log analyzer sre

    1

    SRE-focused log and metric analyzer for incident triage, SLO management, and rapid service restoration.

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    log analyzer sre

    Example session with this skill installed

    Analyze this alert: [ALERT FIRING] Service: checkout-api, Env: PROD, Alert: HighErrorRate, expr: sum(rate(http_errors_total[5m])) / sum(rate(http_requests_total[5m])) > 0.05, duration: 10m.

    • Read your context and instructions
    • Compiled the log analyzer sre

    I have analyzed the firing alert for the checkout-api service in production. The current error rate is exceeding the 5% threshold, indicating a significant impact on the user journey.

    REPORTE DE ANÁLISIS — SRE / MONITORIZACIÓN

    ╔══════════════════════════════════════════════════════════════╗
    ║ REPORTE DE ANÁLISIS — SRE / MONITORIZACIÓN ║
    ╚══════════════════════════════════════════════════════════════╝
    📋 RESUMEN EJECUTIVO
    Stack: Prometheus/AlertManager | Entorno: PROD
    Servicio: checkout-api (Process payments) | Período: T-10m → Actual
    SLO/SLI: Error Rate < 0.1% / 5.0% — Budget consumido: 12% — Burn rate: 14.4x
    Diagnóstico breve: High error rate detected on checkout-api. Immediate SLO threat.

    🔴 HALLAZGOS CRÍTICOS

    SLO Breach: The current 5% error rate represents a fast burn (14.4x). At this rate, the monthly error budget will be exhausted in approximately 5 hours.

    • Urgent Mitigation: Restoration of service is required immediately.

    🟡 ADVERTENCIAS / 🔵 EVENTOS NOTABLES

    • The alert has been firing for 10 minutes without resolution.
    • Possible upstream dependency failure or recent configuration change.

    ⚠️ DATOS SENSIBLES: <span style="color:red;font-weight:bold">checkout-api-internal-01.prod.cluster.local</span>

    ❓ PREGUNTAS ABIERTAS

    • ¿Hubo deployment/cambio de config en los 30-60 min previos?
    • ¿Existen errores similares en los servicios de autenticación o base de datos?

    🛠️ PRÓXIMOS PASOS

    • INMEDIATO: Verificar historial de despliegues y realizar rollback si coincide con el inicio del pico.
    • CORTO PLAZO: Escalar capacidad si se observa saturación en los nodos de ejecución.
    • SEGUIMIENTO: Programar postmortem para analizar por qué el umbral del 5% no fue mitigado automáticamente.

    📤 ¿Deseas el reporte como documento entregable?

    Next steps

    • Check the CI/CD pipeline for recent production deployments.
    • Verify the health of the payment gateway third-party API.
    • Review kubectl logs --previous for the checkout pods.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Monitor metrics and alerts with a specialized SRE perspective. This tool handles raw data from Prometheus, Grafana, Datadog, and PagerDuty to provide structured incident triage. It prioritizes service restoration and SLO preservation over deep-dive root cause analysis during active failures.

    What it does

    • Incident Triage identifies SLO impact, environment criticality, and error budget burn rates.
    • Alert Correlation links PromQL expressions and alert notifications to recent deployments or infrastructure changes.
    • SLO/SLI Analysis calculates burn rates and remaining budget time to determine if an alert justifies paging or a support ticket.
    • Risk Identification highlights sensitive PII, internal architecture names, and integration tokens in red for security compliance.
    • Remediation Planning provides immediate restoration steps, mitigation strategies, and post-mortem follow-up actions.

    How it works

    1. Detection identifies incoming monitor data like [ALERT FIRING] or expr: labels.
    2. Context Inquiry asks for missing stack details, environment (PROD/STAGING), or recent deployment history.
    3. Calculation evaluates metrics against SLO targets to define burn rate (e.g., 14.4x for fast burn).
    4. Reporting generates a structured SRE report with executive summaries and critical findings.

    Frameworks & tools

    Supports Prometheus, Grafana, AlertManager, PagerDuty, OpsGenie, Datadog, New Relic, and Dynatrace.

    Why this beats prompting it yourself

    Generic prompts often suggest debugging application code during an outage. This skill enforces an SRE framework that prioritizes service restoration, rollback procedures, and SLO management.

    Use cases

    • Analyzing a PagerDuty firing alert to decide if an engineer needs to be woken up.
    • Calculating error budget depletion after a failed production deployment.
    • Correlating high p99 latency spikes with background saturation or GC pauses.

    Known limitations

    Focuses on metrics and monitoring alerts rather than raw application logs. Requires manually provided context regarding SLO targets and deployment history for full accuracy.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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