Skill UI

A continuous file-based planning system for multi-step AI agents. Maintains task_plan.md, findings.md, and progress.md files on disk, injecting project-specific planning context into lifecycle hooks. Auto-restoration reads only project planning files. Can inspect local agent session metadata with session-catchup.py --metadata, or replay limited excerpts with --replay. Optional controlled mode requests continuation only when supported by host. Never executes declared Markdown commands. No network data upload path. Used for research or work requiring 5+ tool calls.

Generate a TypeSpec API plugin tailored for Microsoft 365 Copilot, wiring REST operations, authentication flows, confirmation cards, and Adaptive Cards into a guided workflow so Copilot can interact with external services securely.

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Adapt Gymnasium/PettingZoo environments to published PufferLib 3.0.0 or work with redesigned native 4.0 source line with Python 3.10+ and Torch 2.9+. Includes safe defaults for CPU-only, network-free tools with strict JSON output and dependency-free bundled CLIs.

This workflow converts complex clinical encounter notes into plain-language, patient-facing After-Visit Summaries (AVS). It structures key information such as diagnosis, next steps, and medications. The system supports multi-language translation and facilitates pushing the final, readable summary directly to patient portals using FHIR standards.

This comprehensive guide provides a deep dive into migrating AI applications from major LLM providers (like OpenAI or Anthropic) to the Cohere platform. It covers critical technical steps, including API call adaptation, re-vectorizing embeddings, migrating tool definitions, and implementing the Adapter Pattern for seamless, gradual re-platforming of existing AI services.

This advanced skill orchestrates a comprehensive, multi-agent review of legal contracts. It analyzes risk, compliance, missing protections, and plain-English readability simultaneously. By simulating a team of legal specialists, it aggregates findings into a single executive report, complete with a quantifiable Contract Safety Score (0-100). Ideal for founders, freelancers, or small businesses needing expert legal insights without retaining full-time counsel.

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

This bundle collects comprehensive diagnostic evidence for ElevenLabs services. It automatically gathers SDK versions, API connectivity status, quota usage, voice inventories, and available model metadata. Use this tool when encountering persistent bugs or preparing comprehensive support tickets for technical support.

A remote MCP server designed for AI agents to dynamically discover, inspect, and verify external tools, APIs, and services. It allows agents to search a curated index of AI-ready websites, check for machine-readable endpoints (like OpenAPI or MCP), and confirm if an endpoint responds correctly to JSON-RPC, enabling robust, dynamic agent workflow building.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.