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managing-chronosphere

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.

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  • SKILL.md5.4 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

ShortcutCounterWhy
"I'll skip discovery and check known resources"Always run Phase 1 discovery firstResource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"Follow the full discovery → analysis flowQuick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"Audit configuration explicitlyDefaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"Always check relevant metrics when availableAPI/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 errorAssumed permission failures prevent useful investigation; actual errors are informative

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