product-management▌
vasilyu1983/ai-agents-public · updated Apr 8, 2026
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$22
Product Management (Jan 2026)
This skill turns the assistant into an operator, not a lecturer.
Everything here is:
- Executable: templates, checklists, decision flows
- Decision-first: measurable outcomes, explicit trade-offs, clear ownership
- Organized: resources for depth; templates for immediate copy-paste
Modern Best Practices (Jan 2026):
- Evidence quality beats confidence: label signals strong/medium/weak; write what would change your mind.
- Outcomes > output: roadmaps are bets with measurable impact and guardrails, not feature inventories.
- Metrics must be defined (formula + timeframe + data source) to be actionable.
- Privacy, security, and accessibility are requirements, not afterthoughts.
- Hybrid decision loops: AI surfaces anomalies, patterns, and forecasts; humans apply context, ethics, and long-term strategy.
- Accountability: product is often held responsible for business outcomes; confirm the operating model in your org and validate benchmarks with current sources.
- Portfolio diversification: a common heuristic is 70% core, 20% adjacent, 10% transformational; adapt to strategy and constraints.
When to Use This Skill
Use this skill when the user asks to do real product work, such as:
- “Create / refine a PRD / spec / business case / 1-pager”
- “Turn this idea into a roadmap” / “Outcome roadmap for X”
- “Design a discovery plan / interview script / experiment plan”
- “Define success metrics / OKRs / metric tree”
- “Position this product against competitors”
- “Run a difficult conversation / feedback / 1:1 / negotiation”
- “Plan a product strategy / vision / opportunity assessment”
Do not use this skill for:
- Book summaries, philosophy, or general education
- Long case studies or storytelling
Quick Reference
| Task | Template | Domain | Output |
|---|---|---|---|
| Discovery interview | customer-interview-template.md |
Discovery | Interview script with Mom Test patterns |
| Opportunity mapping | opportunity-solution-tree.md |
Discovery | OST with outcomes, problems, solutions |
| PMF survey | pmf-survey-template.md |
Discovery | Sean Ellis + NPS + usage survey |
| Outcome roadmap | outcome-roadmap.md |
Roadmap | Now/Next/Later with outcomes and themes |
| OKR definition | okr-template.md |
Metrics | 1-3 objectives with 2-4 key results each |
| Product positioning | positioning-template.md |
Strategy | Competitive alternatives -> value -> segment |
| Product vision | product-vision-template.md |
Strategy | From→To narrative with 3-5 year horizon |
| Quarterly review | quarterly-product-review.md |
Strategy | Keep / cut / double-down product audit |
| Prioritization | prioritization-scorecard.md |
Prioritization | RICE/ICE scoring with kill criteria |
| Kill criteria | kill-criteria-template.md |
Prioritization | Pre-defined stop conditions per initiative |
| 1:1 meeting | 1-1-template.md |
Leadership | Check-in, progress, blockers, growth |
| Post-incident debrief | a3-debrief.md |
Leadership | Intent vs actual, root cause, action items |
Decision Tree: Choosing the Right Workflow
User needs: [Product Work Type]
├─ Discovery / Validation?
│ ├─ Customer insights? → Customer interview template
│ ├─ Hypothesis testing? → Assumption test template
│ └─ Opportunity mapping? → Opportunity Solution Tree
│
├─ Strategy / Vision?
│ ├─ Long-term direction? → Product vision template
│ ├─ Market positioning? → Positioning template (Dunford)
│ ├─ Big opportunity? → Opportunity assessment
│ └─ Amazon-style spec? → PR/FAQ template
│
├─ Planning / Roadmap?
│ ├─ Outcome-driven? → Outcome roadmap (Now/Next/Later)
│ ├─ Theme-based? → Theme roadmap
│ └─ Metrics / OKRs? → Metric tree + OKR template
│
├─ Prioritization / Focus?
│ ├─ What to build next? → Prioritization scorecard (RICE/ICE)
│ ├─ What to stop? → Kill criteria template + quarterly review
│ ├─ Scope too large? → Scope negotiation patterns
│ └─ PMF check? → PMF survey + retention curve analysis
│
└─ Leadership / Team Ops?
├─ 1:1 meeting? → 1-1 template
├─ Giving feedback? → Feedback template (SBI model)
├─ Post-incident? → A3 debrief
├─ Stakeholder pushback? → Stakeholder management patterns
└─ Negotiation? → Negotiation one-sheet (Voss)
Do / Avoid (Jan 2026)
Do
- Start from the decision: what are we deciding, by when, and with what evidence.
