domains / business / product-management-toolkit / product-manager-toolkit

product-manager-toolkit

Skill card

combined

The three copies share the same core toolkit (RICE prioritization, interview analysis, PRD templates, discovery frameworks), but the LeoYeAI copy adds material steps and constraints the other two lack: explicit validation checklists (sensitivity analysis, dependency checks, engineering estimate validation), a recruitment step (5-8 interviews per segment, mix of power/churned users), a post-launch tracking step, and a table-of-contents structure. The anbeime and sickn33 copies are near-identical to each other and omit those steps. Crowning one copy would hide the more complete workflow in the LeoYeAI version, so the copies should be combined.

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Other copies

Combined skill

---
name: product-manager-toolkit
description: Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.
---

# Product Manager Toolkit

Essential tools and frameworks for modern product management, from discovery to delivery.

## Quick Start

### For Feature Prioritization
```bash
python scripts/rice_prioritizer.py sample  # Create sample CSV
python scripts/rice_prioritizer.py sample_features.csv --capacity 15
```

### For Interview Analysis
```bash
python scripts/customer_interview_analyzer.py interview_transcript.txt
```

### For PRD Creation
1. Choose template from `references/prd_templates.md`
2. Fill in sections based on discovery work
3. Review with stakeholders
4. Version control in your PM tool

## Core Workflows

### Feature Prioritization Process

```
Gather → Score → Analyze → Plan → Validate → Execute
```

#### Step 1: Gather Feature Requests
- Customer feedback (support tickets, interviews)
- Sales requests (CRM pipeline blockers)
- Technical debt (engineering input)
- Strategic initiatives (leadership goals)

#### Step 2: Score with RICE
```bash
# Create CSV with: name,reach,impact,confidence,effort
python scripts/rice_prioritizer.py features.csv --capacity 20
```
- **Reach**: Users affected per quarter
- **Impact**: massive/high/medium/low/minimal
- **Confidence**: high/medium/low
- **Effort**: xl/l/m/s/xs (person-months)

#### Step 3: Analyze Portfolio
- Review quick wins vs big bets distribution
- Check effort concentration (avoid all XL projects)
- Validate against strategy

#### Step 4: Generate Roadmap
- Quarterly capacity allocation
- Dependency identification
- Stakeholder communication plan

#### Step 5: Validate Results
**Before finalizing the roadmap:**
- [ ] Compare top priorities against strategic goals
- [ ] Run sensitivity analysis (what if estimates are wrong by 2x?)
- [ ] Review with key stakeholders for blind spots
- [ ] Check for missing dependencies between features
- [ ] Validate effort estimates with engineering

#### Step 6: Execute and Iterate
- Share roadmap with team
- Track actual vs estimated effort
- Revisit priorities quarterly
- Update RICE inputs based on learnings

### Customer Discovery Process

```
Plan → Recruit → Interview → Analyze → Synthesize → Validate
```

#### Step 1: Plan Research
- Define research questions
- Identify target segments
- Create interview script (see `references/frameworks.md`)

#### Step 2: Recruit Participants
- 5-8 interviews per segment
- Mix of power users and churned users
- Incentivize appropriately

#### Step 3: Conduct Interviews
- Use semi-structured format
- Focus on problems, not solutions
- Record with permission
- Take minimal notes during interview

#### Step 4: Analyze Insights
```bash
python scripts/customer_interview_analyzer.py transcript.txt
```
Extracts:
- Pain points with severity
- Feature requests with priority
- Jobs to be done patterns
- Sentiment and key themes
- Notable quotes

#### Step 5: Synthesize Findings
- Group similar pain points across interviews
- Identify patterns (3+ mentions = pattern)
- Map to opportunity areas using Opportunity Solution Tree
- Prioritize opportunities by frequency and severity

#### Step 6: Validate Solutions
**Before building:**
- [ ] Create solution hypotheses (see `references/frameworks.md`)
- [ ] Test with low-fidelity prototypes
- [ ] Measure actual behavior vs stated preference
- [ ] Iterate based on feedback
- [ ] Document learnings for future research

### PRD Development Process

```
Scope → Draft → Review → Refine → Approve → Track
```

#### Step 1: Choose Template
Select from `references/prd_templates.md`:

| Template | Use Case | Timeline |
|----------|----------|----------|
| Standard PRD | Complex features, cross-team | 6-8 weeks |
| One-Page PRD | Simple features, single team | 2-4 weeks |
| Feature Brief | Exploration phase | 1 week |
| Agile Epic | Sprint-based delivery | Ongoing |

