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Found 1,242 Skills
Comprehensive logging and observability patterns for production systems including structured logging, distributed tracing, metrics collection, log aggregation, and alerting. Triggers for this skill - log, logging, logs, trace, tracing, traces, metrics, observability, OpenTelemetry, OTEL, Jaeger, Zipkin, structured logging, log level, debug, info, warn, error, fatal, correlation ID, span, spans, ELK, Elasticsearch, Loki, Datadog, Prometheus, Grafana, distributed tracing, log aggregation, alerting, monitoring, JSON logs, telemetry.
Expert guidance for Google Ads Script development including AdsApp API, campaign management, ad groups, keywords, bidding strategies, performance reporting, budget management, automated rules, and optimization patterns. Use when automating Google Ads campaigns, managing keywords and bids, creating performance reports, implementing automated rules, optimizing ad spend, working with campaign budgets, monitoring quality scores, tracking conversions, pausing low-performing keywords, adjusting bids based on ROAS, or building Google Ads automation scripts. Covers campaign operations, keyword targeting, bid optimization, conversion tracking, error handling, and JavaScript-based automation in Google Ads editor.
Senior DevOps Engineer with expertise in CI/CD automation, infrastructure as code, monitoring, and SRE practices. Proficient in cloud platforms, containerization, configuration management, and building scalable DevOps pipelines with focus on automation and operational excellence.
Production incident response procedures for Python/React applications. Use when responding to production outages, investigating error spikes, diagnosing performance degradation, or conducting post-mortems. Covers severity classification (SEV1-SEV4), incident commander role, communication templates, diagnostic commands for FastAPI/ PostgreSQL/Redis, rollback procedures, and blameless post-mortem process. Does NOT cover monitoring setup (use monitoring-setup) or deployment procedures (use deployment-pipeline).
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.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Git-centric implementation workflow. Enforces clean checkout, creates a properly named branch, tracks progress in a WIP markdown file, and commits continuously so git logs serve as the primary monitoring channel. Use when starting instructed, offer for any plan-based implementation task.
Production Python engineering patterns covering architecture, observability, testing, performance/concurrency, and core practices. Use when designing Python systems, implementing async/sync APIs, setting up monitoring, structuring tests, optimizing performance, or following Python best practices.
GitHub API operations - repositories, issues, pull requests, actions, code security, discussions, gists, and more. Use for GitHub-related tasks like managing PRs, issues, searching code, and monitoring workflows.
Use when managing project uncertainty through structured risk tracking, identifying and assessing risks with probability×impact scoring (risk matrix), assigning risk owners and mitigation plans, tracking contingencies and triggers, monitoring risk evolution over project lifecycle, or when user mentions risk register, risk assessment, risk management, risk mitigation, probability-impact matrix, or asks "what could go wrong with this project?".
Production MLOps and ML/LLM/agent security skill for deploying and operating ML systems in production (registry + CI/CD, serving, monitoring/drift, evaluation loops, incident response/runbooks, and governance), including GenAI security (prompt injection, jailbreaks, RAG security, privacy, and supply chain).