Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
Helps implement document processing in TypeScript with Azure, extracting text, tables, invoice fields, and receipt details. Covers ready-made models, custom model training, document classification, authentication, and waiting for analysis results.
Build Python AI applications on Microsoft Foundry with azure-ai-projects. Provides examples for creating agents, connecting tools, managing conversations and project resources, and running evaluations through Foundry and OpenAI-compatible clients.
Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.
Implement Java workflows for Azure AI Foundry projects using SDK examples for authentication, connected resources, datasets, search indexes, and model evaluations. The skill also covers client setup, paginated resource lists, and error handling.
Manage Azure Machine Learning resources from Python with SDK v2. The skill provides implementation examples for registering data and models, configuring compute and environments, submitting training jobs, monitoring logs, and connecting steps into pipelines.
Build Python text analysis with Azure AI Language to detect sentiment, names, key phrases, languages, and personal information. Examples also cover healthcare terminology extraction, combined analyses, authentication, and handling results for multiple documents.
Build Python image analysis with Azure AI Vision to generate captions and tags, locate objects and people, and extract text using OCR, or optical character recognition. It also provides suggested crop regions.
Build Java applications that detect unusual patterns in time-series data with Azure AI Anomaly Detector. Covers single measurements, related signals, trend changes, model training, and checking detection results.
Use Python to analyze astronomical data, convert celestial coordinates, units, and times, and read or write FITS astronomy files. It also guides catalog matching and calculations of cosmic distances and ages using cosmological models.
Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.
Design autonomous coding agents with patterns inspired by Cline and OpenAI Codex. Explore tool calling interfaces, permission and approval systems, browser automation, and workflows where people review or guide the agent's actions.
Build vector search that considers both semantic similarity and patterns in how items connect. Use graph features to explore structure in embeddings, numerical representations of content, and compare retrieval results with conventional similarity search.
Collect social posts, business listings, reviews, and other web data by selecting from over 55 Apify Actors, ready-made extraction tools. Run the selected tool, summarize results, and export data as CSV or JSON.
Plan product analytics with event tracking, conversion funnels that reveal drop-offs, and retention analysis by user group. Includes PostHog examples and guidance for evaluating A/B tests, defining a core product metric, and designing dashboards.
Automate Amplitude tasks through Rube MCP and Composio. Send tracking events, retrieve user activity, update user properties, and manage cohorts, saved groups of users, with checks for identifiers and asynchronous update status.
Retrieve financial data for market and company analysis, including stocks, forex, crypto, financial statements, and economic indicators. Access over 20 years of historical data and more than 50 technical indicators, which summarize price and trading patterns.
Assess claims when a request pushes for agreement, invokes authority, or creates artificial urgency. The skill guides responses that separate evidence from persuasion, present strong counter-evidence first, and clearly state what remains uncertain.
Build Apache Airflow data pipelines with clear task dependencies and schedules. This skill guides workflow design, retries, monitoring, local testing, and production deployment, with patterns for investigating failed runs.
Help an AI coding assistant assess claims independently instead of automatically agreeing with the user. Responses lead with a conclusion and supporting evidence, while disagreements are evaluated for new information that could justify changing the assessment.
Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.
Helps design focused AI products built on APIs such as OpenAI and Anthropic. Covers product strategy, prompts, model selection, user experience, usage tracking, and cost management for tools aimed at solving specific customer problems.
Convert human-written CLAUDE.md instructions into a structured label format for AI agents. Use it to review lengthy rules, reduce instruction overhead, and prepare prompts for migration between AI tools while preserving the intended rules.
Improve workflows involving multiple AI agents by measuring bottlenecks, adjusting workload distribution, and managing model costs. This design guide helps compare changes against a measured baseline while checking quality, latency, and resource use.
Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.