데이터·AI 스킬

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.

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• Generate image captions and content tags
• Extract text from images
• Locate objects and people in photos

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.

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• List connected Azure resources
• Create or update search indexes
• Implement access to model evaluations

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.

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• Analyzing sentiment about specific aspects of a review
• Detecting and redacting personal information
• Extracting names and key phrases from text

Develop AxonX plugins and manage quantitative finance research through the command line or MCP, an agent connection interface. Track data preparation, training, prediction, and historical strategy tests using execution status, logs, artifacts, and dependencies.

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• Develop research plugins with defined inputs and outputs
• Run data preparation, training, prediction, and backtests
• Investigate failed tasks through status and logs

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.

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• Submitting training jobs and monitoring logs
• Registering data and model versions
• Connecting preprocessing and training steps

Helps build Python workflows that extract information from documents, images, audio, and video using Azure AI Content Understanding. Covers document text, timed transcripts, video frames, and invoice fields for search and automated processing.

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• Preparing document content for AI search
• Transcribing audio with timestamps
• Extracting transcripts and key frames from video

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.

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• Extracting text and tables from PDFs
• Reading invoice and receipt fields
• Training models for specialized documents

Helps build AI agents in .NET with persistent conversation threads. Covers messages, execution status, streaming responses, document search, and custom function calls using Azure.AI.Agents.Persistent, with examples for creating agents and cleaning up resources.

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• Build an AI assistant that retains conversation threads
• Answer questions using uploaded documents
• Connect an agent to custom functions

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.

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• Planning an autonomous coding agent
• Designing tool and function calling interfaces
• Defining permissions and approval steps

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.

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• Designing an agent's planning and execution loop
• Breaking a complex goal into manageable tasks
• Reviewing self-correction and reliability patterns

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.

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• Converting celestial coordinates for an observing location
• Reading FITS images and inspecting their headers
• Matching objects across astronomical catalogs

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.

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• Building prospect lists from business listings
• Collecting competitor posts and ads
• Gathering reviews for analysis

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.

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• Investigate structure missed by cosine similarity
• Include contextual roles in RAG retrieval
• Compare graph-based and cosine search results

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.

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• Designing data pipelines and task dependencies
• Scheduling recurring batch jobs
• Testing Airflow workflows locally

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.

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• Collect historical stock prices for analysis
• Compare company financial statements
• Track forex and crypto prices

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.

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• Review claims without automatic agreement
• Ask for conclusions supported by evidence
• Reassess a position when new evidence appears

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.

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• Evaluating claims under deadline pressure
• Reviewing requests that invoke authority
• Responding to demands to endorse unsupported claims

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.

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• Send page view and purchase events
• Retrieve a specific user's event history
• Update user plans and other properties

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.

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• Designing events to track product usage
• Investigating drop-offs before signup or purchase
• Comparing weekly retention across user groups

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.

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• Planning an AI SaaS product for a specific problem
• Designing prompts for product features
• Balancing model choices, costs, and user experience

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.

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• Building an AI assistant that answers from company documents
• Improving search with keyword and vector retrieval
• Designing AI agents with multiple specialized roles

Plan AI features for products, covering language model integration, RAG architecture that retrieves information for answers, prompt design, user experiences people can trust, and cost optimization. Use it to guide development decisions for production use.

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• Plan language model integration in a product
• Consider RAG architecture for reference-based answers
• Review prompt design and AI user experience

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.

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• Restructuring a lengthy CLAUDE.md
• Reviewing instruction token usage
• Preparing rules for migration between AI tools

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.

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• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment