python .claude/skills/metrics-analysis/scripts/query_prometheus.py --query PROMQL [--time-range MINUTES] [--step STEP]

# Examples:
python .claude/skills/metrics-analysis/scripts/query_prometheus.py --query "up"
python .claude/skills/metrics-analysis/scripts/query_prometheus.py --query "rate(http_requests_total[5m])" --time-range 60
python .claude/skills/metrics-analysis/scripts/query_prometheus.py --query "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))"

python .claude/skills/metrics-analysis/scripts/list_dashboards.py [--query SEARCH_TERM]

# Examples:
python .claude/skills/metrics-analysis/scripts/list_dashboards.py
python .claude/skills/metrics-analysis/scripts/list_dashboards.py --query "api"

python .claude/skills/metrics-analysis/scripts/get_alerts.py [--state STATE]

# Examples:
python .claude/skills/metrics-analysis/scripts/get_alerts.py
python .claude/skills/metrics-analysis/scripts/get_alerts.py --state alerting

# Instant vector - current value
http_requests_total{service="api"}

# Range vector - values over time (for rate calculations)
http_requests_total{service="api"}[5m]

# Rate of increase per second
rate(http_requests_total{service="api"}[5m])

# Rate (counter → gauge, per second)
rate(http_requests_total[5m])

# Increase (total increase over time range)
increase(http_requests_total[1h])

# Average over time
avg_over_time(cpu_usage[5m])

# Histogram quantile (p95, p99)
histogram_quantile(0.95, rate(http_request_duration_bucket[5m]))

# Sum across all instances
sum(rate(http_requests_total[5m]))

# Group by label
sum by (service) (rate(http_requests_total[5m]))

# Average by label
avg by (instance) (cpu_usage)

# Top 5 by value
topk(5, sum by (service) (rate(http_requests_total[5m])))

# Exact match
http_requests_total{status="500"}

# Regex match
http_requests_total{status=~"5.."}

# Not equal
http_requests_total{status!="200"}

# Multiple labels
http_requests_total{service="api", status=~"5.."}

# Step 1: Check overall latency trend
python query_prometheus.py --query 'histogram_quantile(0.95, rate(http_request_duration_seconds_bucket{service="api"}[5m]))' --time-range 60

# Step 2: Compare p50 vs p99
python query_prometheus.py --query 'histogram_quantile(0.50, rate(http_request_duration_seconds_bucket{service="api"}[5m]))'

# Step 3: Break down by endpoint
python query_prometheus.py --query 'histogram_quantile(0.95, sum by (endpoint) (rate(http_request_duration_seconds_bucket{service="api"}[5m])))'

# Step 1: Overall error rate
python query_prometheus.py --query 'sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))'

# Step 2: Errors by status code
python query_prometheus.py --query 'sum by (status) (rate(http_requests_total{status=~"[45].."}[5m]))'

# Step 3: Errors by service
python query_prometheus.py --query 'sum by (service) (rate(http_requests_total{status=~"5.."}[5m]))'

# CPU usage
python query_prometheus.py --query 'avg by (instance) (rate(container_cpu_usage_seconds_total{pod=~"api-.*"}[5m]))'

# Memory usage percentage
python query_prometheus.py --query 'container_memory_usage_bytes{pod=~"api-.*"} / container_spec_memory_limit_bytes{pod=~"api-.*"}'

http_requests_total                    # Counter
http_request_duration_seconds_bucket   # Histogram
http_requests_in_flight               # Gauge

container_cpu_usage_seconds_total
container_memory_usage_bytes
kube_pod_container_status_restarts_total
kube_pod_status_phase