Results for “data-automation”

Expert knowledge for Azure Data Share development including troubleshooting, decision making, security, configuration, and deployment. Use when estimating Data Share costs, managing invitations/RBAC, cross-region deployments, dataset mapping, or automation, and other Azure Data Share related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure Import Export (use azure-import-export), Azure Open Datasets (use azure-open-datasets), Azure Data Explorer (use azure-data-explorer).

Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions.

Builds a scheduled, AI-powered data collection agent that scrapes public sources, enriches results with Gemini Flash, and stores them in Notion, Sheets, or Supabase.

Manage businesses, projects, time tracking, and budgets in FreshBooks cloud accounting via automated API calls.

Automates repetitive infrastructure and deployment tasks with Terraform, OpenTofu, Ansible, GitHub Actions, and n8n, enforcing safe practices like plan review and confirmation before applying changes.

Automate lead generation, web scraping, and social media data extraction workflows through PhantomBuster's cloud platform via Composio.

Master AI-powered test automation with modern frameworks, self-healing tests, and comprehensive quality engineering. Build scalable testing strategies with advanced CI/CD integration. Use PROACTIVELY for testing automation or quality assurance.

Identifies operational toil, prioritizes automation by ROI, and provides patterns for self-healing systems and runbook automation.

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

AI agent that consolidates extracted sales data into live reporting dashboards with territory, rep, and pipeline summaries

Expert DevOps engineer for CI/CD, IaC, Kubernetes, and deployment automation. Activate on: CI/CD, GitHub Actions, Terraform, Docker, Kubernetes, Helm, ArgoCD, GitOps, deployment pipeline, infrastructure as code, container orchestration. NOT for: application code (use language skills), database schema (use data-pipeline-engineer), API design (use api-architect).

Provides proven architectural patterns for building n8n workflows, covering webhook processing, HTTP API integration, database operations, AI agent workflows, batch processing, and scheduled tasks.

Expert knowledge for Azure Test Plans development including limits & quotas, security, and integrations & coding patterns. Use when configuring test run custom fields, data retention, permissions, access levels, or tcm.exe automation, and other Azure Test Plans related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure App Testing (use azure-app-testing), Azure Boards (use azure-boards).

Expert knowledge for Azure IoT Central development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing device templates, data export, IoT Edge gateways, REST/CLI automation, or IoT Central–to–IoT Hub migrations, and other Azure IoT Central related development tasks. Not for Azure IoT Hub (use azure-iot-hub), Azure IoT Edge (use azure-iot-edge), Azure IoT Operations (use azure-iot-operations), Azure Digital Twins (use azure-digital-twins).

Design and apply QA methodology for software teams: test strategy, regression testing, CI failure triage, test automation, quality gates and metrics, risk-based testing, exploratory testing, test design techniques, AI code quality gates (independent verification, acceptance-criteria testability review for agentic Spec-Driven Development), mutation-guided test hardening and review evidence (surviving mutants, weak assertions, diff-aware mutation testing), agentic eval design (dataset test design, judge-as-system-under-test, flaky-eval discipline), QA career levels (Senior/Staff/Principal), and SDET engineering (test infrastructure, gTAA, CI/CD integration). Do not use for root-cause debugging of production incidents, security implementation or threat modeling, or evaluation framework governance and statistical analysis — route those to systematic-debugging, secure-software-engineering, and agent-evals-and-observability respectively.