Cloud, CI/CD, monitoring, and deployment (6.8K skills)
Analyze and tune PostgreSQL databases using query execution plans, which show how queries run. This skill guides index design, JSONB searches, replication setup, maintenance, and monitoring, with checks to compare behavior before and after changes.
Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.
Investigate slow PostgreSQL and MySQL queries using execution plans, which show how the database runs a query. Develop index, SQL, and configuration changes, then compare measurements before and after each change to validate the results.
Analyze failures in CI, the automated checks run during development. Identify the failing job and step, classify the likely cause, and suggest a small fix, continued monitoring, or human review.
Set up Loop Engineering through a single command-line tool, choosing a workflow pattern and agent tool. Start in report-only mode, check setup health and readiness, and provide next actions and first-run instructions.
Classify failures in CI, automated checks run during development, to distinguish code regressions, temporary infrastructure problems, security test failures, and inconsistent results. Record each failure's category, suggested next action, and attempt count in a structured report.
Configure MCP servers, which expose tools to AI agents, in a holaOS workspace. The skill updates workspace.yaml for remote or local servers, environment variable references, and tool permissions, then checks that the configuration follows the workspace format.
Build and maintain machine learning apps on Hugging Face Spaces. Choose a suitable app framework and computing hardware, load models, deploy files, and check logs and outputs to diagnose build or runtime problems.
Prepare SageMaker deployments and training by finding existing execution roles, checking trust settings, and reviewing required permissions. Guides role creation only when necessary and identifies permission issues that require an AWS administrator.
Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.
Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.
Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.
Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.
Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.
Build and run Omnigent’s server and Postgres together with Docker Compose on a laptop or existing Docker host. Use the supplied configuration and troubleshooting guidance to rebuild images, investigate startup problems, or adapt deployment to another platform.
Prepare a deployment checklist covering tests, approvals, database changes, release steps, and monitoring. Tailor it to the release and document rollback triggers, the conditions for reverting a change, before production deployment begins.
Manage service incidents from initial assessment through recovery review. The skill organizes severity, impact, roles, status updates, and timelines, then drafts a blameless postmortem with root cause analysis and action items assigned to owners.
Prepare and coordinate Octop Python package releases, including version updates, a Chinese changelog, and a pull request. After merging, GitHub Actions handles release tags, package and container publishing, and branch synchronization.
Diagnose slow systems, crashes, and resource problems on servers, PCs, virtual machines, and containers. Quick checks identify affected components, while deeper analysis investigates causes and provides optimization advice or repair plans.
Query, create, update, and delete Tencent Cloud resources through tccli, its command-line tool. The skill checks API documentation before calls, supports browser-based OAuth authorization, and helps interpret results and troubleshoot errors during cloud automation.
Inspect and manage Tencent Cloud resources with tccli, its command line tool. Covers servers, networking, DNS, certificates, permissions, monitoring, and application deployment, with supporting scripts for login and identifying the current cloud instance.
Investigate failed GitHub Actions jobs using logs and recent runs. Receive a report explaining the actual failure trigger, recurrence, any verified breaking commit, and existing pull requests that may fix the issue.
Add Sentry error reporting and performance monitoring to services, scheduled jobs, and database operations. Follow examples for capturing exceptions with useful context and measuring execution time through spans, which record individual operations for investigation.
Check a project's Codex infrastructure, fix missing dependencies, script permissions, and configuration errors, then repeat the check until failures are resolved. Receive a summary of repairs and any remaining optional-feature warnings.