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Found 2,522 Skills
Snapshot test email templates using Verify to catch regressions. Validates rendered HTML output matches approved baseline. Works with MJML templates and any email renderer.
Test email sending locally using Mailpit with .NET Aspire. Captures all outgoing emails without sending them. View rendered HTML, inspect headers, and verify delivery in integration tests.
Write BDD test scenarios in Gherkin for a feature
This skill should be used when the user asks to "break down tasks", "create a task list", "plan implementation", "decompose architecture", "create agent tasks", "plan MVP build", "break down feature", "create execution plan", or mentions task breakdown, agent development workflow, or implementation planning. Two-phase workflow for AI agent development with granular, testable tasks.
Generates a structured playtest report template or analyzes existing playtest notes into a structured format. Use this to standardize playtest feedback collection and analysis.
Red-green-refactor development methodology requiring verified test coverage. Use for feature implementation, bugfixes, refactoring, or any behavior changes where tests must prove correctness.
Iteratively review changes, run automated tests, and apply targeted fixes until issues are resolved (or a stop condition is reached).
Discover and execute repository test commands safely with evidence-based command selection and safety guardrails.
Creates comprehensive test suites for Move contracts with 100% coverage requirement. Triggers on: 'generate tests', 'create tests', 'write test suite', 'test this contract', 'how to test', 'add test coverage', 'write unit tests'.
Use when reviewing any interface for usability — walks through Krug's principles from Don't Make Me Think covering cognitive load, scanning, navigation, homepage clarity, mobile usability, accessibility, and the goodwill reservoir.
Setup and utility scripts for muapi.ai — configure API keys, test connectivity, and poll for async generation results
Repository structure methodology for maximum AI agent effectiveness. Three pillars — context engineering (repo as knowledge product), architectural constraints (deterministic enforcement), garbage collection (active entropy fighting). Use when setting up repos for AI development, diagnosing repeated agent failures, writing AGENTS.md, or designing CI gates and structural tests.