'custom mode' 검색 결과

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

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Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

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Generates python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my model perform", "compare models", or other similar requests.

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Applies Steve Blank's Customer Development methodology from The Four Steps to the Epiphany. Use when a startup is searching for customers and a business model before scaling. Covers Customer Discovery, Customer Validation, Customer Creation, and Company Building. Triggers include 'we built it but no one's buying', 'should we hire salespeople yet', 'how do we find our first customers', 'we're burning cash and sales aren't scaling', 'are we in a new or existing market', 'when do we scale'. NOT for companies that have already crossed the chasm into mainstream (use Crossing the Chasm instead), not for optimizing an existing sales funnel, not for product development methodology (this is its companion, not replacement).

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Customer segmentation model builder — tiers customers by ARR, health, and expansion potential; defines CS motion per tier; maps resource allocation. Use when asked to "segment our customers", "define our CS tiers", "how should we allocate CS resources", "build a customer segmentation model", or "who gets high-touch vs. digital".

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TRL로 언어 모델을 추가 학습해 지시와 사람의 선호에 맞는 답변을 만들도록 안내합니다. 데이터 준비, 학습 방식 선택, 보상 모델 학습과 평가를 다룹니다.

• 답변 예시로 지시 수행을 학습합니다
• DPO로 답변 선호를 학습합니다
• 보상 모델을 쓰는 RLHF를 구성합니다

언어별 자료에 따라 Claude 앱을 구현하고 유지보수합니다. 공식 SDK 호출, 도구 연동, 모델 이전, 비용 검토와 평가 절차 마련을 다룹니다.

• 앱에 Claude API 연동
• 맞춤 도구를 쓰는 에이전트 구현
• 기존 Claude 연동의 모델 이전

공식 자료를 조사해 Codex 설정과 요금, OpenAI API·제품 사용법을 출처와 함께 안내합니다. 모델 선택과 이전, 프롬프트 조정, 문제 원인 파악에도 활용합니다.

• Codex 설정 확인 및 문제 원인 파악
• 모델 비용·속도·품질 비교
• 모델 이전과 프롬프트 조정 검토

Build Claude applications using guidance for the official Anthropic SDK. Choose an integration approach, implement streaming and tool use, and follow workflows for model migration, prompt audits, evaluation, and cost optimization.

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• Build a summarization app with Claude API
• Choose how to run an agent with custom tools
• Migrate existing code to a newer Claude model

Get guidance on Codex, OpenAI APIs, and ChatGPT Work using official documentation with citations. Use it to investigate settings and errors, compare models, plan migrations, or work on prompts and API integrations.

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• Check Codex settings and troubleshoot errors
• Compare models for a particular task
• Plan a model migration and prompt changes

Design custom laboratory parts as adjustable build123d models and export STEP, STL, and DXF fabrication files. Check mating dimensions against standards, test geometry, and visually review holders, mounts, microfluidic parts, and other research fixtures before fabrication.

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• Designing holders for microplates, cuvettes, and tubes
• Creating mounts and adapters for optical tables
• Modeling microfluidic chips and molds

Find professionals with relevant experience and request a written or voice answer, or a short call, through Instant Expert. Use it for customer discovery and practical advice, with a free simulated test mode to try the process.

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• Gathering firsthand experience from practitioners
• Interviewing potential customers
• Requesting a short call with a named professional

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Turn descriptions of 3D shapes and CAD tasks into FreeCAD Python scripts. Create models with adjustable dimensions, automate repeated operations with macros, convert meshes and solids, or build custom dialogs and workbench tools.

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• Create 3D models from written descriptions
• Build models with adjustable dimensions
• Automate repeated CAD operations with macros

Connect a local CopilotKit agent to Slack through a managed Intelligence Channel. The skill guides dedicated Slack app creation, credential setup, runtime configuration, and checks that a human mention receives an actual reply.

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• Setting up a dedicated Slack bot for a local agent
• Investigating a Channel stuck waiting for its runtime
• Diagnosing mentions that receive no reply

Apply psychology and behavioral science to marketing decisions. The skill selects relevant thinking models, explains customer behavior, and suggests ethical changes to pricing, messaging, purchase journeys, and campaign priorities.

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• Understand why customers hesitate to buy
• Review pricing plans and how prices are presented
• Simplify signup and purchase journeys

Guides Python agent development with PydanticAI, covering tool calls and structured outputs checked against defined data types. Explains service injection, provider switching, streaming responses, and tests that run without calling a real language model.

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• Building agents that return validated structured data
• Connecting customer support agents to order data
• Testing agent logic without real model calls

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

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• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Draft customer replies using order details, payment status, support history, and refund policies, matching the owner's writing style. With connected tools, prepare approved refunds and identify Shopify orders that need attention before customers contact support.

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• Drafting replies to order status questions
• Preparing policy-based responses to refund requests
• Checking previous support exchanges before replying

Design or review SaaS pricing by choosing what customers pay for, defining plan features, and assessing price points. Create pricing page outlines, customer research questions, and price increase plans with communication templates and risk scenarios.

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• Designing pricing plans for a new SaaS product
• Reviewing existing prices and plan features
• Planning a price increase and customer notices

Helps develop Handsontable’s React wrapper, covering components, custom cell editors, and preserving selections during settings updates. It also guides TypeScript prop definitions and declaration checks so editor suggestions remain available.

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• Preserve cell selections when React props change
• Develop custom cell editors
• Fix missing HotTable and HotColumn prop suggestions

Import FBX 3D models into CARLA as props that can be placed through its Python API. Handles category tags, measured dimensions, asset registration, and spawn checks for individual files or folders of models.

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• Add custom benches and signs to CARLA
• Import a folder of models grouped by tag
• Check model dimensions and spawning

Generate scientific chart examples from bundled Python scripts using repeatable simulated data. Choose from templates such as raincloud plots, correlation matrices, and ROC charts, then receive PNG, PDF, SVG, and editable script files.

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• Generating sample scientific charts from templates
• Comparing correlation and prediction chart layouts
• Customizing labels and legends in generated charts

Select technologies for AI and machine learning features in .NET 8+ applications. Get task-specific plans or implementation guidance for structured predictions, text generation, document retrieval, tool-using agents, and running pretrained models locally or in production.

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• Choosing libraries for classification or prediction in .NET
• Adding text generation or summarization to an application
• Designing document retrieval with source-backed AI answers