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Found 1,573 Skills
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Full changelog infrastructure from scratch. Greenfield workflow. Installs semantic-release, commitlint, GitHub Actions, LLM synthesis, public page.
Query Langfuse traces for debugging LLM calls, analyzing token usage, and investigating workflow executions. Use when debugging AI/LLM behavior, checking trace data, or analyzing observability metrics.
Verifies implementation against specifications by checking requirement fulfillment, task completion, and contract implementation. Generates a fulfillment report with coverage metrics. Always run after /speckit.implement completes.
한글(HWP/HWPX) 문서를 다양한 포맷(Text, HTML, ODT, PDF)으로 변환하고, Markdown/HTML을 HWPX로 생성하는 작업을 도와줍니다. LLM/RAG 파이프라인을 위한 문서 처리, 청킹, LangChain 연동을 지원합니다.
Interactive tutorial that guides engineers through building their own coding agent (agentic loop) from scratch using raw HTTP calls to an LLM API. Supports Gemini, OpenAI (and compatible endpoints), and Anthropic. Supports TypeScript, Python, Go, and Ruby. Detects progress automatically. Use when someone says "build an agent", "teach me agents", or "/build-agent".
Access real-time, continuously refreshed investment context through the Primary Logic External API under /v1. Use when asked to power Codex, Claude Code, OpenClaw, or custom agents with LLM-ranked relevance and impact signals from podcasts, articles and news, X/Twitter, Kalshi, Polymarket, earnings calls, filings, and other monitored sources across public and private companies for decision support or user-controlled trading workflows.
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Ready-to-use prompt templates for specialized agents. Use when building n8n workflows, AI integrations, or sales materials. Contains structured prompts for automation-architect, llm-engineer, and sales-automator agents.
Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.