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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
中文简介正在准备中。当前显示原文说明。
Build batch processing systems for AI workloads. TRIGGERS - Use when user needs help with ai-batch-processing related tasks.
中文简介正在准备中。当前显示原文说明。
OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC / SPIFFE / X.509 / Idira), moderation API, misalignment monitoring, safety alerts / cases, safety best practices, under-18 guidance, ZDR Private Safety Processing (PSP), production best practices, deployment checklist, cost / latency optimization, fast / ultrafast mode, Realtime API costs。 rate limits・error handling・prompt caching・webhooks は openai-api-core、Codex WIF・管理は openai-codex。
中文简介正在准备中。当前显示原文说明。
Claude API (platform.claude.com) の組織管理・Admin API (sk-ant-admin)。 users, invites, workspaces, API keys, service accounts, RBAC (groups / roles), Workload Identity Federation (WIF, federation issuers / rules), Usage & Cost API, rate / spend limits, Analytics API, Compliance API (transcripts, access transparency log), Plugins API, OpenTelemetry telemetry, CMEK, zero data retention (ZDR), data residency, inference hooks。
中文简介正在准备中。当前显示原文说明。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
中文简介正在准备中。当前显示原文说明。
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
中文简介正在准备中。当前显示原文说明。
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
中文简介正在准备中。当前显示原文说明。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
中文简介正在准备中。当前显示原文说明。
Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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Architecture and systems design for agents that retain bounded state across sessions. Covers durable event history, owned state, derived memory, retrieval indexes, framework evaluation, recovery, deletion, and rebuild behavior. Uses constructed implementation examples and workload calibration rather than universal vendor, latency, similarity, retention, or cost claims. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).
中文简介正在准备中。当前显示原文说明。
Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.
中文简介正在准备中。当前显示原文说明。
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
中文简介正在准备中。当前显示原文说明。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
中文简介正在准备中。当前显示原文说明。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
中文简介正在准备中。当前显示原文说明。
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
中文简介正在准备中。当前显示原文说明。
Phase 4 of the Forge pipeline. Summarize the whole workload — what was built, which Beads issues closed, a before/after, and a step-by-step walkthrough the user can run to verify it themselves — then run quality gates, push, and open the PR. Use when starting /forge-ship or the ship step of /forgemaster.
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Expert hybrid cloud architect specializing in complex multi-cloud solutions across AWS/Azure/GCP and private clouds (OpenStack/VMware). Masters hybrid connectivity, workload placement optimization, edge computing, and cross-cloud automation. Handles compliance, cost optimization, disaster recovery, and migration strategies. Use PROACTIVELY for hybrid architecture, multi-cloud strategy, or complex infrastructure integration.
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This skill teaches security teams how to detect and respond to unauthorized cryptocurrency mining operations in cloud environments. It covers identifying cryptomining indicators through compute usage anomalies, network traffic patterns to mining pools, GuardDuty CryptoCurrency findings, and runtime process monitoring on EC2, ECS, EKS, and Azure Automation workloads.
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This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.
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Estimates infrastructure needs based on traffic forecasts, workload analysis, and performance requirements with sizing recommendations and cost trade-offs. Use for "capacity planning", "infrastructure sizing", "resource estimation", or "scalability planning".
中文简介正在准备中。当前显示原文说明。
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
中文简介正在准备中。当前显示原文说明。