「distribute」の検索結果

Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity needs.

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Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.

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Comprehensive guide for writing Apache Cassandra in-JVM distributed tests (dtests). Use when creating tests that simulate multi-node Cassandra clusters within a single JVM for faster integration testing. Covers cluster creation (single-node, multi-node, multi-datacenter), configuration (all cassandra.yaml parameters, features, network topology), instance lifecycle (startup/shutdown/restart), query execution, message filtering for failure scenarios, running code on instances, ClusterUtils utilities, and debugging classloader-related issues (serialization failures, same-class-different-classloader problems).

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Create, run, and verify TLA+ and PlusCal formal specifications. Use for modeling distributed systems, protocols, concurrent algorithms, state machines. Can compose specs from code, find divergences between spec and implementation, spot concurrency bugs and invariant violations. Use when asked to "write a TLA+ spec", "model check", "verify protocol", "find race conditions", or "formal verification".

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Manually distribute working-tree changes (or a batch of edits already made on the current branch) across the correct branches of a Graphite (gt) stack, when gt absorb's blame-based auto-split doesn't match the logical grouping - e.g. new code with no prior line to blame onto, or changes that conceptually belong with a commit gt's algorithm wouldn't pick. Use when asked to "split these changes into the stack", "absorb this properly", "put this fix on the right branch", or when a change was made on top of a branch but really belongs several branches down.

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Comprehensive Python programming reference covering syntax, concurrency, networking, databases, ML/LLM development, and HPC. Use for: Python questions, Python interview preparation, debugging, performance optimization, async patterns, library examples, code review, best practices, MLOps workflows, distributed computing, security implementations, and any Python development tasks.

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Build and distribute Expo development clients locally or via TestFlight

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比较推理副本、prefill/decode 分离、专家放置与共享 KV 的跨实例方案。用于容量规划、路由、扩缩容和故障恢复。

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Review Apache Pinot concurrency and state safety when locks, shared callbacks, lifecycle, or distributed metadata change.

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How to coordinate with squads on different machines using git as transport

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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

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Build and distribute Expo development clients locally or via TestFlight

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Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

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Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

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Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.

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Golang everyday observability — the always-on signals in production. Covers structured logging with slog, Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling with pprof/Pyroscope, server-side RUM event tracking, alerting, and Grafana dashboards. Apply when instrumenting Go services for production monitoring, setting up metrics or alerting, adding OpenTelemetry tracing, correlating logs with traces, migrating legacy loggers (zap/logrus/zerolog) to slog, adding observability to new features, or implementing GDPR/CCPA-compliant tracking with Customer Data Platforms (CDP). Not for temporary deep-dive performance investigation (→ See `samber/cc-skills-golang@golang-benchmark` and `samber/cc-skills-golang@golang-performance` skills).

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Azure Cosmos DB library for Rust (NoSQL API). Document CRUD, containers, and globally distributed data. Triggers: "cosmos db rust", "CosmosClient rust", "document crud rust", "NoSQL rust", "partition key rust".

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Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.

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Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data. Triggers: "cosmos db", "CosmosClient", "container", "document", "NoSQL", "partition key".

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Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed from Kraken2 classification output.

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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

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Configures AWS Shield Advanced for enhanced Distributed Denial of Service (DDoS) protection: subscribing accounts and adding resource protections, enabling automatic application layer (layer 7) mitigation through AWS WAF, configuring health-based detection with Route 53 health checks, setting up Shield Response Team (SRT) access and proactive engagement, reviewing DDoS events and requesting cost protection credits, and aggregating resources into protection groups. Applicable when the user wants stronger DDoS protection for internet-facing resources (CloudFront, Application or Network Load Balancers, Elastic IP addresses, Global Accelerator, or Route 53 hosted zones), wants expert help during an attack, or wants to recover attack-driven scaling charges. Routes to the right per-task procedure in references. Not applicable for authoring AWS WAF rules (waf skill), creating Route 53 health checks (route53 skill), or org-wide Shield Advanced rollout with Firewall Manager (firewallmanager skill).

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Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.

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Guides building full-stack applications with AWS Blocks — an Infrastructure-from-Code framework. Applies when creating APIs, selecting Building Blocks (KVStore, DistributedTable, Database, AuthBasic, AuthCognito, Realtime, AsyncJob, FileBucket, etc.), running local development, or deploying AWS Blocks applications. Also covers AWS Blocks topics with validated, version-specific patterns that prevent common mistakes. Triggers when user mentions AWS Blocks; project has aws-blocks/ directory; code imports @aws-blocks packages.

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