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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 |
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/:
Optional: AI / Automation
Use only when explicitly requested and policy-compliant.
Navigation
Resources
Templates
Related Skills
- ../docs-ai-prd/SKILL.md — PRD, stories, and prompt/playbook templates
- ../software-architecture-design/SKILL.md — System design guidance for specs and PRDs
- ../software-frontend/SKILL.md — UI implementation considerations for product specs
- ../software-backend/SKILL.md — Backend/API implications of product decisions
- ../startup-growth-playbooks/SKILL.md — PLG case studies for activation design
Operational Guide
See references/operational-guide.md for detailed patterns, template walkthroughs, example flows, and execution checklists. Keep SKILL.md as the navigation hub; use assets/ when producing artifacts.
External Resources
See data/sources.json for official frameworks (Lean Startup, OST, PR/FAQ, OKRs) and AI/LLM safety references.
Use the quick reference and decision tree above to choose a template, then follow the operational guide for depth.
Trend Awareness Protocol
IMPORTANT : When users ask recommendation questions about product management tools, frameworks, or practices, use a web search tool to check current trends before answering. If web search is unavailable, use data/sources.json and state clearly what you verified vs assumed.
Trigger Conditions
- "What's the best tool for [roadmapping/product analytics/discovery]?"
- "What should I use for [OKRs/metrics/customer feedback]?"
- "What's the latest in product management?"
- "Current best practices for [discovery/roadmaps/prioritization]?"
- "Is [framework/tool] still relevant in 2026?"
- "[Linear] vs [Jira] vs [other]?" or "[Amplitude] vs [Mixpanel]?"
- "Best approach for [AI product management/agentic products]?"
Required Searches
- Search:
"product management best practices 2026"
- Search:
"[specific tool] vs alternatives 2026"
- Search:
"product management trends January 2026"
- Search:
"[discovery/roadmap/OKR] frameworks 2026"
What to Report
After searching, provide:
- Current landscape : What PM tools/frameworks are popular NOW
- Emerging trends : New tools, methods, or patterns gaining traction
- Deprecated/declining : Frameworks/tools losing relevance
- Recommendation : Based on fresh data, not just static knowledge
Example Topics (verify with fresh search)
- Product management tools (Linear, Productboard, Notion, Coda)
- Analytics platforms (Amplitude, Mixpanel, PostHog)
- Discovery and research tools (Maze, UserTesting, Dovetail)
- Roadmapping approaches (outcome-based, theme-based, now/next/later)
- AI product management patterns
- Prioritization frameworks (RICE, ICE, opportunity scoring)
- OKR and metrics tools
Fact-Checking
- Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
- Prefer primary sources; report source links and dates for volatile information.
- If web access is unavailable, state the limitation and mark guidance as unverified.
Weekly Installs
410
Repository
vasilyu1983/ai-…s-public
GitHub Stars
47
First Seen
Jan 23, 2026
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