llmintegration
Multi-model LLM integration patterns for Claude, GPT, Gemini, and Ollama. Features API handling, prompt engineering, token management, and model-agnostic orchestration.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
153 skills found
Multi-model LLM integration patterns for Claude, GPT, Gemini, and Ollama. Features API handling, prompt engineering, token management, and model-agnostic orchestration.
Audit outbound network requests and detect data exfiltration patterns in OpenClaw skills to ensure secure outbound communication.
Manage YNAB budgets, track spending, and automate financial reports via API. Features include transaction logging, goal monitoring, and automated budget analysis.
A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.
Automated setup and configuration of Model Context Protocol (MCP) servers for Claude Code to enable seamless integration with external databases, APIs, and file systems.
Implement a full Model Context Protocol (MCP) stack in Rails. Connect to external servers, expose your Rails app as an MCP server, or manage subprocess MCP containers via Docker with OAuth 2.1 PKCE support.
Deprecated skill for GTM prospecting, lead enrichment, and outbound workflows. Use the 'deepline-gtm' skill for all GTM engineering tasks.
Local speech-to-text transcription using the OpenAI Whisper CLI, providing private, high-accuracy audio processing without external API keys.
Standardized detective skill integration for agent roles. Maps agents to code-analysis skills and enforces claudemem usage for memory-indexed code investigation.
Generates comprehensive API references, user manuals, and architectural system documentation directly from your codebase and technical specifications.
Lightweight MCP (Model Context Protocol) connection handler supporting stdio, SSE, and streamable HTTP transports for seamless server integration.
Accelerate software delivery by shifting testing to the earliest development phases, using AI-driven requirements validation, TDD, and automated CI pipelines to reduce defect costs.