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.
327 skills found
Guidance and operational tips for identifying, reviewing, and managing pull requests created by the GitHub Copilot coding agent within your repository.
Comprehensive security audit and hardening for AI agents: credential scanning, PII protection, prompt injection defense, and workspace config optimization.
A framework for collecting, analyzing, and prioritizing user feedback across multiple channels to drive product strategy and feature development.
MCP Gateway design patterns for managing Agent Gateway, Subprocess, and Daemon isolation strategies to optimize context token usage and system performance.
Master memory forensics with techniques for acquisition, process analysis, and artifact extraction using Volatility 3 for incident response and malware analysis.
Synchronize project documentation with code. Maintains feature specs, API contracts, and READMEs using init-project standards to ensure traceability and completeness.
Read and navigate external documentation efficiently using llms.txt, MCP search, and smart parsing strategies.
A team of 6 specialist PMO agents for portfolio governance, resource planning, risk analysis, and executive reporting. Dispatch to handle complex multi-project oversight and strategic coordination.
Build and manage MCP servers using the FastMCP framework. Guide for creating tools, resources, prompts, Claude Desktop integration, and deployment with Python and TypeScript.
Generates comprehensive API references, user manuals, and architectural system documentation directly from your codebase and technical specifications.
A standardized workflow for converting raw PM notes, workshops, or rough drafts into polished, validated, and repository-compliant AI skills.
Collaborative PR review using a swarm of three specialized AI agents (Correctness, Health, UX) that discuss findings and reach consensus before posting a structured summary with inline comments.