Skill UI

Guide to automate Adrapid operations through Composio's toolkit using Rube MCP, covering prerequisites, tool discovery, execution patterns, and best practices for schema compliance, connection checks, and session reuse.

Guides connecting Rube MCP to Composio’s Adyntel toolkit, searching for live tool schemas, validating connections, and executing multi-tool workflows while handling session reuse, memory, and pagination requirements.

Automate Composio’s Aero Workflow toolkit through the Rube MCP bridge, discovering current tool schemas with `RUBE_SEARCH_TOOLS`, validating connections, and dispatching schema-compliant calls to `RUBE_MULTI_EXECUTE_TOOL` for safe workflow operations.

Automate Aeroleads operations via Composio’s toolkit using Rube MCP: search for current tool schemas, confirm active connections, and invoke multi-tool executions with schema-compliant arguments and session handling.

Guides automating Affinda tasks through Composio's Rube MCP client, detailing connection setup, tool discovery, multi-tool execution, and reminders about schema searches, sessions, and pagination safety.

Automates Affinity tasks through Composio’s toolkit by using the Rube MCP commands to search for current tool schemas, validate the affinity connection, and execute schema-compliant workflows across RUBE_SEARCH_TOOLS, RUBE_MANAGE_CONNECTIONS, and RUBE_MULTI_EXECUTE_TOOL.

AFL++ is an advanced fork of AFL that runs multi-core campaigns, supports diverse mutations, and targets large C/C++ codebases with stable tooling via Docker or host builds.

Streamlines Agencyzoom operations through Composio’s toolkit by driving Rube MCP searches, connection checks, and multi-execute calls before any action, making it ideal for automation engineers embedding CRM tasks into composable workflows.

Automatically detects and loads specialized agent instructions from the `AGENTS.md` file in the current working directory. This skill seamlessly integrates project-specific rules and behavioral guidelines into the AI's session context, ensuring the AI operates according to project standards without manual intervention. It is crucial for maintaining specialized agent behaviors across different development tasks and directory changes.

Design multi-agent system architectures using patterns like supervisor, swarm, and pipeline. Generate provider-ready tool schemas for Anthropic and OpenAI. Evaluate execution logs to identify bottlenecks, latency, and costs. Use for complex agent workflows requiring structured design and optimization, not simple automation scripts.

This skill provides a comprehensive guide to designing and developing autonomous AI agents for code plugins. It covers the required file structure (YAML frontmatter), best practices for defining agent roles, designing robust system prompts, and setting up clear triggering conditions. Use this when you need to conceptualize, structure, or refine an agent's capabilities to handle complex, multi-step tasks autonomously.

A comprehensive framework for rigorously testing and benchmarking LLM agents. It moves beyond simple pass/fail checks to assess complex behaviors, reliability metrics, and capability consistency across multiple runs. Ideal for production monitoring and identifying subtle failure modes in advanced AI agents.