Product Manager Toolkit

Product Manager Toolkit

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/product-manager-toolkit from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Key Scripts, Prioritization Frameworks, Discovery Frameworks, Metrics & Analytics, Common Pitfalls to Avoid, Integration Points.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Essential tools and frameworks for modern product management, from discovery to delivery.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Gather Feature Requests
2. Customer feedback
3. Sales requests
4. Technical debt
5. Strategic initiatives
6. Score with RICE
7. Reach: Users affected per quarter

Imported Workflow Notes

Imported: Core Workflows

Feature Prioritization Process

1. Gather Feature Requests

   • Customer feedback
   • Sales requests
   • Technical debt
   • Strategic initiatives

2. Score with RICE

   # Create CSV with: name,reach,impact,confidence,effort
   python scripts/rice_prioritizer.py features.csv
   

   • Reach: Users affected per quarter
   • Impact: massive/high/medium/low/minimal
   • Confidence: high/medium/low
   • Effort: xl/l/m/s/xs (person-months)

3. Analyze Portfolio

   • Review quick wins vs big bets
   • Check effort distribution
   • Validate against strategy

4. Generate Roadmap

   • Quarterly capacity planning
   • Dependency mapping
   • Stakeholder alignment

Customer Discovery Process

1. Conduct Interviews

   • Use semi-structured format
   • Focus on problems, not solutions
   • Record with permission

2. Analyze Insights

   python scripts/customer_interview_analyzer.py transcript.txt
   

   Extracts:

   • Pain points with severity
   • Feature requests with priority
   • Jobs to be done
   • Sentiment analysis
   • Key themes and quotes

3. Synthesize Findings

   • Group similar pain points
   • Identify patterns across interviews
   • Map to opportunity areas

4. Validate Solutions

   • Create solution hypotheses
   • Test with prototypes
   • Measure actual vs expected behavior

PRD Development Process

1. Choose Template

   • Standard PRD: Complex features (6-8 weeks)
   • One-Page PRD: Simple features (2-4 weeks)
   • Feature Brief: Exploration phase (1 week)
   • Agile Epic: Sprint-based delivery

2. Structure Content

   • Problem → Solution → Success Metrics
   • Always include out-of-scope
   • Clear acceptance criteria

3. Collaborate

   • Engineering for feasibility
   • Design for experience
   • Sales for market validation
   • Support for operational impact

Imported: Key Scripts

rice_prioritizer.py

Advanced RICE framework implementation with portfolio analysis.

Features:

• RICE score calculation
• Portfolio balance analysis (quick wins vs big bets)
• Quarterly roadmap generation
• Team capacity planning
• Multiple output formats (text/json/csv)

Usage Examples:

# 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:

# 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

Examples

Example 1: Ask for the upstream workflow directly

Use @product-manager-toolkit to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @product-manager-toolkit against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @product-manager-toolkit for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @product-manager-toolkit using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Quick Start

For Feature Prioritization

python scripts/rice_prioritizer.py sample  # Create sample CSV
python scripts/rice_prioritizer.py sample_features.csv --capacity 15

For Interview Analysis

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

Imported: Quick Commands Cheat Sheet

# Prioritization
python scripts/rice_prioritizer.py features.csv --capacity 15

# Interview Analysis
python scripts/customer_interview_analyzer.py interview.txt

# Create sample data
python scripts/rice_prioritizer.py sample

# JSON outputs for integration
python scripts/rice_prioritizer.py features.csv --output json
python scripts/customer_interview_analyzer.py interview.txt json

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Start with the problem, not solution
• Include clear success metrics upfront
• Explicitly state what's out of scope
• Use visuals (wireframes, flows)
• Keep technical details in appendix
• Version control changes
• Mix quick wins with strategic bets

Imported Operating Notes

Imported: Best Practices

Writing Great PRDs

1. Start with the problem, not solution
2. Include clear success metrics upfront
3. Explicitly state what's out of scope
4. Use visuals (wireframes, flows)
5. Keep technical details in appendix
6. Version control changes

Effective Prioritization

1. Mix quick wins with strategic bets
2. Consider opportunity cost
3. Account for dependencies
4. Buffer for unexpected work (20%)
5. Revisit quarterly
6. Communicate decisions clearly

Customer Discovery Tips

1. Ask "why" 5 times
2. Focus on past behavior, not future intentions
3. Avoid leading questions
4. Interview in their environment
5. Look for emotional reactions
6. Validate with data

Stakeholder Management

1. Identify RACI for decisions
2. Regular async updates
3. Demo over documentation
4. Address concerns early
5. Celebrate wins publicly
6. Learn from failures openly

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/product-manager-toolkit, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• prd_templates.md
• customer_interview_analyzer.py
• rice_prioritizer.py
• prd_templates.md
• customer_interview_analyzer.py
• rice_prioritizer.py

Imported Reference Notes

Imported: Reference Documents

prd_templates.md

Multiple PRD formats for different contexts:

1. Standard PRD Template

   • Comprehensive 11-section format
   • Best for major features
   • Includes technical specs

2. One-Page PRD

   • Concise format for quick alignment
   • Focus on problem/solution/metrics
   • Good for smaller features

3. Agile Epic Template

   • Sprint-based delivery
   • User story mapping
   • Acceptance criteria focus

4. Feature Brief

   • Lightweight exploration
   • Hypothesis-driven
   • Pre-PRD phase

Imported: 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

Imported: 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

Imported: Metrics & Analytics

North Star Metric Framework

1. Identify Core Value: What's the #1 value to users?
2. Make it Measurable: Quantifiable and trackable
3. Ensure It's Actionable: Teams can influence it
4. Check Leading Indicator: Predicts business success

Funnel Analysis Template

Acquisition → Activation → Retention → Revenue → Referral

Key Metrics:
- Conversion rate at each step
- Drop-off points
- Time between steps
- Cohort variations

Feature Success Metrics

• Adoption: % of users using feature
• Frequency: Usage per user per time period
• Depth: % of feature capability used
• Retention: Continued usage over time
• Satisfaction: NPS/CSAT for feature

Imported: Common Pitfalls to Avoid

1. Solution-First Thinking: Jumping to features before understanding problems
2. Analysis Paralysis: Over-researching without shipping
3. Feature Factory: Shipping features without measuring impact
4. Ignoring Technical Debt: Not allocating time for platform health
5. Stakeholder Surprise: Not communicating early and often
6. Metric Theater: Optimizing vanity metrics over real value

Imported: Integration Points

This toolkit integrates with:

• Analytics: Amplitude, Mixpanel, Google Analytics
• Roadmapping: ProductBoard, Aha!, Roadmunk
• Design: Figma, Sketch, Miro
• Development: Jira, Linear, GitHub
• Research: Dovetail, UserVoice, Pendo
• Communication: Slack, Notion, Confluence

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.