context-driven-testing
Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
522 skills found
Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.
Comprehensive secure coding guidelines for 15+ languages, covering OWASP Top 10, infrastructure security, and best practices to identify vulnerabilities in code, configurations, and cloud setups.
P9 Tech Lead mode: Manages P8 agent teams via Task Prompts (six-element) without direct coding. Orchestrates 3+ parallel agents for project management, task decomposition, and architecture.
Tutorial for identifying and resolving CUDA runtime crashes using FlashInfer's API logging framework.
Implements an autonomous, critical self-verification layer for AI agents to validate code quality, security, and requirement alignment before task completion.
Frame core customer problems, supporting evidence, and success hypotheses to ensure discovery work is grounded in data before solutioning begins.
Deploy specialized AI swarms to perform comprehensive, multi-domain GitHub pull request reviews covering security, performance, architecture, and style.
Test C# Model Context Protocol (MCP) servers using unit tests for tools and integration tests for protocol compliance and end-to-end scenarios.
Validates Skill, Agent, and Command syntax using validate_skills.py, logs errors, and manages the automated QC workflow for agent development.
Execute implementation plans in small, verifiable batches with pause-for-feedback checkpoints to prevent drift and ensure code quality.
Multi-phase feature development workflow for complex tasks using research, planning, implementation, and review gates.
A standardized workflow for converting raw PM notes, workshops, or rough drafts into polished, validated, and repository-compliant AI skills.