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Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Restrict which tools an agent can call
• Check suspicious inputs before execution
• Require human approval for sensitive operations

Manage the credentials Arize uses to call LLM providers such as OpenAI and Anthropic. Create, inspect, update, or delete connections, configure allowed models and authentication methods, and retrieve connection IDs for use in evaluations.

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• Connect an LLM provider to Arize
• Review existing connections and allowed models
• Rotate stored provider API keys

Inspect existing LLM application records in Arize to investigate errors, slow operations, or behavior changes. Export traces, which group related operations, or individual operations and conversation sessions as JSON using the ax command-line tool.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate errors in an LLM application
• Review the inputs and outputs of a conversation
• Find slow operations in recorded traces

Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check answers for unsupported claims
• Score experiment results for correctness
• Continuously evaluate new production records

Design Azure infrastructure from plain language or visualize existing resources as interactive diagrams. Refine the architecture through conversation, then generate Bicep infrastructure code, review it, and validate changes before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new Azure architecture
• Visualizing existing Azure resources
• Planning cost or security changes

Create and analyze AI model evaluation experiments in Arize using the ax CLI, a command-line tool. Collect real model outputs, export results, compare evaluation scores, and identify examples where a new model performs worse.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same evaluation dataset
• Finding examples that perform worse after a model change
• Exporting experiment results for further analysis

Guides development of ChatGPT apps with interactive widgets using mcp-use and OpenAI Apps SDK, covering server tools, React interfaces, and shared state. This skill is deprecated and directs users to mcp-app-builder.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a ChatGPT app with interactive widgets
• Implement MCP server tools and React widgets
• Manage widget state and context shared with ChatGPT

Add or verify model support in SGLang's Rust processor so chat requests produce the same prompt text and token IDs as Python. Use reference-generated test cases to find differences in tools, reasoning settings, conversation history, and errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding model support to sglang-processor
• Porting model rendering from sgl-router
• Rechecking prompt behavior after a Dynamo update

Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Research stock and cryptocurrency strategies through historical testing, factor analysis, and options pricing. Analyze trading journals, extract rules from profitable trades, and generate reports comparing simulated results with actual trading outcomes.

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• Testing strategies on historical market data
• Comparing predefined predictive factors
• Reviewing trading habits from broker CSV exports

Extract text from PDFs, Office documents, images, spreadsheets, and text files through one tool. Results share a common JSON structure, supporting document summaries, contract checks, and spreadsheet previews, with optical character recognition for scanned content.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing papers and reports
• Checking key clauses in Word contracts
• Previewing every sheet in an Excel workbook

Examine AI research directions and safety strategies through thinking models drawn from Ilya Sutskever’s public statements. The skill checks current facts when needed and presents a concise judgment with an everyday analogy.

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• Assessing AI research directions
• Examining model scaling and data limits
• Discussing AI safety strategies

Examine AI product reliability, learning methods, and model limitations through frameworks drawn from Andrej Karpathy’s public statements. Assess the gap between demonstrations and real use, uneven capabilities, and where people should retain control.

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• Assessing AI product reliability beyond a demo
• Planning hands-on study of neural networks
• Examining LLM strengths and failure patterns

Set up repeatable security tests for LLM apps with Promptfoo and DeepTeam. Connect them to automated build pipelines, detect renewed vulnerabilities to malicious instructions or data leakage, and compare results across model or prompt changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running automated attack tests on every code change
• Blocking merges when security tests fail
• Comparing security across model or prompt versions

Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Apply consistent coding practices across TypeScript, JavaScript, React, and Node.js projects. Get guidance on naming, types, error handling, API design, file organization, and tests to make code easier to read and maintain.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aligning coding conventions across a team
• Refactoring long functions and deeply nested code
• Reviewing React components and state updates

Generate and edit images through the OpenAI Image API using a bundled command-line tool. Create product shots, illustrations, and interface mockups, replace backgrounds, modify selected areas, or produce batches while specifying details to preserve.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating product shots and website images
• Removing or replacing image backgrounds
• Editing selected objects or text in an image

Design visually distinctive websites, landing pages, and portfolios as React components, reusable pieces of a web page. Use industry references to guide typography, color, layout, motion, and relevant 3D visuals that fit the brand.

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• Build a brand website with a distinctive visual identity
• Design a landing page around a product
• Create a portfolio with an original layout

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

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• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Design focused products built around AI APIs, with guidance on prompts, model selection, output validation, and user experience. Track usage and set cost limits to connect product behavior with operating costs and pricing decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an AI service for a specific task
• Create prompt templates and validate responses
• Track API costs and set usage limits

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing an LLM for CPU-only use
• Running a local model on Apple Silicon
• Comparing model size and quality across quantization levels