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Found 1,615 Skills
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.
Use before launching sequences to validate content, data, compliance, and monitoring.
Elasticsearch and Elastic APM integration with Serilog structured logging for .NET applications. Use when: (1) Implementing or configuring Serilog with Elasticsearch sink, (2) Setting up Elastic APM with data streams and authentication, (3) Creating logging extension methods in Infrastructure layer, (4) Enriching logs with app-name and app-type properties, (5) Configuring log levels and environment-specific logging, (6) Questions about logging security (PII, credentials), or (7) Troubleshooting observability and monitoring setup.
Use this skill to work with Microsoft Foundry (Azure AI Foundry): deploy AI models from catalog, build RAG applications with knowledge indexes, create and evaluate AI agents. USE FOR: Microsoft Foundry, AI Foundry, deploy model, model catalog, RAG, knowledge index, create agent, evaluate agent, agent monitoring. DO NOT USE FOR: Azure Functions (use azure-functions), App Service (use azure-create-app).
Set up comprehensive observability for Mistral AI integrations with metrics, traces, and alerts. Use when implementing monitoring for Mistral AI operations, setting up dashboards, or configuring alerting for Mistral AI integration health. Trigger with phrases like "mistral monitoring", "mistral metrics", "mistral observability", "monitor mistral", "mistral alerts", "mistral tracing".
Application security covering threat modeling (STRIDE), OWASP Top 10 (2025), OWASP API Security Top 10 (2023), secure coding review, authentication/authorization patterns, input validation, encryption, security headers, supply chain security, compliance (GDPR/HIPAA/SOC2/PCI-DSS), and security monitoring. Use when reviewing code for vulnerabilities, implementing auth patterns, securing APIs, configuring security headers, hardening supply chain, preventing injection attacks, or preparing for compliance audits.
Browser automation via Puppeteer CLI scripts (JSON output). Capabilities: screenshots, PDF generation, web scraping, form automation, network monitoring, performance profiling, JavaScript debugging, headless browsing. Actions: screenshot, scrape, automate, test, profile, monitor, debug browser. Keywords: Puppeteer, headless Chrome, screenshot, PDF, web scraping, form fill, click, navigate, network traffic, performance audit, Lighthouse, console logs, DOM manipulation, element selector, wait, scroll, automation script. Use when: taking screenshots, generating PDFs from web, scraping websites, automating form submissions, monitoring network requests, profiling page performance, debugging JavaScript, testing web UIs.
Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.
Apply when making VTEX IO services easier to observe, troubleshoot, and operate in production. Covers metrics, structured logging, failure visibility, rate-limit awareness, and production readiness checks for backend apps. Use for integration monitoring, error diagnosis, or improving the operational quality of VTEX IO services before or after release.
Exports Amazon RDS or Aurora database snapshots to Amazon S3 in Apache Parquet format for analytics, backup, or data migration. Handles snapshot selection or creation, IAM role setup, KMS encryption, S3 bucket preparation, export task execution, progress monitoring, and data verification. Use when exporting RDS/Aurora data to S3 for Athena, Glue, or Redshift Spectrum consumption.
Logging, testing, cost hygiene, incident triage, and usage metrics for PubNub apps. Covers the correlation fields every send/receive must log, the test pyramid for real-time apps, payload + fan-out cost hygiene, the incident triage runbook, and PubNub usage metrics for billing reconciliation. Use during code reviews, when planning monitoring, when triaging incidents, or when investigating PubNub cost overruns.