- Define metrics precisely (formula + timeframe + data source) and add guardrails.
- Use discovery to de-risk value before building; prioritize by evidence, not opinions.
- Write “match vs ignore” competitive decisions, not feature grids.
Avoid
- Roadmap theater (shipping lists) without outcomes and learning loops.
- Vanity KPIs (raw signups, impressions) without activation/retention definitions.
- "Build-first validation" (shipping MVPs without falsifiable hypotheses).
- Collecting customer data without purpose limitation, retention, and access controls.
- Building for engineering elegance instead of user value (technical founder trap).
- Feature creep without kill criteria (every feature should have a pre-defined stop condition).
- Saying "yes" to stakeholder requests without trade-off analysis.
- Measuring PMF once instead of continuously across segments.
Prioritization & Saying No
The most common founder-PM failure: building everything, killing nothing, and running out of time before impact.
Prioritization Frameworks
| Framework | Formula / Method | Best For | Watch For |
|---|---|---|---|
| RICE | (Reach x Impact x Confidence) / Effort | Comparing features with data | Gaming confidence scores |
| ICE | Impact x Confidence x Ease | Quick gut-check prioritization | Over-simplification |
| Opportunity Scoring | Importance x (Importance - Satisfaction) | Discovery-driven, JTBD-aligned | Requires user research data |
| Cost of Delay | Value per unit time / Duration | Time-sensitive decisions | Harder to estimate accurately |
| Weighted Shortest Job First (WSJF) | Cost of Delay / Job Size | SAFe/Lean, flow optimization | Requires calibrated estimates |
Pick one. Use it consistently. The framework matters less than the discipline of scoring everything the same way.
Kill Criteria
Every initiative should have pre-defined conditions for stopping:
- Usage threshold: If <X% of target users adopt within Y weeks, stop.
- Cost ceiling: If development exceeds X hours/dollars, pause and re-evaluate.
- Time limit: If not shipped within X weeks, kill or radically descope.
- Metric guardrail: If [guardrail metric] degrades by >X%, roll back.
Use assets/prioritization/kill-criteria-template.md to define these before starting.
Feature Bridge Migration
When replacing an existing feature with a new one, don't hard-kill the old feature. Use a bridge migration pattern to prevent user loss.
Bridge mode: Run both old and new features simultaneously. Route users to the new experience by default but keep the old path accessible (via link, fallback, or settings toggle).
Substitution-based kill rule:
- Define the absorption metric: % of old-feature users who now use the new feature for the same job.
- Set the kill threshold: new feature absorbs ≥80% of old-feature users.
- Set the duration: threshold must hold for 14 consecutive days with no retention regression.
- Only kill the old feature when all three conditions are met.
BRIDGE MIGRATION SEQUENCE:
1. Ship new feature alongside old feature
2. Default new users to new experience
3. Migrate existing users gradually (progressive rollout)
4. Monitor: absorption rate, retention by cohort, support tickets
5. Old feature absorbs ≥80% for 14 days + no retention drop?
├─ Yes → Kill old feature, remove code
└─ No → Investigate gaps, iterate new feature, extend bridge
When NOT to bridge: Security vulnerabilities, compliance requirements, or features with near-zero usage (<1% MAU). These can be killed directly with notice.
Scope Negotiation
When stakeholders push for more scope:
- Reframe as trade-offs: "We can add X if we cut Y — which matters more?"
- Anchor on outcomes: "The goal is [metric]. Does this addition move it?"
- Offer phased delivery: "V1 without this; measure; add in V2 if data supports it."
- Document non-goals explicitly in every spec.
"What to Stop Doing" Quarterly Review
Every quarter, review the product with assets/strategy/quarterly-product-review.md:
- Which features have <5% usage? → Candidate for removal
- Which initiatives produced no measurable outcome? → Stop or pivot
- Which ongoing costs (maintenance, support) exceed their value? → Sunset
- What are you doing "because we always have" but nobody asked for? → Question
For detailed prioritization patterns and worked examples: see references/prioritization-frameworks.md.
Product-Market Fit Measurement
PMF is not a binary event. It's a signal you measure across multiple dimensions.
Sean Ellis Test
Survey users: "How would you feel if you could no longer use [product]?"
- Very disappointed: Target >40% for PMF signal
- Somewhat disappointed: Useful but not dependent
- Not disappointed: Not finding value
Use assets/discovery/pmf-survey-template.md for the full survey (combines Sean Ellis + NPS + usage questions).
