Skill UI

An interactive tool designed to discover, select, and assemble specialized teams of AI agents. It supports structured agent collections across different domains (e.g., Engineering, Marketing). Users select multiple roles for a specific task, and the skill launches them in parallel, synthesizing their independent findings into a unified report, including consensus and conflict analysis.

This skill dispatches computationally intensive quantitative trading jobs (such as multi-year walk-forward analysis, large Monte-Carlo simulations, parameter sweeping, or deep model training) to a managed cloud agent runtime. It is designed for large-scale, production-grade backtesting that exceeds local machine capacity, ensuring robust execution and artifact management via Anthropic's cloud services.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This tool allows users to create and manage advanced WASM agents within a secure, sandboxed environment. It ensures complete isolation during code execution, making it ideal for running untrusted code, experimenting with complex agent configurations, or developing portable applications that require guaranteed safety without accessing the host system resources.

This tool suite provides functionality to interact with a community gallery for WebAssembly (WASM) agents. Users can browse available agents, search for specific functionalities, install pre-built agents, publish their own creations, and export agents for packaging. It streamlines the process of sharing and utilizing modular WASM components.

Manages the creation and orchestration of complex, multi-step workflows. Supports two powerful methods: persistent MCP templates for human-gated, resumable pipelines, or native JavaScript scripts for advanced, programmatic agent fan-out. Ideal for complex business logic such as auditing, research, or multi-stage data processing.

Ruflo is a powerful, cross-agent orchestration layer designed to manage complex, multi-step AI coding workflows. It supports coordinating large swarms of AI agents, managing persistent memory through semantic search, and executing sophisticated pipelines using 30+ specialized plugins. Ideal for large-scale development tasks, system benchmarking, and deep research requiring coordinated agent effort.

A sophisticated, multi-agent workflow designed for comprehensive code reviews of GitHub Pull Requests. It performs deep analysis by simulating multiple specialized perspectives, checking PR eligibility (handling draft/merged status and follow-ups), and managing large or complex diffs. The skill synthesizes high-signal, actionable feedback by integrating parallel reviewers' findings with historical PR discussions, ensuring robust code quality before merging.

A comprehensive, human-guided onboarding skill designed for users new to AI coding agents and Power BI agentic development. It provides structured setup help, deep conceptual explanations, and conversational guidance, mimicking a mentor's session. Modes include concise setup assistance and deep conceptual learning. Use cases: Setting up a new agent environment, understanding core AI concepts (models, prompts, tools), or auditing existing agent configurations.

This skill performs a comprehensive, setup-wide health check for agentic developers. It audits various components—including skills, memory, tools, configuration, and workflow habits—to identify inefficiencies and potential improvements. It provides a detailed report and guides the user through applying targeted fixes, ensuring the agent environment remains lean and optimized.

SkillOpt-Sleep enables an AI agent to undergo a simulated "sleep cycle" for continuous, measurable self-improvement. It harvests past sessions, mines recurring tasks, and re-runs them offline. It then consolidates findings into the agent's memory and skills, ensuring only validated improvements are adopted. This process maximizes long-term competence from short-term interactions.

This skill acts as an advanced orchestration protocol for reviewing GitHub pull requests. It is stack-agnostic, meaning it does not enforce its own rules. Instead, it dynamically discovers the project's existing standards—such as the test runner, architectural conventions, and requirement specifications—before running six specialized subagents in parallel. It provides a comprehensive, context-aware review and summary directly on GitHub.