Skill UI

Automate Astica AI workflows through Composio’s Rube MCP bridge by discovering tools with RUBE_SEARCH_TOOLS, managing Astica connections, and executing tasks via RUBE_MULTI_EXECUTE_TOOL while respecting dynamic schemas.

An expert system for designing and implementing advanced AI context architectures. It specializes in integrating intelligent memory systems, vector databases, and knowledge graphs to ensure AI applications maintain coherent, relevant, and scalable state across complex, multi-agent workflows. Essential for enterprise RAG implementation and stateful AI development.

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 guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.

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.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

This skill is designed for complex, multi-step tasks requiring coordination among multiple AI agents. It implements a robust workflow engine that first generates a detailed plan (task graph, topology) and then orchestrates parallel execution with adversarial verification. Use this when the task spans many files, requires multiple specialized roles, or involves an overall 'workflow' concept, ensuring structured, reliable, and auditable results.

Design task-local harnesses, evaluation gates, and reusable skill extraction for adaptive agent workflows. This skill helps build structured systems for one-off work, shared skills, and observable checkpoints. It defines input boundaries, tool bindings, and safety checks to ensure reliable automation without manual intervention.

This skill evaluates and assembles the smallest, most efficient set of AI agent skills required to achieve a complex, natural language goal. It dynamically derives multi-step workflows, searching local indices, registries, and external sources using four specialized lenses (need, operation, outcome, method). It prioritizes user-friendly plain-language outputs while rigorously verifying compatibility, safety, and overlap between candidate skills before recommending the final stack.

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Provides best practices for orchestrating complex AI workflows, optimizing context windows, and integrating enterprise knowledge bases.

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.

Plan first, then orchestrate parallel agents with adversarial verification via the local odw daemon. Use when a task spans many files or items and benefits from concurrent agents.