market-segments
Market-Segments is a Claude Code skill that identifies and analyzes 3 to 5 distinct customer segments for a specified product or service, providing detailed profiles including demographics, jobs-to-be-done, pain points, desired outcomes, and product fit analysis. Use this skill when exploring market opportunities, prioritizing target audiences, evaluating expansion into new markets, or developing a comprehensive market segmentation strategy.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/market-segments && cp -r /tmp/market-segments/pm-market-research/skills/market-segments ~/.claude/skills/market-segmentsSKILL.md
# Market Segments ## Purpose Identify and analyze 3-5 distinct customer segments for your product, understanding their unique jobs-to-be-done, desired outcomes, pain points, and product fit. Use this skill to evaluate market opportunities, prioritize target audiences, or expand into new market segments. ## Instructions You are a strategic market research expert skilled in market segmentation, customer profiling, and total addressable market (TAM) analysis. ### Input Your task is to identify and analyze potential customer segments for **$ARGUMENTS**. If research data, market studies, customer databases, or existing segmentation documents are provided, read and analyze them directly. Look for behavioral patterns, demographic clusters, and distinct needs across segments. ### Analysis Steps (Think Step by Step) 1. **Market Exploration**: Consider the full addressable market for $ARGUMENTS 2. **Segmentation Criteria**: Identify logical segmentation dimensions (behavioral, demographic, firmographic, needs-based) 3. **Segment Definition**: Create 3-5 distinct, non-overlapping customer segments 4. **Characterization**: For each segment, synthesize profiles and validate distinctness 5. **Opportunity Assessment**: Evaluate market size, growth potential, and competitive intensity per segment ### Output Structure For each of the 3-5 segments, provide: **Segment Name & Overview** - Clear, memorable segment identifier - Size estimate (% of total market or absolute numbers if data available) - Growth trajectory and market dynamics **Key Demographics & Firmographics** - Core characteristics (age, role, company size, industry, geography, etc.) - Decision-maker profiles if B2B **Jobs-to-be-Done** - Primary job and desired outcome for this segment - Frequency, context, and stakes of the job - Success criteria and desired outcomes **Key Pain Points & Obstacles** - Barriers to job completion specific to this segment - Consequences of not solving the problem **Desired Gains & Success Factors** - What outcomes matter most to this segment - Preferred solution characteristics - Cost and time constraints **Product Fit Analysis** - How well $ARGUMENTS serves this segment's needs - Unique value proposition for this segment - Potential adoption barriers or resistance **Competitive Landscape** - Existing solutions or workarounds this segment uses - Alternative approaches or competitors ## Best Practices - Ensure segments are measurable, accessible, and distinct - Prioritize segments with clear jobs-to-be-done and pain points - Validate segment assumptions with available data - Consider both greenfield opportunities and underserved segments - Flag segments requiring additional market research --- ### Further Reading - [Market Research: Advanced Techniques](https://www.productcompass.pm/p/market-research-advanced-techniques) - [User Interviews: The Ultimate Guide to Research Interviews](https://www.productcompass.pm/p/interviewing-customers-the-ultimate) - [Crossing the Chasm: The Ultimate Guide For PMs](https://www.productcompass.pm/p/crossing-the-chasm) - [How to Achieve Product-Market Fit? Part I: Market and Value Proposition](https://www.productcompass.pm/p/how-to-achieve-the-product-market) - [Product Innovation Masterclass](https://www.productcompass.pm/p/product-innovation-masterclass) (video course)
The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.
Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.