Skills

Diagnoses failing Kubernetes workloads from symptoms, events, logs, and manifests. Works through a systematic checklist (pod status, events, resource limits, probes, scheduling, networking) to identify the root cause of issues like CrashLoopBackOff, ImagePullBackOff, OOMKilled, and pending pods, then recommends concrete fixes and verification steps.

Analyzes Terraform plan output and state to find configuration drift between declared infrastructure and what actually exists. Explains each drifted resource in plain language, flags risky or destructive changes, and recommends whether to reconcile in code, import, or apply, so teams can keep state and reality aligned safely.

Writes clear, calm customer-facing incident communications during an active outage: status-page updates, in-app banners, and stakeholder emails. Adapts the message to the incident phase (investigating, identified, monitoring, resolved), states impact and scope honestly without leaking sensitive detail, and keeps a consistent tone across updates.

Optimizes prompts and agent workflows for finite context windows by prioritizing evidence, compressing history, managing retrieval budgets, and measuring token-cost trade-offs.

Guides containment, evidence collection, root-cause analysis, customer communication, and remediation when an AI agent takes an unsafe, incorrect, or unauthorized action.

Reviews cloud and AI infrastructure spend to identify measurable savings while preserving reliability, performance, security, ownership, and product requirements.

Produces blameless, actionable incident postmortems with a precise timeline, contributing factors, impact, decisions, corrective actions, owners, and measurable follow-through.

Designs traces, metrics, structured events, privacy controls, dashboards, and alerts for LLM applications so teams can diagnose quality, cost, latency, and tool-use behavior.

Writes idempotent Ansible playbooks and roles from a described target state. Structures tasks with proper handlers, variables, and templates; favors modules over shell commands; applies role-based layout and inventory grouping; and adds check-mode safety and tags. Produces playbooks that are re-runnable without unintended side effects.

Builds disaster recovery and business continuity plans for systems and data. Defines RTO and RPO targets, maps critical dependencies, chooses backup and replication strategies, documents failover and restore procedures, and designs a testing cadence with game days. Produces a plan that balances resilience against cost and operational complexity.

Produces production-ready nginx configuration for common scenarios: reverse proxy, load balancing, TLS termination, static file serving, HTTP/2, gzip/brotli, caching, rate limiting, and security headers. Explains each directive, warns about risky defaults, and includes a validation step so the config can be tested before reload.

Produces operational runbooks for services and common incidents. Documents prerequisites, step-by-step diagnosis and remediation, exact commands, verification checks, rollback steps, and escalation paths. Structures each runbook so an on-call engineer can follow it under pressure, and keeps destructive steps clearly flagged with safeguards.

Designs chaos engineering experiments to validate system resilience. Defines steady-state hypotheses, blast-radius limits, fault injections (latency, errors, instance/zone loss, resource exhaustion), abort conditions, and observability checks. Produces a safe, incremental experiment plan and success criteria for game days and automated chaos.

Turns reliability goals into concrete SLIs, SLOs, and error budgets. Helps choose the right service level indicators, set realistic targets from historical data, compute burn rates, and design multi-window multi-burn-rate alerting so teams get paged before the budget is exhausted.

Analyzes cloud spend and produces a FinOps optimization plan. Covers rightsizing, autoscaling, spot/reserved/savings plans, storage tiering, idle resource cleanup, data transfer reduction, and tagging for cost allocation. Prioritizes actions by savings and risk, and defines guardrails and budgets to prevent regressions.

Adds production-grade observability to a codebase by instrumenting it with structured logs, metrics, and distributed traces. Recommends span boundaries, cardinality-safe labels, and OpenTelemetry conventions, then generates the wiring code and dashboards needed to make a service debuggable in production.

Writes reusable, well-structured Terraform modules with clean input/output interfaces, sensible defaults, validation rules, and examples. Covers module composition, variable typing and validation, remote state and backends, provider version pinning, and testing with terraform validate/plan and tools like Terratest. Emphasizes least-privilege IAM and safe defaults.

Generates and refactors Helm charts to package Kubernetes applications. Produces templated manifests, values.yaml with sensible defaults, helpers, chart dependencies, and hooks, and parameterizes images, resources, probes, and ingress so a workload can be deployed consistently across environments.

Reviews and rewrites Dockerfiles for smaller images, faster builds, and better security. Applies multi-stage builds, optimal layer ordering and caching, minimal base images, non-root users, and .dockerignore tuning, and flags vulnerabilities and bloat while keeping the build reproducible.

Turns incident timelines, alerts, and chat logs into a clear, blameless postmortem. Produces an executive summary, impact assessment, detailed timeline, root-cause analysis using techniques like the five whys, and a prioritized list of follow-up action items with owners, in a format ready to share with stakeholders.

Designs feature-flagging and progressive-delivery strategies and generates the integration code. Covers flag naming and lifecycle, targeting and segmentation rules, percentage rollouts, kill switches, and cleanup of stale flags across providers like LaunchDarkly, Unleash, Flagsmith, or a homegrown config service.