312 件 ・ 関連度順
概要と使いどころ
Assesses whether a medical research topic is worth starting now by separating true novelty from pseudo-novelty, auditing real feasibility under stated resource constraints, and forcing a concrete start / narrow / redesign / stop decision. Always require explicit assumptions and never fabricate references, datasets, resource availability, precedent studies, or publication claims.
日本語の概要は準備中です。原文の説明を表示しています。
Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example datasets as reference candidates only, specify the core analysis modules and method choices, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, cohort availability, assay coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.
日本語の概要は準備中です。原文の説明を表示しています。
Use for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.
日本語の概要は準備中です。原文の説明を表示しています。
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
日本語の概要は準備中です。原文の説明を表示しています。
Expert database administrator specializing in modern cloud databases, automation, and reliability engineering. Masters AWS/Azure/GCP database services, Infrastructure as Code, high availability, disaster recovery, performance optimization, and compliance. Handles multi-cloud strategies, container databases, and cost optimization. Use PROACTIVELY for database architecture, operations, or reliability engineering.
日本語の概要は準備中です。原文の説明を表示しています。
Assemble a submission-ready replication package to the AEA Data and Code Availability Standard (DCAS) / openICPSR / Social Science Reproduction Platform expectations — standard replication README, dataset manifest, computational-requirements capture, a Table/Figure → script:line map, and a confidential-data deposit plan. Use when user says "build the replication package", "prepare the openICPSR deposit", "make the AEA data and code package", "DCAS compliance", "assemble the deposit for the journal", or after a paper is accepted and the journal's data editor needs the package. NOT a numeric verifier — it calls /audit-reproducibility to confirm claims reproduce before packaging.
日本語の概要は準備中です。原文の説明を表示しています。
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".
日本語の概要は準備中です。原文の説明を表示しています。
Assess and improve the reliability posture of PaaS Applications (Azure Functions and Azure App Service). Scans deployed resources for zone redundancy, ZRS storage, health probes, and multi-region failover. Presents a feature-pivoted checklist, then drives staged remediation (CLI or IaC patches) end-to-end with user confirmation. WHEN: "assess reliability", "check reliability", "zone redundant", "multi-region failover", "high availability", "disaster recovery", "single points of failure", "reliability posture", "resiliency".
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
日本語の概要は準備中です。原文の説明を表示しています。
Plan, deploy, operate, and troubleshoot multi-rack (rack scale) deployments of Azure Local — preintegrated racks scaling to hundreds of machines, built on Network Fabric Controller, Cluster Manager, SAN storage, and managed network fabric. Use for the Microsoft.NetworkCloud and Microsoft.ManagedNetworkFabric control plane. NOT for standard 1-16 node Azure Local, and NOT for rack-aware clusters (two racks as availability zones, up to 8 nodes, synchronous replication) — those are standard scale. WHEN: multi-rack, rack scale Azure Local, aggregation rack, compute rack, Network Fabric Controller, NFC, Cluster Manager, network fabric, isolation domain, az networkcloud, az networkfabric, multi-rack logical network, multi-rack Arc VM.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
Tests/benchmarks a third-party LLM endpoint (OpenAI- or Anthropic-compatible): availability, fidelity, speed, concurrency, protocol compliance, quality regression. Use before hand-rolling a curl loop, when onboarding a provider, or debugging "system prompt 不生效" / a tok/s claim — 测评/压测一个模型或渠道. Not for TTS/voice-clone supplier eval (use the audio skill).
日本語の概要は準備中です。原文の説明を表示しています。
Read and manage Calendly: list scheduled events, event types, invitees, and availability schedules; cancel bookings. Trigger phrases: calendly, my meetings, scheduled events, cancel booking.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to check their calendar, find availability, or create/update/delete events in Apple Calendar on macOS. Powered by LMCP's Calendar tools, which are macOS-only.
日本語の概要は準備中です。原文の説明を表示しています。
Reapply and repair Windows Codex Desktop after Store upgrades, including custom provider models hidden by Statsig available_models filtering, the dependent blue-purple Power slider and its Ultra toggle, Fast Mode request/UI gates, locale i18n, plugin UI gates, Chrome/browser_use gates, Goal command gates, Windows Computer Use availability gates and plugin/runtime repair, phone remote-control pairing under third-party/API-key main app usage, Desktop dynamicTools/inputSchema thread-start schema drift, local conversation visibility recovery after model_provider switches, restored-conversation missing-cwd continuation repair, ASAR integrity repair, signing/installing patched MSIX packages, SDK cleanup, Fast Mode wire verification, local plugin marketplace registration, and optional custom model_instructions_file setup (the bundled system prompt, also called 听话水).
日本語の概要は準備中です。原文の説明を表示しています。
Use when running the final pre-submission preflight for American Economic Review: Insights (AER: Insights) via the AEA/ScholarOne submission system — the word-count and exhibit-count gates, abstract limit, fees, author metadata, declarations, and the AEA Data and Code Availability Policy. Final checks; it does not draft content.
日本語の概要は準備中です。原文の説明を表示しています。
Use when running the final pre-submission preflight for Agricultural Systems (AgSy) via the current Elsevier submission path / Editorial Manager — article-type selection, abstract <= 250 words, Highlights, graphical abstract, CRediT and declaration-of-interest statements, funding/AI disclosures, reference style, and data/code/model availability. Final checks; it does not draft content.
日本語の概要は準備中です。原文の説明を表示しています。
Use when running the final pre-submission preflight for the American Economic Journal: Microeconomics (AEJ: Micro) via the AEA submission system — single-blind review, the 100-word abstract, JEL classification, the submission fee, format, and the AEA Data and Code Availability Policy. Final checks; it does not draft content.
日本語の概要は準備中です。原文の説明を表示しています。
Use when assembling the data and code deposit for an American Economic Review: Insights (AER: Insights) short-format manuscript to pass the AEA Data and Code Availability Policy and the AEA Data Editor's pre-publication reproducibility check. Builds the package and README; it does not run the analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Use when assembling the proof appendix and any code/data deposit for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript under the AEA Data and Code Availability Policy. Covers proof appendices for pure theory plus numerical/structural/experimental code; it does not run your estimation.
日本語の概要は準備中です。原文の説明を表示しています。
Use when running the final pre-submission preflight for AEJ: Economic Policy via the AEA online submission system — single-blind review front matter, JEL codes, submission and publication fees, format, the Data and Code Availability Statement, and disclosure. Final checks; it does not draft content.
日本語の概要は準備中です。原文の説明を表示しています。
Use when assembling the data and code deposit for an AEJ: Economic Policy manuscript to satisfy the AEA Data and Code Availability Policy and the AEA Data Editor's pre-publication reproducibility check. Builds the openICPSR deposit, README, and restricted-data access paths; it does not run the analysis or write the paper.
日本語の概要は準備中です。原文の説明を表示しています。
Use when assembling the data, code, and documentation package for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript to pass the AEA Data and Code Availability Policy and the AEA Data Editor's pre-publication reproducibility check. Covers macro specifics (simulation/calibration code, restricted-access data); it does not write the analysis itself.
日本語の概要は準備中です。原文の説明を表示しています。