Cloud, CI/CD, monitoring, and deployment (7.3K skills)
Deno runtime guidance — TypeScript-first development, permissions, standard library, and deploying Deno apps.
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Design data loss prevention — data discovery, classification, policy enforcement across endpoint, network, and cloud.
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Deploy and operate endpoint detection and response — policy tuning, behavioral detections, threat hunting, and response actions.
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Scheduled jobs on PaaS and in the cloud — cron syntax, reliability, and observability — vendor-neutral patterns.
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Monitoring scheduled jobs — heartbeat checks, missed runs, and duration anomalies — platform-agnostic patterns.
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Master cloud storage: provider choice, sync vs. backup, sharing, storage tiers, and cost control. Use when organizing cloud files, cutting storage costs, or designing team storage.
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Understanding and optimizing cloud/PaaS pricing — cost models, common traps, and FinOps habits — vendor-neutral.
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Cloudflare guidance — DNS, CDN caching, WAF, Workers, Pages, Zero Trust, and edge performance tuning.
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Design and operate CI/CD pipelines: fast feedback, deployment strategies, environments, and pipeline reliability. Use when building, fixing, or scaling build and deploy pipelines.
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Build on AWS with Amazon Bedrock — managed foundation models, agents, knowledge bases, and guardrails.
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Caddy guidance — automatic HTTPS, Caddyfile patterns, reverse proxying, and simple production deployments.
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Azure guidance — subscriptions and RBAC, networking, AKS/App Service choices, Entra ID, and cost management.
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Master Bitbucket: Pipelines CI/CD, pull request workflow, branch permissions, and Jira integration. Use when building Bitbucket Pipelines or managing Bitbucket repos.
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Capacity planning — forecasting demand, sizing infrastructure, and avoiding both outages and waste — use when scaling systems.
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Use OpenAI models on Azure — enterprise deployment with private networking, compliance, and managed scaling.
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AWS guidance — core services, IAM least privilege, networking, cost control, and production architecture patterns.
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Deploy ML models to production with Baseten — high-performance inference with autoscaling and custom runtimes.
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Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product feature.
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Build AI chatbots end to end — conversation design, persona, retrieval grounding, escalation, and deployment. Use when creating a chatbot for support, sales, or internal assistance.
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Practice AI safety across the lifecycle — risk assessment, alignment concepts, evaluation, deployment safeguards, and governance. Use when building or deploying AI systems responsibly.
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Application performance monitoring — transactions, profiling, and finding slow code — works with any APM platform.
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Designing alert rules that fire when it matters — thresholds, burn rates, and routing — for any monitoring stack.
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Manage fleets of local AI agents: lifecycle, monitoring, task assignment, and resource control. Use when running multiple CLI agents via an orchestrator like AI Maestro.
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Use Xquik in Hermes Agent for public X research, monitoring, thread summaries, creator discovery & approved actions. Not affiliated with X Corp. Use when the user requests X data or a named action. Trigger with "search X", "monitor X", "post tweet", or "X trends".
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