「vision」の検索結果

Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.

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Azure Resource Manager SDK for Fabric in .NET. Use for MANAGEMENT PLANE operations: provisioning, scaling, suspending/resuming Microsoft Fabric capacities, checking name availability, and listing SKUs via Azure Resource Manager. Triggers: "Fabric capacity", "create capacity", "suspend capacity", "resume capacity", "Fabric SKU", "provision Fabric", "ARM Fabric", "FabricCapacityResource".

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Articulate a long-horizon product vision that aligns teams and anchors strategy. Use when direction is contested or absent. For near-term project scope, use `design-brief`.

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Provisions, connects, migrates, and operates Amazon RDS for Db2. Applies when provisioning with IBM customer and site IDs (License Manager, BYOL, GovCloud), connecting over TLS, fixing SQL30082N after Secrets Manager rotation, migration from Db2 LUW (Linux, AIX, Windows, AS400) or z/OS mainframe (ADB2GEN, Q Replication), choosing code page/collation (EBCDIC, CCSID), S3 backup/restore, Multi-AZ and cross-region standby replicas, RDSADMIN procedures, customer-managed KMS BYOK, self-managed Active Directory Kerberos, Db2 audit to S3, minimum IAM, or colocation.

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Guides migration, provisioning, search, log-analytics, trace-analytics, and Agentic AI Assistant workflows for Amazon OpenSearch Service and Serverless across six capabilities — migration (Solr/ES/self-managed into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, FGAC, monitoring); search (vector / semantic / hybrid / RAG with Bedrock); log-analytics (PPL, OSI, anomaly detection, Dashboards); trace-analytics (OTel spans, service maps, Data Prepper); ai-assistant (natural language data exploration, incident investigation, root cause analysis). Triggers on OpenSearch, AOS, AOSS, Elasticsearch, Solr, vector/k-NN/semantic/hybrid search, RAG, log analytics, PPL, trace analytics, ISM, FAISS, HNSW, Migration Assistant, UltraWarm, OR1, query my data, analyze logs, investigate errors, root cause analysis.

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Authors and deploys MWAA workflow artifacts: Python Airflow DAGs for provisioned environments or YAML workflow files for Serverless. Covers operator selection, timeout design, retry strategy, scheduling, failure notifications, idempotency, and MWAA Serverless schema compliance. Deploys the artifact (S3 DAG upload or Serverless CreateWorkflow/UpdateWorkflow), creates an environment inline when approved, and redeploys fixes, then optionally hands off to testing-mwaa-workflow. Triggers on: create a DAG, write a pipeline, build a workflow, orchestrate tasks, Airflow DAG, data pipeline, schedule a job, deploy a DAG, deploy a workflow, YAML workflow. Not applicable to converting or migrating existing DAGs between provisioned and serverless (conversion is out of scope), running or smoke-testing a deployed workflow (handled by testing-mwaa-workflow) or diagnosing a failed run (handled by debugging-mwaa-workflow).

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Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error, debug my DAG, why did my workflow fail, DAG not showing up, MWAA import error, requirements failing, worker crashed, serverless run failed. Not applicable to authoring workflows (handled by authoring-mwaa-workflow), running or smoke-testing a workflow (handled by testing-mwaa-workflow), or CI-CD deploy failures.

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Implement the Entrepreneurial Operating System (EOS) to align vision and execution across a company. Use when the user mentions "EOS", "Entrepreneurial Operating System", "V/TO", "quarterly rocks", "Level 10 meetings", "accountability chart", "IDS process", "my company feels chaotic", "we keep having the same problems", or "get the whole team aligned". Also trigger when a growing company needs meeting structure, goal-setting frameworks, or a systematic way to solve recurring organizational issues. Covers the six EOS components: Vision, People, Data, Issues, Process, Traction. For team motivation design, see drive-motivation. For lean experimentation, see lean-startup.

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Build empowered product teams using discovery and delivery dual-track. Use when the user mentions "product discovery", "empowered teams", "feature factory", "opportunity assessment", "product vision", "product strategy", "what should we build", or "our roadmap is just a feature list". Also trigger when restructuring teams away from output-driven models, or deciding what to build next based on outcomes. Covers discovery techniques, team structure, opportunity assessment, vision/strategy, and continuous delivery. For customer interviews, see mom-test. For ongoing discovery systems, see continuous-discovery.

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Analyze images using GPT-4 Vision for detailed description, OCR text extraction, object recognition, and visual Q&A. Use when the user needs to understand image content, extract text from screenshots, identify objects in photos, or ask questions about images via OpenAI GPT-4 Vision API.

