2,314 件 ・ 関連度順
概要と使いどころ
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
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 a dense, data-heavy screen - an analytics console or a trading terminal - with real charts drawn from tokens: candlestick, volume, depth, stacked area, waterfall, scatter, correlation matrix, donut, gauge, sparklines, heatmaps and order books. Use when the request is a dashboard, terminal, monitoring view, data console, or any screen whose job is to show many numbers at once. Covers the layout sequence, the SVG geometry, the accessibility contract per chart type, and the contrast traps that only appear in dark mode.
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。
Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.
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。
Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from generic statements of importance.
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。
Finds translational opportunities that connect basic-research discoveries to clinically meaningful use cases such as diagnosis, stratification, prognosis, treatment response prediction, monitoring, or therapeutic development. Use this skill when a user wants to turn a mechanism finding, pathway signal, cellular phenotype, experimental observation, or omics discovery into a stronger translational research direction. Always separate mechanistic relevance from translational usability, and never present a basic finding as clinically actionable unless the evidence supports that level.
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 provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.
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。
Migrate Hibernate 5 to Hibernate 6 with Spring Boot 3. Use when fixing HQL/JPQL query parsing issues, removing deprecated Criteria API, updating ID generation strategies, or diagnosing N+1 query behavior changes. Covers breaking changes, type mappings, and performance monitoring.
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。
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.
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 erlang's concurrency model including lightweight processes, message passing, process links and monitors, error handling patterns, selective receive, and building massively concurrent systems on the BEAM 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。
Context window monitoring and budget management. Keeps orchestrator at 15-30% context usage while subagents get full 200k tokens. Provides warnings at thresholds, context-aware summarization triggers, and wave-level budget planning.
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。
State capture and restore across context window compactions. Monitors usage thresholds and serializes quality, task, and spec state for seamless continuation.
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。
Track beads as git-backed issues with persistent attribution, supporting Gas Town's bead lifecycle and convoy progress monitoring.
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 asks to run, submit, monitor, or cancel a job on a remote machine over SSH — their own GPU/CPU server, a workstation, or a Slurm cluster ("the cluster", a login node, "my 3090 box", "the compute server"). Picks a saved machine, runs the work directly over SSH (or via Slurm when present), tracks it, and fetches results back into the workspace.
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。
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
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。
Monitor portfolio risk, R-multiples, and position limits. Creates hedging strategies, calculates expectancy, and implements stop-losses. Use PROACTIVELY for risk assessment, trade tracking, or portfolio protection.
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。
Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.
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。
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
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。
Design ML pipelines covering data preparation, model training, deployment, monitoring, and continuous improvement. Use when coordinating an end-to-end MLOps workflow.
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 production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
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 comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
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。
Implement error tracking, structured logging, grouping, and alert routing. Use when improving production error detection, triage, or integration with monitoring services.
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。
You are an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging, and ensure teams can quickly identify and resolve production issues.
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。
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
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 optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and cloud database optimization. Handles complex query analysis, migration strategies, and performance monitoring. Use PROACTIVELY for database optimization, performance issues, or scalability challenges.
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。