github-copilot-agent-tips-and-tricks
Guidance and operational tips for identifying, reviewing, and managing pull requests created by the GitHub Copilot coding agent within your repository.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
538 skills found
Guidance and operational tips for identifying, reviewing, and managing pull requests created by the GitHub Copilot coding agent within your repository.
Search and execute dynamic external tools via the QVeris API for real-time data retrieval, stock market analysis, and web-based tasks.
A professional bug bounty reporting agent that enforces impact-first writing, CVSS 3.1 scoring, and pre-submit validation for platforms like HackerOne, Bugcrowd, and Intigriti.
Expert AWS solution architecture for startups focusing on serverless, scalable, and cost-effective cloud infrastructure with modern DevOps practices and IaC.
Advanced workflow orchestration for AI agents, featuring multi-model routing, Codex sandbox iteration, parallel swarm execution, and persistent memory across complex pipelines.
Diagnose, isolate, and mitigate LLM context failures like lost-in-middle, poisoning, distraction, and context clash to improve agent reliability.
Analyze markdown documentation files to ensure compliance with predefined AI token budgets and optimize content for efficient AI ingestion.
Create a comprehensive product strategy using the 9-section Product Strategy Canvas to define your vision, segments, value propositions, and defensibility.
Manage the full lifecycle of blog posts, from initial concept and outlining to drafting and editorial refinement for Nuxt/Vue developers.
Python coding assistant providing best practices, PEP 8 enforcement, automated testing with pytest, and dependency management using uv.
A framework for managing the end-to-end LLM project lifecycle, from evaluating task-model fit and pipeline architecture design to implementing structured output parsing and agent-assisted development.
Write INVEST-compliant user stories with testable Given-When-Then acceptance criteria to bridge the gap between requirements and development.