managing-chronosphere

name	managing-chronosphere
description	Use when working with Chronosphere — chronosphere cloud-native observability platform for metrics, tracing, and alerting at scale. Covers PromQL-based metric queries, trace analysis, monitor management, dashboards, and control plane configuration. Use when querying Chronosphere metrics, investigating service performance, managing alerting rules, or analyzing distributed traces.
connection_type	chronosphere
preload	false

Chronosphere Monitoring Skill

Query, analyze, and manage Chronosphere observability data using the Chronosphere API.

API Overview

Chronosphere uses a REST API at your tenant URL: https://<TENANT>.chronosphere.io/api.

Core Helper Function

#!/bin/bash

chrono_api() {
    local method="$1"
    local endpoint="$2"
    local data="${3:-}"
    local url="${CHRONOSPHERE_URL}/api/v1/${endpoint}"
    if [ -n "$data" ]; then
        curl -s -X "$method" "$url" \
            -H "Authorization: Bearer $CHRONOSPHERE_API_TOKEN" \
            -H "Content-Type: application/json" \
            -d "$data"
    else
        curl -s -X "$method" "$url" \
            -H "Authorization: Bearer $CHRONOSPHERE_API_TOKEN"
    fi
}

chrono_query() {
    local promql="$1"
    local time="${2:-$(date +%s)}"
    chrono_api GET "query?query=$(python3 -c "import urllib.parse; print(urllib.parse.quote('${promql}'))")&time=${time}"
}

MANDATORY: Discovery-First Pattern

Always discover available metrics, services, and monitors before querying.

Phase 1: Discovery

#!/bin/bash

echo "=== Available Metric Namespaces ==="
chrono_api GET "label/__name__/values" | jq -r '.data[]' | cut -d'_' -f1 | sort -u | head -20

echo ""
echo "=== Monitors ==="
chrono_api GET "config/monitors" | jq -r '.monitors[] | "\(.slug)\t\(.name)\t\(.state // "unknown")"' | head -20

echo ""
echo "=== Dashboards ==="
chrono_api GET "config/dashboards" | jq -r '.dashboards[] | "\(.slug)\t\(.name)"' | head -20

echo ""
echo "=== Collections (Metric Groupings) ==="
chrono_api GET "config/collections" | jq -r '.collections[] | "\(.slug)\t\(.name)"' | head -15

Phase 2: Analysis

#!/bin/bash

echo "=== CPU Usage by Service (top 15) ==="
chrono_query 'avg by (service) (rate(process_cpu_seconds_total[5m]) * 100)' \
    | jq -r '.data.result[] | "\(.metric.service)\t\(.value[1])%"' | sort -t$'\t' -k2 -rn | head -15

echo ""
echo "=== Request Rate by Service ==="
chrono_query 'sum by (service) (rate(http_requests_total[5m]))' \
    | jq -r '.data.result[] | "\(.metric.service)\t\(.value[1]) req/s"' | sort -t$'\t' -k2 -rn | head -15

echo ""
echo "=== Error Rate by Service ==="
chrono_query 'sum by (service) (rate(http_requests_total{status=~"5.."}[5m])) / sum by (service) (rate(http_requests_total[5m])) * 100' \
    | jq -r '.data.result[] | "\(.metric.service)\t\(.value[1])%"' | sort -t$'\t' -k2 -rn | head -15

echo ""
echo "=== Firing Monitors ==="
chrono_api GET "config/monitors" | jq -r '.monitors[] | select(.state == "firing") | "\(.slug)\t\(.name)"' | head -10

Output Rules

• TOKEN EFFICIENCY: Target ≤50 lines — use PromQL aggregation and head in output
• Use avg by, sum by for grouping instead of raw series
• Prefer instant queries over range queries unless trend analysis is needed

Output Format

Present results as a structured report:

Managing Chronosphere Report
════════════════════════════
Resources discovered: [count]

Resource       Status    Key Metric    Issues
──────────────────────────────────────────────
[name]         [ok/warn] [value]       [findings]

Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]

Target ≤50 lines of output. Use tables for multi-resource comparisons.

Anti-Hallucination Rules

1. NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
2. NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
3. NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
4. ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
5. ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative