Skill UI

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

This module provides a centralized interface for the AgentHub message board. It allows multiple AI agents and the coordinator to communicate, track tasks, and log results through structured markdown posts across dedicated channels (Dispatch, Progress, Results). It is essential for monitoring complex multi-agent workflows and ensuring transparent collaboration and auditing.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

Use Clarvia AEO (Agent Experience Optimization) to score any MCP server, API, or CLI tool for agent-readiness. It evaluates 15,400+ AI tools across four dimensions—API accessibility, data structuring, agent compatibility, and trust signals—ensuring the components are robust enough for advanced AI agent workflows and dynamic selection processes.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

Provides OpenClaw agents with local persistent memory via MemOS, managing installation, configuration, upgrades, and a memory viewer for secure offline workflows.

Automate routine HelpDesk tasks using the dedicated Rube MCP toolkit. This skill allows users to list, browse, and paginate through support tickets, manage predefined agent views, retrieve canned responses, and inspect custom field definitions, streamlining entire support workflows and improving operational efficiency.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

The GitHub Copilot SDK empowers developers to embed advanced, agentic AI workflows directly into their proprietary applications. It provides a programmatic interface to Copilot's production-tested agent runtime, allowing you to define complex agent behaviors, manage tool invocation, process file edits, and implement real-time streaming responses. Ideal for building custom, highly intelligent tools and automating complex tasks across multiple languages (Python, TypeScript, Go, .NET).

Provides an end-to-end kit for building Microsoft 365 Copilot declarative agents across basic, enterprise, and validation workflows, with TypeSpec definitions, JSON schema validation, and Agents Toolkit integration for planning, testing, deployment, and optimization.