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Analyze images and multi-frame sequences using OpenAI GPT vision models

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Generate a structured response-to-referees document from a referee report and the revised manuscript. Maps each referee comment to the specific revision, classifies coverage (addressed / partially / deferred / disagreement), and drafts polite but firm responses. Use during the R&R (revise-and-resubmit) stage of paper revision.

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Screenplay format, page craft, the writing process and adaptation (剧本格式/写作流程/改编) — merged from Wendy Henson (spec format rules), Richard Walter (page craft, revision), Neill Hicks (selling vs shooting script, draft chain), Syd Field (master shot, card method, adaptation as original work), Robert McKee (step outline → treatment → screenplay), Julian Hoxter (format elements, adaptation four questions), Diamond & Weissman (one-page nine-step) and Eric Bork — plus an exact typographic grid, element conventions (V.O./O.S., MORE/CONT'D, dual dialogue), a machine-writable output contract (Fountain, with forced markers for Chinese), and Asian industry formats (Chinese 场号制 影视剧本格式, Japanese 柱・ト書き・セリフ / 決定稿). Use when formatting a spec script, choosing the output file format, deciding what goes in action lines, planning drafts and outlines, cutting pages, adapting a novel, play or true story, writing a Chinese TV/film or Japanese-style script page, or preparing a submission-ready draft.

Half-hour television comedy craft (半小时情景喜剧/笑点力学/困境机器/多机位单机位格式) — merged from Evan S. Smith's Writing Television Sitcoms (premise-driven comedy in three levels, nine predicaments, six character mixes, setup/punchline mechanics, toppers, running gags, ten comedic nuances, format rules), Neil Landau's sitcom seven-step formula and comedy-room models, Richard Blum's act-length and set-count parameters, Phoebe Waller-Bridge's Fleabag scripts (direct address as a season-long device, six-episode skeleton), Pamela Douglas on dramedy, Kam Miller on complementary comic traits, and Jesse Armstrong on jokes carrying tragedy. Use when writing or fixing a sitcom episode or spec, raising joke density per page, designing a comic predicament that generates stories, choosing between multi-camera and single-camera format and page counts, structuring cold open/acts/tag, writing a half-hour pilot, running a comedy punch-up, or making a drama funny without shifting genre.

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Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS or ingress failure, connectivity timeout, network policy, SNAT exhaustion, node-pool scaling blocked by QuotaExceeded or InsufficientVCPUQuota, upgrade stuck, spot or zone disruption, a bare VMExtensionProvisioningError or AllocationFailed wrapper, an uncataloged capacity symptom, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use azure-kubernetes); cost (use cost-analysis from the optional azure-cost plugin); pod rightsizing (use azure-kubernetes); a fully qualified documented AKS signature with every required nested qualifier (use aks-known-issues); standalone failures on non-AKS Azure resources (use azure-diagnostics). Unqualified errors and open-ended incidents stay here only for AKS.

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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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Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS, optimize AKS, AKS spot nodes, AKS cluster-autoscaler, rightsize AKS pod, pod rightsizing, over-provisioned AKS pod, pod resource requests and limits, Vertical Pod Autoscaler, VPA recommendations.

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Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.

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Diagnoses and designs safe Terraform releases, provisioners (remote-exec/local-exec/file, cloud-init, Compose, Caddy), multi-environment isolation, and fresh-host bootstrap. Use when writing or reviewing plan/apply wrappers or provisioners; when staging/production config may differ; when a rollout can mutate a shared gateway; or debugging drift, TLS, container restarts, DNS duplication, or snapshot contamination.

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Backend infrastructure for a project via Cohesivity (cohesivity.ai). Provisions Postgres, Redis, object storage, vector database, hosting, auth, realtime, email inbox, and AI model APIs through one HTTP API. No account or API keys required to start. Trigger phrases: cohesivity, provision backend, need a database, need hosting, need storage, deploy backend, on-the-fly infrastructure.

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Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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Use when responding to Advanced Materials reviewer reports and an editor's decision — major or minor revision, or a rejection with a resubmission or transfer path. Structures the response and the manuscript changes; does not perform the underlying new experiments.

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Use when an American Economic Journal: Macroeconomics (AEJ: Macro) decision letter has arrived (R&R or reject-with-comments) and you need a response-letter strategy and revision plan. Plans the rebuttal and revision; it does not run new identification, model, or robustness work itself (route to those skills).

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Use when drafting the revision and response letter after an Academy of Management Journal (AMJ) revise-and-resubmit — structuring point-by-point responses to the action editor and reviewers across a multi-round developmental process. Drafts the response after revisions; it does not interpret the decision letter (amj-review-process).

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