Skill UI

Guides using Composio's API Sports toolkit via Rube MCP to discover current tool schemas, manage active connections, and execute multi-tool operations while enforcing schema compliance, memory inclusion, and session reuse.

An advanced guide for optimizing API interaction with Apollo.io. This skill covers critical performance enhancements including implementing connection pooling, setting per-endpoint time-to-live (TTL) caching, utilizing bulk enrichment endpoints for efficiency, and controlling concurrency during parallel searches. It also demonstrates how to slim down large API response payloads to reduce memory and bandwidth usage.

Automates Appcircle workflows through Composio’s Rube MCP toolkit, guiding users to discover current tools, validate connections, and execute schema-compliant calls with session reuse and memory handling.

Automates Appcircle workflows through Composio’s Rube MCP toolkit, guiding users to discover current tools, validate connections, and execute schema-compliant calls with session reuse and memory handling.

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

Atheris is a coverage-guided Python fuzzer built on libFuzzer for fuzzing pure Python code and C extensions, with AddressSanitizer support to catch memory corruption and an easy harness setup for command-line fuzz runs.

Atheris is a coverage-guided Python fuzzer built on libFuzzer for fuzzing pure Python code and C extensions, with AddressSanitizer support to catch memory corruption and an easy harness setup for command-line fuzz runs.

A specialized skill for writing highly efficient, memory-safe, and robust C code. This guide focuses on best practices for systems programming, including rigorous memory management (malloc/free), advanced pointer arithmetic, POSIX compliance, multi-threading (pthreads), and embedded systems constraints. It emphasizes using static analysis tools, implementing comprehensive error handling, and ensuring performance through proper profiling. Ideal for complex, low-level system tasks.

Automate Cal operations through Composio’s toolkit by routing every step via Rube MCP APIs; always begin with schema discovery, verify Cal connections, then execute inspected tools with exact arguments, session reuse, and memory placeholders.

Automate Callerapi operations by orchestrating Rube MCP’s Composio toolkit: search the latest tool schemas, verify the Callerapi connection, and run workflows through RUBE_MULTI_EXECUTE_TOOL with schema-compliant arguments and memory/session handling.

Orchestrates Cardly operations through Rube MCP and Composio, guiding users to search current tool schemas, connect the Cardly toolkit, and execute workflows via schema-compliant RUBE_MULTI_EXECUTE_TOOL calls with managed sessions and memory.