#### Step 2: Draft Content
- Lead with problem statement
- Define success metrics upfront
- Explicitly state out-of-scope items
- Include wireframes or mockups

#### Step 3: Review Cycle
- Engineering: feasibility and effort
- Design: user experience gaps
- Sales: market validation
- Support: operational impact

#### Step 4: Refine Based on Feedback
- Address technical constraints
- Adjust scope to fit timeline
- Document trade-off decisions

#### Step 5: Approval and Kickoff
- Stakeholder sign-off
- Sprint planning integration
- Communication to broader team

#### Step 6: Track Execution
**After launch:**
- [ ] Compare actual metrics vs targets
- [ ] Conduct user feedback sessions
- [ ] Document what worked and what didn't
- [ ] Update estimation accuracy data
- [ ] Share learnings with team

## Key Scripts

### rice_prioritizer.py
Advanced RICE framework implementation with portfolio analysis.

**Features**:
- RICE score calculation with configurable weights
- Portfolio balance analysis (quick wins vs big bets)
- Quarterly roadmap generation based on capacity
- Multiple output formats (text, JSON, CSV)

**Usage Examples**:
```bash
# Basic prioritization
python scripts/rice_prioritizer.py features.csv

# With custom team capacity (person-months per quarter)
python scripts/rice_prioritizer.py features.csv --capacity 20

# Output as JSON for integration
python scripts/rice_prioritizer.py features.csv --output json
```

### customer_interview_analyzer.py
NLP-based interview analysis for extracting actionable insights.

**Capabilities**:
- Pain point extraction with severity assessment
- Feature request identification and classification
- Jobs-to-be-done pattern recognition
- Sentiment analysis
- Theme extraction
- Competitor mentions
- Key quotes identification

**Usage Examples**:
```bash
# Analyze single interview
python scripts/customer_interview_analyzer.py interview.txt

# Output as JSON for aggregation
python scripts/customer_interview_analyzer.py interview.txt json
```

## Prioritization Frameworks

### RICE Framework
```
Score = (Reach × Impact × Confidence) / Effort

Reach: # of users/quarter
Impact: 
  - Massive = 3x
  - High = 2x
  - Medium = 1x
  - Low = 0.5x
  - Minimal = 0.25x
Confidence:
  - High = 100%
  - Medium = 80%
  - Low = 50%
Effort: Person-months
```

### Value vs Effort Matrix
```
         Low Effort    High Effort
         
High     QUICK WINS    BIG BETS
Value    [Prioritize]   [Strategic]
         
Low      FILL-INS      TIME SINKS
Value    [Maybe]       [Avoid]
```

### MoSCoW Method
- **Must Have**: Critical for launch
- **Should Have**: Important but not critical
- **Could Have**: Nice to have
- **Won't Have**: Out of scope

## Discovery Frameworks

### Customer Interview Guide
```
1. Context Questions (5 min)
   - Role and responsibilities
   - Current workflow
   - Tools used

2. Problem Exploration (15 min)
   - Pain points
   - Frequency and impact
   - Current workarounds

3. Solution Validation (10 min)
   - Reaction to concepts
   - Value perception
   - Willingness to pay

4. Wrap-up (5 min)
   - Other thoughts
   - Referrals
   - Follow-up permission
```

### Hypothesis Template
```
We believe that [building this feature]
For [these users]
Will [achieve this outcome]
We'll know we're right when [metric]
```

### Opportunity Solution Tree
```
Outcome
├── Opportunity 1
│   ├── Solution A
│   └── Solution B
└── Opportunity 2
    ├── Solution C
    └── Solution D
```

## Conflicts

- **LeoYeAI/openclaw-master-skills** includes validation and execution steps (Steps 5-6) in the Feature Prioritization, Customer Discovery, and PRD Development processes that are absent from the anbeime/skill and sickn33/agentic-awesome-skills copies. The combined instruction includes these steps.
- **LeoYeAI/openclaw-master-skills** references a separate `references/frameworks.md` file for RICE formula and scoring guidelines, while the other two copies embed the RICE formula directly in the SKILL.md. The combined instruction embeds the RICE formula directly and references `references/frameworks.md` for the interview script and hypothesis template.
- **LeoYeAI/openclaw-master-skills** includes a recruitment step (Step 2) in the Customer Discovery process with specific guidance on 5-8 interviews per segment, which is absent from the other two copies. The combined instruction includes this step.