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Found 388 Skills
Use for Roblox persistent data and cross-server state design: choosing between DataStoreService, OrderedDataStore, MemoryStoreService, and MessagingService; designing save and load flows, schema shape, versioning, metadata, retries, quotas, observability, and concurrency-safe coordination across servers.
Use when assessing or reviewing Kubernetes workloads running on Amazon EKS for best practice compliance, including pod configuration, security posture, observability, networking, storage, image security, and CI/CD practices. Requires kubectl and awscli access to the target cluster. Triggers on "assess my EKS workloads", "check k8s best practices", "assess container workloads", "evaluate pod security", "workload compliance check", "EKS workload assessment", "检查 K8s 工作负载", "评估容器最佳实践", "审计 EKS 应用", "检查 Pod 配置", "容器安全评估", "工作负载合规检查".
Application performance profiling and bottleneck identification — Node.js profiling, Chrome DevTools, flame graphs, memory leak detection, CPU profiling, React rendering performance. Activate on "profiling", "performance bottleneck", "flame graph", "memory leak", "slow app", "CPU profiling", "heap snapshot", "React re-renders", "EXPLAIN ANALYZE", "event loop lag", "clinic.js", "Core Web Vitals". NOT for infrastructure monitoring or observability (use logging-observability), load testing (use a load-testing skill), or database schema optimization.
Audit and build the infrastructure a repo needs so agents can work autonomously — boot scripts, smoke tests, CI/CD gates, dev environment setup, observability, and isolation. Use when a repo can't boot, tests are broken or missing, there's no dev environment, agents can't verify their work, or agents need human help to get anything done. Do not use for reviewing an existing diff or for documentation-only cleanup.
Interact with KWeaver Knowledge Network and Decision Agent — build knowledge networks, query Schema/instances, semantic search, execute Action, Agent CRUD and conversation, Trace data analysis. Interact with Dataflow document processes — list processes, trigger runs, query run history, view step logs. Interact with Skill management module — register Skill, search in market, progressive reading, download and installation. Interact with Toolbox / Tool — create toolbox, upload OpenAPI tools, publish, start and stop. Interact with Vega observability platform — query Catalog/resources/connector types, health inspection. This skill is automatically activated when users mention intents such as "knowledge network", "knowledge graph", "query object type", "execute Action", "what Agents are there", "create Agent", "converse with Agent", "list all Agent templates", "list Agents I created", "list Agents in private space", "dataflow", "data flow", "process orchestration", "process run records", "process logs", "trigger dataflow", "view dataflow run history", "Skill", "skill package", "register Skill", "install Skill", "read SKILL.md", "toolbox", "toolbox", "upload tool", "register tool", "OpenAPI tool", "enable tool", "publish toolbox", "data source", "data view", "atomic view", "Catalog", "Vega", "health check", "inspection", "trace", "evidence chain", "data flow tracking", "data source", "how data is obtained", etc.
Set up, configure, and troubleshoot Grafana Cloud integrations for AWS, Azure, and other cloud providers. Use when the user asks to connect AWS CloudWatch, set up Azure Monitor, configure Confluent Cloud observability, install a Grafana integration, set up hosted exporters, use AWS Firehose for CloudWatch logs, or troubleshoot a cloud integration. Triggers on phrases like "AWS CloudWatch", "Azure Monitor", "Confluent integration", "cloud integration", "hosted exporter", "AWS Firehose", "install integration", "cloud metrics", or "cloud logs".
Use when writing or reviewing TypeScript/full-stack code. Encodes principles for type safety (branded types, discriminated unions, end-to-end types), real tests over mocks, OpenTelemetry observability, and picking the right abstractions instead of premature ones.
Use this skill when the user asks to "check data usage", "list TCO policies", "view quotas", "reduce Coralogix costs", "optimize observability spend", "lower our logging bill", "data budget exceeded", "TCO policy", "retention tier", "archive storage", "ingestion costs", "frequent search vs archive", "why is our bill so high", "spending too much on logs", "data retention settings", "quota rules", "cost analysis", "usage breakdown", "optimize log volume", "control data ingestion", "archive cold data", "billing units", "plan consumption", "daily plan", "overage", "PAYG", "usage anomaly", "usage trend", "cx_data_usage_units", or wants to investigate, analyze, or reduce Coralogix data costs.
This skill provides AWS cost optimization, monitoring, and operational best practices with integrated MCP servers for billing analysis, cost estimation, observability, and security assessment.
Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).
Search and analyze DealerVision production logs via SolarWinds Observability API. Use when investigating errors, debugging issues, checking system health, or when the user mentions logs, SolarWinds, production errors, or system monitoring. Requires the `logs` CLI tool to be installed.
Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".