Log Analyzer

Overview

When systems fail at 3 AM, the first thing you do is check the logs. But modern applications generate millions of log lines across dozens of services, making manual analysis impractical. Log Analyzer automates the process of finding the signal in the noise.

The skill ingests logs from files, streams, or aggregation platforms (ELK, Datadog, CloudWatch), automatically detects the log format, and applies AI analysis to surface anomalies, error spikes, and correlation patterns. It doesn’t just find errors — it traces them across service boundaries to identify root causes.

Built on Semantic Kernel for multi-model orchestration, it combines pattern matching for known issues with AI analysis for novel problems.

How It Works

1. Ingest — Connect to log sources (files, streams, APIs)
2. Parse — Auto-detect log format and extract structured fields
3. Detect — Identify anomalies, error spikes, and unusual patterns
4. Correlate — Trace issues across services and time windows
5. Report — Generate root cause analysis with timeline and evidence

Use Cases

• Incident response — Quickly identify root cause during outages
• Proactive monitoring — Detect anomalies before they become incidents
• Performance analysis — Find slow endpoints and resource bottlenecks
• Security — Detect suspicious access patterns and potential breaches
• Compliance — Audit log analysis for regulatory requirements

Getting Started

# Analyze local log files
log-analyze --input /var/log/app/*.log --format auto

# Stream analysis
log-analyze --stream "https://logs.example.com/api/stream" --alert slack

# Multi-service correlation
log-analyze --sources "api-service,auth-service,database" --timerange "last 2h"

Example

Analysis: Production logs (last 2 hours)

🔴 ANOMALY DETECTED at 14:23 UTC
Error rate spike: 0.1% → 12.4% over 3 minutes

Root Cause Analysis:
1. [14:22:45] auth-service: Redis connection timeout (new error)
2. [14:22:48] api-service: 500 errors spike (cascading from auth)
3. [14:23:01] frontend: Retry storm (3x normal request volume)

Timeline:
  14:22:45 — Redis node failover triggered
  14:22:48 — Auth service cache miss → DB fallback → slow responses
  14:23:01 — API timeouts trigger client retries
  14:25:00 — Redis failover complete, errors subsiding

Recommendation: Add circuit breaker between API and auth service

Alternatives

• Datadog Log Management — Cloud-native log analytics
• Elastic (ELK) — Open-source log aggregation and analysis
• Splunk — Enterprise log management and SIEM