test-scenarios
The test-scenarios skill generates structured QA test cases from user stories by systematically defining test objectives, preconditions, user roles, step-by-step actions, and expected outcomes. Use this when developing comprehensive test plans, writing acceptance test criteria, or preparing detailed test scenarios for QA teams to execute during feature validation and quality assurance processes.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/test-scenarios && cp -r /tmp/test-scenarios/pm-execution/skills/test-scenarios ~/.claude/skills/test-scenariosSKILL.md
# Test Scenarios Create comprehensive test scenarios from user stories with test objectives, starting conditions, user roles, step-by-step test actions, and expected outcomes. **Use when:** Writing QA test cases, creating test plans, defining acceptance test scenarios, or validating user story implementations. **Arguments:** - `$PRODUCT`: The product or system name - `$USER_STORY`: The user story to test (title and acceptance criteria) - `$CONTEXT`: Additional testing context or constraints ## Step-by-Step Process 1. **Review the user story** and acceptance criteria 2. **Define test objectives** - What specific behavior to validate 3. **Establish starting conditions** - System state, data setup, configurations 4. **Identify user roles** - Who performs the test actions 5. **Create test steps** - Break down interactions step-by-step 6. **Define expected outcomes** - Observable results after each step 7. **Consider edge cases** - Invalid inputs, boundary conditions 8. **Output detailed test scenarios** - Ready for QA execution ## Scenario Template **Test Scenario:** [Clear scenario name] **Test Objective:** [What this test validates] **Starting Conditions:** - [System state required] - [Data or configuration needed] - [User setup or permissions] **User Role:** [Who performs the test] **Test Steps:** 1. [First action and its expected result] 2. [Second action and observable outcome] 3. [Third action and system behavior] 4. [Completion action and final state] **Expected Outcomes:** - [Observable result 1] - [Observable result 2] - [Observable result 3] ## Example Test Scenario **Test Scenario:** View Recently Viewed Products on Product Page **Test Objective:** Verify that the 'Recently viewed' section displays correctly and excludes the current product. **Starting Conditions:** - User is logged in or has browser history enabled - User has viewed at least 2 products in the current session - User is now on a product page different from previously viewed items **User Role:** Online Shopper **Test Steps:** 1. Navigate to any product page → Section should appear at bottom with previously viewed items 2. Scroll to bottom of page → "Recently viewed" section is visible with product cards 3. Verify product thumbnails → Images, titles, and prices are displayed correctly 4. Check current product → Current product is NOT in the recently viewed list 5. Click on a product card → User navigates to the corresponding product page **Expected Outcomes:** - Recently viewed section appears only after viewing at least 1 prior product - Section displays 4-8 product cards with complete information - Current product is excluded from the list - Each card shows "Viewed X minutes/hours ago" timestamp - Clicking cards navigates to correct product pages - Performance: Section loads within 2 seconds ## Output Deliverables - Comprehensive test scenarios for each acceptance criterion - Clear test objectives aligned with user story intent - Detailed step-by-step test actions - Observable expected outcomes after each step - Edge case and error scenario coverage - Ready for QA team execution and documentation
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.