github-project-management
AI-driven GitHub project management using swarm coordination, automated issue triage, project board synchronization, and intelligent task decomposition for efficient development workflows.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
538 skills found
AI-driven GitHub project management using swarm coordination, automated issue triage, project board synchronization, and intelligent task decomposition for efficient development workflows.
Dialectical reasoning and adversarial coding agent for MCP-enabled editors, forcing LLMs to resolve internal contradictions for higher quality outputs.
Orchestrate end-to-end quality engineering across CI/CD pipelines, from commit-stage unit testing and shift-left strategies to production-stage synthetic monitoring and compliance gates.
Review, audit, and build production-grade frontend interfaces with high design quality, accessibility standards, and design system compliance.
Verify that dotfiles are properly symlinked, synchronised, and configured across the system to ensure development environment health.
Expert assistant for the DGame Unity framework, facilitating development, architecture, hotfix, and resource management within the TEngine-based ecosystem.
Visual web workspace for roadmap management, providing interactive kanban boards and graph-based dependency views for task planning and project progress tracking.
Manage OpenClaw's built-in Chrome browser and chrome-devtools-mcp integration for robust browser automation using the Model Context Protocol.
Autonomous multi-agent LinkedIn system using LangGraph and Claude Opus 4.5 for trend research, content creation, voice profiling, and analytics-driven optimization.
Expert guidance and configuration standards for creating specialized OpenCode AI agents, including YAML frontmatter, tool permissions, and operational modes.
Directly interface with RagCode MCP via SSE protocol without complex configuration files or binary dependencies.
Get deep, critical, NeurIPS/ICML-style peer reviews of your research, paper drafts, and experimental setups using external LLMs via Codex MCP.