1.4K skills ・ Works with: Kubernetes · sorted by popularity
Prepare web applications for production with Docker configurations, automated testing and deployment workflows, health checks, and rollback plans. Compare release strategies and review application, infrastructure, monitoring, security, and operational readiness before launch.
Create and review Kubernetes configuration files with examples for health checks, access permissions, resource limits, and automatic scaling. Use the included kubectl commands to investigate container crashes, memory shortages, and deployment problems.
Set up reproducible development environments with Flox on macOS and Linux. Define tools, package versions, setup steps, and local services in one configuration, then share it with teammates or add project-specific tools for AI agents.
Helps prepare web applications for production with automated testing and deployment workflows, Docker packaging, health checks, and rollback plans. Provides examples and checklists for choosing release strategies and reviewing readiness before launch.
Check shell commands before they run and warn about destructive actions such as file deletion, database removal, and Git history changes. Users can approve ordinary warnings, while certain catastrophic command patterns are denied.
Review recent Claude Code session logs to find frequently used read-only Bash commands and MCP tools, which connect agents to services. Rank suitable permission rules and merge them into project settings to reduce repeated approval prompts.
Compare job requirements with your current skills and experience to identify learning priorities. Get a report with ranked gaps, study resources found through web searches, estimated learning time, and a recommended study sequence.
Consult bundled technical documentation to understand and integrate Hindsight, a memory system for AI agents. Find guidance on storing and retrieving memories, API calls, configuration, deployment, SDKs, troubleshooting, and performance.
Follow guidance to create and operate Kubernetes clusters on Azure Kubernetes Service. Configure groups of worker machines, automatic scaling, networking, monitoring, identity integration, and upgrades, with command examples and Terraform configuration for repeatable setup.
Set up ArgoCD to keep Kubernetes deployments aligned with configuration stored in Git, an approach called GitOps. Follow guidance for multiple environments, automatic synchronization, deployment ordering, access permissions, and troubleshooting differences.
Review system architecture and major design changes for scalability, resilience, and maintainability. Identify risks, compare design tradeoffs, and document recommendations and next steps across services, APIs, data storage, security, and deployment.
Design production alerts, route notifications by severity, and organize on-call schedules. Use configuration examples to define backup contacts, escalation steps when alerts go unanswered, and runbooks that guide responders through incident investigation.
Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.
Design traffic controls for AI inference services using Istio configuration examples. Covers mutual authentication, model version routing, gradual releases, retry limits, failure containment, and monitoring, with guidance for validating changes on the target cluster.
Define security controls for AI coding agents used by a team. Covers command and file permissions, secret scanning, isolated execution environments, code review gates, and audit trails, with configuration examples to adapt.
Develop incident response procedures for LLM outages, declining answer quality, spending spikes, and unsafe outputs. Use example monitoring alerts, severity levels, recovery playbooks, and postmortem guidance to organize detection, response, and follow-up work.
Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.
Add monitoring to AI agents with response times, token usage, estimated costs, and error metrics. Trace records follow model and tool calls step by step, helping teams investigate failures and understand operating costs.
Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.
Guide reviews of Linux Kernel patches for memory safety, Terraform plans for costly resource changes, and Kubernetes configurations for restricted permissions. A separately installed tool and configured AI model produce advisory findings and candidate manifests for validation.
Prepare Apache Airflow provider releases by classifying pending changes, inspecting pull request diffs for compatibility risks, and applying version bumps and changelog entries. Use existing breeze tooling to regenerate release documentation and consult the release manager on uncertain decisions.
Adapt a resume to one specific job posting by mapping requirements to evidence from real experience. Identify gaps, revise emphasis and wording, check readability for applicant tracking systems, and optionally draft a cover letter.
Investigate Kubernetes permissions and cluster behavior in CTF sandboxes. Trace role-based access control, service accounts, and automated controllers to explain workload changes, secret exposure, and differences between configuration files and live objects.
Analyze running containers in a CTF sandbox by comparing declared configuration with live processes, mounts, and routes. Trace how secrets and helper containers affect behavior, then document the earliest mismatch and a minimal evidence chain.