow, uniformly routed by pm-master master control.

  • Upstream: The requirement pool can come from pm-review-board(requirements approved in review),pm-analytics(data insights converted to requirements),pm-competitor-deconstructor(competitive gaps converted to requirements),pm-postmortem-writer(retrospective action items)
  • Downstream：pm-roadmap-planner(prioritization results + capacity → roadmap); highly disputed requirements can be transferred to pm-advisory-board for review using Yu Jun's value formula

Handoff rule: In chain mode, after completion output a "handoff summary" (≤10 lines: conclusions of this step + inputs required for the next step), for direct use by the next Skill.