Skill UI

A comprehensive CI/CD pipeline designed for LangChain applications. It automates quality assurance through fast, mocked unit tests and slower, more accurate integration tests utilizing real LLMs (e.g., OpenAI). It specifically validates the entire RAG pipeline and integrates tracing with LangSmith, ensuring robust deployment quality.

A comprehensive guide to configuring a structured, local development environment for LangChain applications. It covers setting up project architecture, managing dev dependencies, and implementing a two-tiered testing strategy: isolated unit tests using mocked LLMs (for fast, API-free testing) and robust integration tests using real API providers. Ideal for establishing a reliable, rapid iteration workflow.

A minimal example demonstrating how to implement basic tracing using the Langfuse SDK. It illustrates the hierarchy of traces, spans, and generations (LLM calls), providing code examples in TypeScript and Python for easy setup and integration testing.

This guide provides a comprehensive local development setup for LLM applications using Langfuse. It enables fast iteration cycles, real-time trace debugging, and hot reloading, making it ideal for developers building and testing complex AI workflows locally. Instructions cover environment setup, advanced SDK usage, and optional self-hosted Docker deployment.

This guide demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.

A comprehensive guide for developing, optimizing, and standardizing skills for AI agents. This resource provides a decision tree for choosing the right architecture (Tier 1-3), incorporating best practices like CSO (LLM SEO) and Anti-Rationalization, and ensuring compliance through rigorous testing and checklists. Essential for robust AI system building.

Automate the Continuous Integration and Continuous Deployment (CI/CD) workflow for applications leveraging Together AI's OpenAI-compatible API. This skill sets up comprehensive GitHub Actions workflows to run unit tests (mocking completions and embeddings) on every Pull Request, and full integration tests (live API calls) upon merging to the main branch. It ensures robust validation of prompt formatting, model selection, and API connectivity for various LLMs.

Turn any OpenAPI or Swagger spec into an MCP server using the mcp-use TypeScript SDK. Maps REST operations to LLM-callable tools with zod schemas, auth wiring, live testing, and one-command deploy.

An open-source automation framework for conducting multi-turn, adversarial red-teaming against conversational LLMs. PyRIT simulates real-world attacker behavior using advanced orchestrators (Crescendo, TAP) and scorer feedback loops. It is designed for comprehensive security assessments of AI agents and chatbots, going far beyond single-shot prompt testing to evaluate robustness under gradual or adaptive attacks.

Detailed methods for red-teaming and penetration testing of LLM applications. This skill extracts sensitive system prompts, secrets, and embedded logic using advanced techniques like jailbreaking, encoding tricks, and automated scanners (Garak, Promptfoo), ensuring compliance with OWASP LLM07:2025.