user-stories
Write INVEST-compliant user stories with testable Given-When-Then acceptance criteria to bridge the gap between requirements and development.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
173 skills found
Write INVEST-compliant user stories with testable Given-When-Then acceptance criteria to bridge the gap between requirements and development.
Behavioral guidelines for LLMs to reduce coding mistakes, follow best practices, and improve output quality by enforcing simplicity, surgical changes, and goal-driven verification.
Transforms complex information into structured study notes, summaries, and practice questions for effective learning and information retention.
Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.
Generates comprehensive API references, user manuals, and architectural system documentation directly from your codebase and technical specifications.
Techniques for writing effective fuzzing harnesses across languages. Use when creating new fuzz targets or improving existing harness code.
Create aesthetically beautiful interfaces using systematic design principles, AI-driven evaluation, and automated inspiration analysis.
Context Engineering agent skill to initialize, generate, and execute comprehensive implementation blueprints (PRPs) for one-pass software development.
Advanced QE reporting, quality dashboards, and predictive analytics for test metrics, code coverage, and deployment readiness to drive data-informed quality decisions.
Automates the creation of QA verification guides for Positron bug fixes and features by analyzing GitHub issues and PRs.
Dialectical reasoning and adversarial coding agent for MCP-enabled editors, forcing LLMs to resolve internal contradictions for higher quality outputs.
Comprehensive AI-generated text detection framework. Features multi-layer analysis of vocabulary, structural patterns, model-specific fingerprints, and technical metadata artifacts to identify AI authorship.