github-workflow-automation
AI-driven GitHub Actions automation featuring swarm-based workflow orchestration, intelligent CI/CD pipeline management, and autonomous repository maintenance.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
252 skills found
AI-driven GitHub Actions automation featuring swarm-based workflow orchestration, intelligent CI/CD pipeline management, and autonomous repository maintenance.
Expert assistant for designing and optimizing production-grade Trigger.dev background jobs, AI workflows, and resilient asynchronous task architectures in TypeScript.
End-to-end autonomous research agent: from idea generation and literature review to experiment execution, adversarial review loops, and paper writing.
Cross-agent interaction skill via ANP protocol. Use decentralized identity (DID) to discover and invoke remote agents like maps, booking, and logistics services across the ANP network.
Conduct systematic literature reviews across PubMed, arXiv, and Semantic Scholar with AI-driven synthesis, verified citations, and mandatory schematic visualization.
Translate research papers (markdown) while preserving LaTeX formulas, code blocks, and images, with support for batch processing, retries, and portable bundles.
Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.
Expert Kokoro TTS implementation skill for real-time, secure, and offline voice synthesis in JARVIS-style assistants. Features streaming output, prosody control, and performance-optimized audio generation.
Discover and recommend combinations of agent skills to complete complex, multi-faceted tasks using Maximum Quality or Minimum Dependencies strategies.
Enable long-running, multi-session autonomous development tasks with state tracking, resumable execution, and dual-agent planning-execution workflows.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement in AI agents.
Generate finite-difference stencils, select optimal numerical schemes for PDEs/ODEs, and perform truncation error analysis to improve simulation accuracy.