Skill UI

A comprehensive workflow for advanced AI applications using Mistral AI. This guide covers generating text and code embeddings (1024 dimensions), implementing semantic search via cosine similarity, and building robust RAG pipelines. It also demonstrates advanced tool augmentation using function calling, allowing LLMs to interact with external APIs and databases.

Automatically collects comprehensive diagnostic evidence for Mistral AI support tickets. This bundle includes environment details (OS, Node/Python versions), installed SDK versions, API connectivity test results, available models, and recent error logs. It is essential for reproducing and diagnosing persistent issues when contacting technical support.

This comprehensive guide provides minimal working examples for integrating with the Mistral AI API. It demonstrates core LLM functionalities including basic chat completion, streaming responses, multi-turn conversations, and generating structured JSON output. Use this guide when starting a new Mistral integration, testing API connections, or learning advanced AI patterns using TypeScript or Python.

This skill provides a comprehensive framework for configuring Mistral AI across distinct environments (development, staging, and production). It manages environment-specific secrets (using GCP, AWS, or Vault), defines critical parameters like default models, rate limits, and feature flags, ensuring secure, isolated, and reliable deployments for AI services in multi-stage pipelines.

A comprehensive guide detailing essential security measures for integrating Mistral AI models. Covers API key management, defending against prompt injection, implementing content moderation, sanitizing outputs, and secure logging practices, ensuring robust and safe application development.

Guides researchers through drafting publication-ready machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM by combining expert writing philosophy with LaTeX templates, citation verification APIs, and collaborative workflows.

Combines fine-tuned models with Mergekit to blend math, coding, and chat expertise without retraining, covering SLERP, TIES, DARE, Task Arithmetic, and linear merges for rapid experimentation and deployment.

Automates News API operations via Rube MCP/Composio by first discovering tool schemas, confirming connections, and executing RUBE_MULTI_EXECUTE_TOOL flows with schema-compliant arguments for repeatable news workflows.

Automate OpenAI API workflows through the Composio MCP integration, covering text and multimodal responses with structured outputs, embeddings, image generation, and model discovery.

This guide provides comprehensive steps to install and authenticate the OpenEvidence SDK/API. It covers setting up environment variables, installing required packages (supporting both Node.js and Python), and verifying the connection using sample code. This is essential for developers integrating OpenEvidence's medical AI capabilities for clinical decision support and evidence-based queries into a new application.

This guide provides a comprehensive quickstart for integrating with OpenRouter, a unified endpoint for accessing multiple large language models (LLMs). It includes examples in cURL, Python, and TypeScript, demonstrating how to send chat completion requests, handle complex JSON responses, manage model parameters, and calculate usage cost across various providers (e.g., Llama, GPT-4o, Claude). Ideal for developers testing API connectivity or building multi-model applications.

This workflow executes a single-query search using the Perplexity Sonar model, ensuring all returned answers are web-grounded and accompanied by inline citations. It is essential for building high-trust applications, fact-checking tools, or any system requiring verifiable, real-time information retrieval. The process includes API interaction, detailed response parsing (answer, citations, search results), and formatting the output into a usable, source-linked format.