Arize AI LLM 제공자 통합을 생성, 읽기, 업데이트 및 삭제합니다. OpenAI, Anthropic, Azure OpenAI 등 다양한 LLM 제공자와의 연결을 지원하며, 평가자 및 기타 Arize 기능에서 사용되는 자격 증명을 관리합니다.
이 스킬은 **Arize AI LLM 제공자 통합(integration)**을 관리하는 전문 도구입니다. 사용자가 'AI 통합', 'LLM 제공자 자격 증명', '통합 생성', '통합 목록 보기', '자격 증명 업데이트', '통합 삭제' 또는 'Arize에 LLM 제공자 연결'을 언급할 때 활성화됩니다. 주요 기능: - **통합 생성**: 새 LLM 제공자(예: OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM)에 대한 자격 증명을 저장하는 통합을 생성합니다. - **통합 읽기**: 기존 통합의 세부 정보(제공자 유형, 자격 증명 등)를 조회합니다. - **통합 업데이트**: 기존 통합의 자격 증명이나 설정을 업데이트합니다. - **통합 삭제**: 더 이상 필요 없는 통합을 삭제합니다. 이 스킬은 **Claude Code** 및 기타 에이전트 환경에서 작동하며, 사용자가 LLM 제공자 자격 증명을 안전하게 관리하고 평가자(evaluator) 및 기타 Arize 기능이 원활히 작동하도록 지원합니다.
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/arize-ai-provider-integrationInstalled? Explore more 코딩 & 개발 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
Arize AI 통합을 생성, 읽기, 업데이트 및 삭제합니다. 이 통합은 평가자(evaluator) 및 기타 Arize 기능에서 사용하는 LLM 제공자 자격 증명을 저장합니다. 모든 LLM 제공자(예: OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM)를 지원합니다. 사용자가 AI 통합, LLM 제공자 자격 증명, 통합 생성, 통합 목록 보기, 자격 증명 업데이트, 통합 삭제 또는 Arize에 LLM 제공자 연결에 대해 언급할 때 이 스킬을 사용하세요.
This repo contains 20 individual skills — each has its own dedicated page.
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
Instrument a webapp to send useful telemetry data to Azure App Insights
Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.
Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.
Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.
Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.
Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
Create ASP.NET Minimal API endpoints with proper OpenAPI documentation
Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.
Control Eight Sleep pods (status, temperature, alarms, schedules).
BluOS CLI (blu) for discovery, playback, grouping, and volume.
Create, search, and manage Bear notes via grizzly CLI.
Capture frames or clips from RTSP/ONVIF cameras.
Search GIF providers with CLI/TUI, download results, and extract stills/sheets.