Retention Curve Analysis
- Plot cohort retention over time (weekly or monthly depending on product cadence)
- Flattening curve = PMF signal (users who stay, stay)
- Declining curve = No PMF (even retained users eventually leave)
- Segment by ICP: you may have PMF in one segment but not another
Engagement Scoring
Define activation precisely (formula + timeframe + data source):
- What actions constitute "activated"? (not just signed up)
- What's the activation window? (first 7 days, first 14 days?)
- What engagement depth separates power users from casual?
Feature Audit
Periodically audit feature usage to identify what to keep, improve, or remove:
- Top 20% features by usage → invest, polish
- Middle 60% → maintain, don't expand
- Bottom 20% → candidate for removal or redesign
- Features with high support cost relative to usage → redesign or sunset
Segmented PMF
PMF varies by segment. Measure separately for:
- ICP vs non-ICP customers
- Free vs paid users
- Self-serve vs sales-assisted
- By company size, industry, or geography
For detailed PMF measurement methodology: see references/pmf-measurement.md.
Stakeholder Management
Founders manage board members, investors, early customers, co-founders, and (eventually) team leads — often without formal PM training.
Key patterns:
- Board / investors: Update monthly with metrics + decisions + asks. Use narrative format, not slide decks. Lead with "what we learned" not "what we shipped."
- Early customers: They are partners, not just users. Share roadmap intent (not commitments). Ask for input on priorities, not feature requests.
- Co-founder alignment: Weekly sync on priorities. Disagree and commit. Document decisions.
- Saying no to stakeholders: "We're not doing X because [reason tied to strategy]. Here's what we're doing instead and why."
For detailed stakeholder management patterns: see references/stakeholder-management.md.
What Good Looks Like
- Evidence: 5–10 real user touchpoints or equivalent primary data for material bets.
- Scope: clear non-goals and acceptance criteria that can be tested.
- Learning: post-launch review with metric deltas, guardrail impact, and next decision.
PRDs and Specs
For PRDs/specs and writing-quality requirements, use the templates in ../docs-ai-prd/:
- PRD templates: ../docs-ai-prd/assets/prd/prd-template.md and ../docs-ai-prd/assets/prd/ai-prd-template.md
Optional: AI / Automation
Use only when explicitly requested and policy-compliant.
- AI system lifecycle: assets/ai/ai-lifecycle-template.md
- Agentic workflow docs: assets/ai/agentic-ai-orchestration.md
- AI product patterns: references/ai-product-patterns.md
Navigation
Resources
- references/discovery-best-practices.md
- references/roadmap-patterns.md
- references/delivery-best-practices.md
- references/strategy-patterns.md
- references/positioning-patterns.md
- references/data-product-best-practices.md
- references/interviewing-patterns.md
- references/metrics-best-practices.md
- references/leadership-decision-frameworks.md
- references/operational-guide.md
- references/prioritization-frameworks.md
- references/pmf-measurement.md
- references/stakeholder-management.md
- data/sources.json
Templates
- Discovery: assets/discovery/customer-interview-template.md, assets/discovery/assumption-test-template.md, assets/discovery/opportunity-solution-tree.md, assets/discovery/pmf-survey-template.md
- Prioritization: assets/prioritization/prioritization-scorecard.md, assets/prioritization/kill-criteria-template.md
- Strategy/Vision: assets/strategy/product-vision-template.md, assets/strategy/opportunity-assessment.md, assets/strategy/positioning-template.md, assets/strategy/PRFAQ-template.md, assets/strategy/quarterly-product-review.md
- Data: assets/data/data-product-canvas.md
- Roadmaps: assets/roadmap/outcome-roadmap.md,
How to use product-management on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add product-management
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches product-management from GitHub repository vasilyu1983/ai-agents-public and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate product-management. Access the skill through slash commands (e.g., /product-management) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★72 reviews- ★★★★★Kwame Gupta· Dec 24, 2024
I recommend product-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Diego Dixit· Dec 20, 2024
Keeps context tight: product-management is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aarav Taylor· Dec 16, 2024
Useful defaults in product-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Liam Flores· Dec 8, 2024
product-management is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Dhruvi Jain· Dec 4, 2024
product-management fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Omar Abebe· Nov 27, 2024
Useful defaults in product-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Oshnikdeep· Nov 23, 2024
Registry listing for product-management matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Rahul Santra· Nov 19, 2024
product-management has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ira Abbas· Nov 15, 2024
Keeps context tight: product-management is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Isabella Chen· Nov 11, 2024
I recommend product-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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