'custom mode' 검색 결과

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a deployable model and supported hardware
• Estimate hourly deployment costs before approval
• Check deployment progress and test predictions

Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan AWS hosting for a language model
• Choose a serving route for an embedding model
• Plan asynchronous inference for image generation

Use when choosing between Custom Metadata Types and Custom Settings, understanding hierarchical vs list settings, deployment behavior, governor limit implications, or accessing either from Apex and Flow. Trigger keywords: 'custom metadata vs custom settings', 'hierarchical settings per user profile', 'deployable config vs runtime settings', 'getValues getInstance in apex', 'flow get records custom settings'. NOT for modelling the CMT itself or protecting packaged defaults — use admin/custom-metadata-types. NOT for records users edit as business data — use admin/object-creation-and-design. NOT for secrets — use integration/named-credentials-setup. More trigger keywords: customSettingsType, customSettingsVisibility, SetupOwnerId, getOrgDefaults, getAll, DUPLICATE_VALUE in test, SeeAllData custom settings, enableAdvancedCSSecurity, customSettingAccesses, org default profile user override.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create a Business Model Canvas, a nine-part overview of how a business creates value and earns revenue. Connect customers, offerings, channels, operations, costs, and income to examine the business model and identify assumptions and risks.

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• Outlining a new business model
• Reviewing how an existing business works
• Explaining a business model to investors

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when creating a new Salesforce custom object: naming the object and setting its API name, selecting optional features (Activities, Chatter, History Tracking), choosing an org-wide default sharing model, and creating a tab. Triggers: 'create a custom object', 'new custom object setup', 'what sharing model should I choose', 'how do I create a tab for my object', 'object features like activities and history tracking'. NOT for designing the fields on the object - use admin/custom-field-creation. NOT for sharing rules or role hierarchy configuration - use admin/sharing-and-visibility. NOT for lookup-vs-master-detail and junction design - use data/data-model-design-patterns. More triggers: 'custom object records not showing in search', 'sharing model greyed out on my object', 'auto number restarted at 1 after deploy', 'cannot create a queue for my custom object', 'CustomObject deploy failed enableBulkApi', 'field history tracking shows no rows'.

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Apple의 FoundationModels로 iOS 26 이상 앱에 기기 내 언어 모델을 통합합니다. 글 생성·요약, 데이터 추출, 앱 기능 호출과 생성 중 화면 갱신을 다룹니다.

• 오프라인으로 글을 생성하고 요약합니다
• 입력 문장에서 필요한 항목을 추출합니다
• AI가 앱의 자체 기능을 호출하게 합니다

Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing retention metrics and reasons for churn
• Allocating support investment across customer tiers
• Estimating customer success staffing needs

Analyze SaaS customer data with three Python tools that calculate account health, rank churn risk, and prioritize expansion opportunities. Use repeatable weighted scores to plan customer outreach and prepare quarterly business reviews.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Scoring customer health across an account portfolio
• Prioritizing outreach to accounts at risk of leaving
• Ranking upgrade and additional-seat opportunities

Find the relevant guidance for writing Remotion animations that agents can understand and that remain editable in Studio’s Visual Mode. It points to resources for scene structure, editable styles and animations, and interactive custom components.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find guidance for animations editable in Visual Mode
• Look up scene structure and editable styles
• Find instructions for making custom components interactive

Use LitGPT to explore language model implementations and train models on custom data. Covers pretrained model loading, LoRA fine-tuning that updates a small set of parameters, pretraining, and preparing trained models for deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Studying language model architecture through readable code
• Fine-tuning a model on custom instruction data
• Configuring LoRA training for a single GPU

Use LitGPT to explore language model implementations and train models on custom data. Covers pretrained model loading, LoRA fine-tuning that updates a small set of parameters, pretraining, and preparing trained models for deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Studying language model architecture through readable code
• Fine-tuning a model on custom instruction data
• Configuring LoRA training for a single GPU

Switch the Manager Agent's primary language model with a script that checks connectivity before saving configuration. You can also set how much context the model accepts and whether extended thinking is enabled.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Change the Manager's saved primary model
• Correct a model's context window setting
• Disable extended thinking for a model

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Interpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. Find which predictors, features, or columns matter most; explain why the model made a specific prediction, including diagnosing predictions it got wrong; show how a predictor affects the output; and compare how the model behaves across cohorts or subgroups. Uses model-agnostic techniques and model-native measures, and works on custom models (such as a dlnetwork) through a prediction function handle. Use for model interpretability, explainability, and feature-importance questions on tabular data, not for training, tuning, feature selection, deploying models, or models trained on image, text, signal, or other non-tabular data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a model to predict customer churn
• Training a neural network for image classification
• Preparing missing values and categorical data for training

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Manage the recorded list of vendors that process customer data and check advance notices and audit evidence. The skill also explains temporary staff access to customer accounts, including access limits, revocation, and the actions recorded in customer audit logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a vendor that processes customer data
• Checking notice timing before sending data to a new vendor
• Retrieving notification evidence for an auditor

Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn raw customer material into evidence-backed personas and verbatim voice-of-customer language. Two modes - analyse sources the user already has (call transcripts, support tickets, survey responses, sales notes, reviews), or source new material from public places customers actually talk. Produces quoted pains, objections, and switch triggers that other skills consume, never invented ones. Use when the user says "customer research", "voice of customer", "what do our customers actually say", "we need real quotes", "analyse these call transcripts", "read our support tickets", "build a persona from data", "find what customers complain about", or when a persona is marked provisional. For structuring the finished persona into the knowledge base, see brand-context. For the JTBD persona template itself, see customer-persona.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

대화를 통해 claude-mem의 기록 유형과 태그를 업무나 학습에 맞게 설계합니다. 설정 설치, 활성화, 작동 확인과 선택적인 Telegram 알림을 지원합니다.

• 기억 분류를 업무나 학습에 맞출 때
• 기록할 내용과 생략할 내용을 정할 때
• 특정 기록을 Telegram으로 받을 때

Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing large text collections for language models
• Training a tokenizer on domain-specific text
• Mapping model outputs back to original text