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product-management

by vasilyu1983526GitHub

Provides ready-to-use templates for core product management tasks like customer interviews, outcome roadmaps, OKRs, and 1:1 meetings. Includes a decision tree to guide users to the right template. Focuses on operational execution over theory, with clear do/avoid guidelines.

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Target Audience

Product managers, product owners, and startup founders who need structured frameworks to execute discovery, planning, and strategy work.

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Low security risk, safe to use

9
Clarity
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Quality
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product-managementtemplatesroadmappingokrsdiscovery
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SKILL.md

Product Operations Skill – Quick Reference

This skill turns Claude 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 (Dec 2025):

  • 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.
  • Optional: AI / Automation is allowed only when explicitly requested and policy-compliant.

When to Use This Skill

Claude should invoke 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”

Claude should NOT use this skill for:

  • Book summaries, philosophy, or general education
  • Long case studies or storytelling

Quick Reference

TaskTemplateDomainOutput
Discovery interviewcustomer-interview-template.mdDiscoveryInterview script with Mom Test patterns
Opportunity mappingopportunity-solution-tree.mdDiscoveryOST with outcomes, problems, solutions
Outcome roadmapoutcome-roadmap.mdRoadmapNow/Next/Later with outcomes and themes
OKR definitionokr-template.mdMetrics1-3 objectives with 2-4 key results each
Product positioningpositioning-template.mdStrategyCompetitive alternatives → value → segment
Product visionproduct-vision-template.mdStrategyFrom→To narrative with 3-5 year horizon
1:1 meeting1-1-template.mdLeadershipCheck-in, progress, blockers, growth
Post-incident debriefa3-debrief.mdLeadershipIntent 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
    │
    └─ Leadership / Team Ops?
        ├─ 1:1 meeting? → 1-1 template
        ├─ Giving feedback? → Feedback template (SBI model)
        ├─ Post-incident? → A3 debrief
        └─ Negotiation? → Negotiation one-sheet (Voss)

Do / Avoid (Dec 2025)

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.

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.

Optional: AI / Automation

Use only when explicitly requested and policy-compliant.

Navigation

Resources

Templates

Related Skills


Operational Guide

See resources/operational-guide.md for detailed patterns, template walkthroughs, example flows, and execution checklists. Keep SKILL.md as the navigation hub; use templates/ 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.

Source: https://github.com/vasilyu1983/AI-Agents-public#frameworks~shared-skills~skills~product-management

Content curated from original sources, copyright belongs to authors

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WORKS WITH

Claude Code
Claude
Codex CLI
Codex
Gemini CLI
Gemini
O
OpenCode
O
OpenClaw
GitHub Copilot
Copilot
Cursor
Cursor
W
Windsurf
C
Cline
R
Roo
K
Kiro
J
Junie
A
Augment
W
Warp
G
Goose