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Found 776 Skills
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.
Telegram bot management and monitoring. TRIGGERS - telegram bot, claude-orchestrator, bot status, bot restart.
Scheduler and background jobs syntax for Frappe/ERPNext v14/v15/v16. Use for scheduler_events in hooks.py, frappe.enqueue() for async jobs, queue configuration, job deduplication, error handling, and monitoring. Triggers on questions about scheduled tasks, background processing, cron jobs, RQ workers, job queues, async tasks.
Gathers and filters information systematically. Applies scanning, focusing, filtering, triangulating, monitoring, and synthesizing modes to build accurate situational awareness. Use when researching, verifying claims, monitoring signals, or combining multiple sources. Triggers on "what's happening", "verify this", "monitor for", "gather information", "is this true".
Guides development with supastarter for Next.js only (not Vue/Nuxt): tech stack, setup, configuration, database (Prisma), API (Hono/oRPC), auth (Better Auth), organizations, payments (Stripe), AI, customization, storage, mailing, i18n, SEO, deployment, background tasks, analytics, monitoring, E2E. Use when building or modifying supastarter Next.js apps, adding features, or when the user mentions supastarter Next.js, Prisma, oRPC, Better Auth, or related Next.js stack topics.
This skill should be used when users need to interact with Kubernetes clusters via kubectl CLI. It covers pod management, deployment operations, log viewing, debugging, resource monitoring, scaling, ConfigMaps, Secrets, Services, and all standard kubectl operations. Supports multiple clusters (production, staging, local k3s) with predefined aliases. Triggers on requests mentioning Kubernetes, k8s, pods, deployments, containers, or cluster operations.
Comprehensive plugin for SAP Datasphere development with 3 specialized agents, 5 slash commands, and validation hooks. Use when building data warehouses on SAP BTP, creating analytic models, configuring data flows and replication flows, setting up connections to SAP and third-party systems, managing spaces and users, implementing data access controls, using the datasphere CLI, creating data products for the marketplace, or monitoring data integration tasks. Covers Data Builder (graphical/SQL views, local/remote tables, transformation flows), Business Builder (business entities, consumption models), analytic models (dimensions, measures, hierarchies), 40+ connection types (SAP S/4HANA, BW/4HANA, HANA Cloud, AWS, Azure, GCP, Kafka, Generic HTTP), real-time replication, task chains, content transport, CLI automation, catalog governance, and data marketplace. Includes 2025 features: Generic HTTP connections, REST API tasks in task chains, SAP Business Data Cloud integration. Keywords: sap datasphere, data warehouse cloud, dwc, data builder, business builder, analytic model, graphical view, sql view, transformation flow, replication flow, data flow, task chain, remote table, local table, sap btp data warehouse, datasphere connection, datasphere space, data access control, elastic compute node, sap analytics cloud integration, datasphere cli, data products, data marketplace, catalog, governance
Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.
Track Clawdbot AI model usage and estimate costs. Use when reporting daily/weekly costs, analyzing token usage across sessions, or monitoring AI spending. Supports Claude (opus/sonnet), GPT, and Codex models.
Health monitoring knowledge and procedures for infrastructure platforms. Use when assessing system health, running health audits, or setting up monitoring.
Track finance investment signal evolution and update logic based on new finance market information. Use when monitoring finance signals and determining if they are strengthened, weakened, or falsified.
Use when "deploying ML models", "MLOps", "model serving", "feature stores", "model monitoring", or asking about "PyTorch deployment", "TensorFlow production", "RAG systems", "LLM integration", "ML infrastructure"