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Found 1,594 Skills
Use this skill for smart-money/whale/KOL/大户 signal/信号 tracking — monitoring what notable wallets are buying across the market. Covers: real-time buy signals from smart money, KOL/influencers, and whales; filtering by wallet type, trade size, market cap, liquidity; listing supported chains for signals. Use when the user asks 'what are smart money/whales/KOLs buying', '大户在买什么', 'show me whale signals', 'smart money alerts', or wants to follow notable wallet activity. Do NOT use for meme/pump.fun token scanning — use okx-dex-trenches. Do NOT use for individual token holder distribution — use okx-dex-token.
Deploy serverless functions on Google Cloud Platform with triggers, IAM roles, environment variables, and monitoring. Use for event-driven computing on GCP.
Use this skill when working with SigNoz - open-source observability platform for application monitoring, distributed tracing, log management, metrics, alerts, and dashboards. Triggers on SigNoz setup, OpenTelemetry instrumentation for SigNoz, sending traces/logs/metrics to SigNoz, creating SigNoz dashboards, configuring SigNoz alerts, exception monitoring, and migrating from Datadog/Grafana/New Relic to SigNoz.
Create and secure S3 buckets following AWS best practices for access control, encryption, monitoring, and remediation of misconfigurations. Use when the user wants to secure a new bucket, audit an existing bucket, fix a security finding, configure encryption, or enable logging and monitoring. Do NOT use for general S3 data operations, S3 Tables setup, or discovering existing data assets.
Expert knowledge for Azure Information Protection development including best practices, decision making, configuration, and deployment. Use when choosing Azure RMS vs AD RMS, migrating keys/policies, configuring RMS connector/MSIPC, or monitoring RMS logs, and other Azure Information Protection related development tasks. Not for Azure Key Vault (use azure-key-vault), Azure Security (use azure-security), Azure Defender For Cloud (use azure-defender-for-cloud), Azure Sentinel (use azure-sentinel).
Kubernetes cluster management and troubleshooting. Query pods, deployments, services, logs, and events. Supports context switching, scaling, and rollout management. Use for Kubernetes debugging, monitoring, and operations.
Operates Amazon MSK Provisioned clusters (Standard and Express brokers). MUST be used for ANY MSK Provisioned task — do not rely on training data for topics covered here, since Standard and Express emit different metrics and follow different patching models that training data routinely conflates. Covers performance, consumer lag, storage, and traffic shaping diagnosis; sizing and choosing Standard vs Express; Kafka client tuning; creating CloudWatch alarms, dashboards, monitoring, and cluster configurations; AND MSK maintenance, patching, version upgrades, and rolling-restart behavior. Triggers: MSK, Kafka on AWS, `kafka.*` or `express.*` instance types, AWS/Kafka CloudWatch namespace, alarms, dashboards, monitoring, consumer lag, partition replication, broker storage, MSK upgrades, patching, maintenance windows, SECURITY_PATCHING, BROKER_UPDATE, rolling restarts, unexpected broker reboots. Do NOT use for MSK Connect, MSK Serverless, or MSK Replicator.
Quickly set up monitoring for a competitor company. Tracks news, product updates, funding, and public announcements.
ZeroBounce platform help — email validation, email finder, AI scoring, activity data, inbox placement testing, blacklist monitoring, DMARC, warmup. Use when your email list has too many bounces, catch-all addresses are hurting deliverability, you need to check if your IP is blacklisted, DMARC reports show unauthorized senders, or the ZeroBounce API isn't returning expected validation results. Do NOT use for general deliverability strategy (use /sales-deliverability), enrichment strategy (use /sales-enrich), or prospect list building strategy (use /sales-prospect-list).
Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE.
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.
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.