Cloud, CI/CD, monitoring, and deployment (7.3K skills)
CI/CD pipeline patterns for GitHub Actions, PR automation, and deployment workflows. Use when setting up CI, fixing broken pipelines, automating PR checks, or configuring deployment.
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Create a structured on-call log by pulling alerts from monitoring and writing a summary to your team's wiki. Requires a monitoring MCP (recommended: Datadog) and a wiki MCP (Confluence or Notion).
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Aurora Smart Home orchestrator — routing layer for all smart home skills. Use this skill when the user asks ANY smart home question and you need to decide which skill to invoke, or when a task spans multiple skills (e.g., "build a sensor that shows on a dashboard and triggers automations"). Invoke aurora FIRST before reaching for a specific skill — it will route to the right specialist(s) and recommend the correct Claude model to keep token usage efficient. Trigger on: smart home, Home Assistant, ESPHome, automation, IoT, dashboard, ESP32, Node-RED, or any request about controlling or monitoring devices at home.
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Home Assistant YAML configuration and automation. The most common skill for general HA questions. Covers automations, blueprints, scripts, scenes, template sensors, Lovelace dashboards, Mushroom cards, packages, helpers, presence detection, voice Assist, calendar automation, Jinja2 templates, notification patterns, energy monitoring, Matter devices, automation debugging and traces, dashboard sections view, and trigger-based template entities.
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Use when creating new skills, editing existing skills, or verifying skills work before deployment
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Scrape web pages using Scrapling with anti-bot bypass (like Cloudflare Turnstile), stealth headless browsing, spiders framework, adaptive scraping, and JavaScript rendering. This skill should be used when asked to scrape, crawl, or extract data from websites; web_fetch fails; the site has anti-bot protections; write Python code to scrape/crawl; write spiders.
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Prove a deploy landed — the served revision is the commit you meant, its env is present, and the product does its job — instead of trusting the deploy command's exit code. Also catches server drift before a pull or deploy. Use after any deploy, rollout, restart or server-side fix ("задеплой", "выкати", "проверь на проде", "почему не обновилось"). Loaded by devops, l3-support, infra-provisioner.
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Standardized cost-estimation framework for great_cto plans. Forces explicit LLM cost, infra cost, human-supervision time, and the (defensible) human-equivalent comparison. Output format is parsable by the board's /api/cost path — must follow exactly.
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Scaffold-time observability so a shipped product is not blind in prod from day one — error capture (Sentry), request-id structured logging, and /healthz + /readyz endpoints. stack-baseline pins Sentry but nothing wires it; this is the wiring. Loaded by app-scaffolder (bake into the scaffold), infra-provisioner (prod env + probes), and consumed by l3-support (traces) and devops (deploy gate).
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Complete launch readiness validation covering every department: code, content, store, marketing, community, infrastructure, legal, and go/no-go sign-offs.
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Use when configuring the Cognee connection, checking whether Cognee is ready or connected, or diagnosing a local or cloud Cognee instance from Cursor.
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Diagnose HitKeep traffic drops, spikes, source shifts, and suspicious changes. Use when traffic, sessions, visitors, pageviews, events, or conversions changed; when a deploy may have affected measurement or behavior; or when search, referral, direct, campaign, and bot explanations need to be separated.
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Explain what the Cognee plugin captures, recalls, stores and sends, where its data and logs live, and how to inspect or turn off each behavior; check its local or cloud connection and settings.
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Capture or review how this project connects to external systems (GitHub, AWS, npm, SSH, etc.) so future sessions know the auth recipe. Auto-detection hook logs candidate captures to an inbox; this skill promotes them to permanent recipes in `.planning/AUTH-RECIPES.md` and optionally to user-global memory at `~/.claude/auth-recipes/`.
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[BETA] Offload plan phase to Claude Code's ultraplan cloud — drafts remotely while terminal stays free, review in browser with inline comments, import back via /gsd:import. Claude Code only.
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Simulates a real user searching on Amazon's storefront to get real-time keyword ranking and search results page data. Use when the user mentions Amazon product search, search result scraping, keyword ranking on search pages, ASIN ranking position check, competitor discovery, search page price comparison, sponsored product analysis, new product monitoring, or storefront search simulation. Even if the user does not explicitly mention "search simulation", trigger this skill whenever their need involves real-time Amazon search results, product ranking data, or storefront SERP analysis.
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Chooses PaaS hosting for apps, agents, APIs, and workers. Use when picking Vercel, Fly.io, Railway, Render, Cloudflare, Netlify, Heroku, Cloud Run, or container PaaS.
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Design local AI engines for iOS. Use when wiring Apple Foundation Models, local classifiers, extraction, summarization, grounded answers, and cloud fallbacks.
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Guides Cloudflare Wrangler CLI usage for Workers, bindings, deploys, local dev, and config. Use when running or reviewing wrangler commands.
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Designs DevOps and platform engineering systems. Use when planning Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, or cloud-native operations.
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Builds production voice bots and IVR with Python STT/TTS pipelines. Use when designing telephony, streaming audio, latency budgets, or voice quality monitoring.
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Runs production ML and LLM systems. Use when deploying, canarying, rolling back, monitoring drift or feature skew, promoting champions, migrating providers, or handling incidents.
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Sizes models and token budgets using Kaplan/Chinchilla scaling laws. Use when reasoning about compute-optimal N and D, tokens-per-parameter ratios, or over-training tradeoffs.
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Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, tool grading, regression, or cost. Not for general LLM evals (ai-evals).
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