Use when diagnosing issues with Postfix: availability domain, throughput domain, latency domain, or errors domain degradation. Queries Netdata via MCP for postfix master daemon running, smtp listener port accessibility, messages received per minute, messages delivered per minute, queue dwell time (average), applies…
Use when diagnosing issues with PostgreSQL: connection exhaustion, lock contention cascade, autovacuum starvation / bloat spiral, transaction id wraparound emergency, or checkpoint storms. Queries Netdata via MCP for process liveness, recovery state, transaction rate (commits and rollbacks), row operations rate, query…
Use when diagnosing issues with Proxysql: backend connection starvation, multiplexing collapse, monitor false decisions, query rule cpu overload, or configuration drift. Queries Netdata via MCP for backend server health status, monitor health check results, backend connection pool usage, client connection metrics…
Use when diagnosing issues with RabbitMQ: availability, resource alarms, throttling & backpressure, or throughput degradation. Queries Netdata via MCP for node availability status, network partition status, memory alarm status, disk free alarm status, connection flow control state, applies the diagnostic tree from the…
Use when diagnosing issues with Redis: the fork/cow storm, the event loop wedge, the replication backlog overflow, the memory pressure spiral, or the connection exhaustion cascade. Queries Netdata via MCP for redis reachability, uptime and unexpected restarts, memory usage ratio, memory fragmentation ratio, rejected…
Use when diagnosing issues with smartctl (S.M.A.R.T. Disk Health): gradual media degradation, sudden mechanical failure, ssd wear-out cliff, interface/transport failure, or thermal damage. Queries Netdata via MCP for smartctl (S.M.A.R.T. Disk Health) health signals, applies the diagnostic tree from the Netdata…
Use when diagnosing issues with Apache Tomcat: thread pool exhaustion, heap exhaustion / gc death spiral, classloader leak on redeploy, stuck threads, or connection saturation. Queries Netdata via MCP for thread pool utilization, jvm process alive, request throughput, bytes sent/received rate, request processing time…
Use when diagnosing issues with Traefik: Traefik operational issues. Queries Netdata via MCP for Traefik health signals, applies the diagnostic tree from the Netdata operator playbook, and recommends remediation.
Use when diagnosing issues with Uwsgi: worker exhaustion, harakiri storm, memory creep, stuck workers, or reload failure. Queries Netdata via MCP for accepting worker count, worker busy ratio, request throughput (delta requests), average response time (avgrt), worker running time, applies the diagnostic tree from the…
Use when diagnosing issues with Varnish Cache: thread pool exhaustion, cache stampede (thundering herd), ban list explosion, storage exhaustion, or workspace overflow. Queries Netdata via MCP for session and request drop rate, backend health state, cache hit ratio, backend request rate, thread pool saturation, applies…
Use when diagnosing issues with VMware vCenter Server Appliance (vCSA): certificate expiry cascade, disk space exhaustion on a specific partition, database bloat / stats table growth, vpxd memory exhaustion / crash loop, or service dependency deadlock. Queries Netdata via MCP for VMware vCenter Server Appliance (vCSA)…
Use when diagnosing issues with VMware vSphere: cpu starvation, memory pressure cascade, storage latency cliff, numa penalty, or snapshot accumulation. Queries Netdata via MCP for cpu ready time (per vm), cpu co-stop (per vm), memory balloon (per vm and host), host swap activity (vmkernel-level swap), datastore…
Use when diagnosing issues with Zfs: pool state degradation, capacity-fragmentation cliff, txg sync hang, arc memory starvation, or silent data corruption. Queries Netdata via MCP for pool health state, per-vdev state and error counts (data-bearing vdevs), pool i/o operations and bandwidth, pool i/o latency, i/o queue…
Step-by-step workflow for adding, modifying, or debugging a source-format adapter. Use whenever editing an adapter under internal/adapters, internal/canonical, or related fixture files.
Apply ai-viewer coding standards for production-quality Go and TypeScript changes. Use for implementation, refactoring, runtime behavior changes, and any change where clean code, separation of concerns, modularity, or maintainability matters.
Helper-subagent patterns for ai-viewer. Use when bounded read-only investigation, summarization, or parallel context gathering would help. Do not use this skill to delegate implementation by default, and do not use helper subagents as external reviewer gates.
Install, run, and operate ai-viewer on the operator's workstation — the system install (/opt/ai-viewer, runs-as-operator, explicit --source flags, systemd units), the user install alternative, build/run/upgrade/uninstall commands, and the threat-model reasoning behind the run-as-operator decision. Use when installing…
Apply React/TypeScript/Vite patterns used in ai-viewer's frontend. Use when editing frontend/ — pages, components, API client, SSE handling, D3 visualizations.
Apply Go-specific patterns used in ai-viewer for fsnotify watching, SQLite access, SSE streaming, and structured logging. Use when editing internal/ingest, internal/store, internal/presenter, internal/adapters, internal/canonical, or internal/obs.
Catalog of every automated quality gate ai-viewer enforces — commands, thresholds, and what to do when a gate fails. Use before claiming any work done, before any commit, when adding a new gate, or when investigating a CI failure. The runtime companion to .agents/sow/specs/quality-gates.md.
Run ai-viewer's external reviewer gates. Use on meaningful chunks of work, at least per SOW and per substantial SOW milestone: gap analysis, implementation plan, and implementation review. Reviewers are glm, minimax, kimi, mimo, deepseek, and qwen. They are gates, not implementers.
Run, write, and maintain ai-viewer tests across Go backend and React frontend. Use when adding or changing tests, debugging CI failures, managing fixtures, or running benchmarks.