# Log Analyzer

> Log Analyzer

- Skill: `comeonoliver/log-analyzer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add comeonoliver/log-analyzer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/comeonoliver/log-analyzer/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ComeOnOliver (https://skillmd.com/u/comeonoliver)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/comeonoliver/log-analyzer

---

# Log Analyzer

## Overview

Parse, filter, and analyze application and infrastructure logs to quickly identify root causes of production issues. Handles JSON structured logs, plaintext server logs, syslog format, and multi-line stack traces.

## Instructions

### 1. Identify log format

Detect whether logs are JSON (one object per line), plaintext with timestamps, syslog, or mixed format. Look for common patterns:
- JSON: `{"timestamp":"...","level":"ERROR","message":"..."}`
- Syslog: `Feb 17 14:23:01 hostname service[pid]: message`
- Plaintext: `2026-02-17 14:23:01.234 ERROR [thread] class - message`

### 2. Filter and extract relevant entries

When investigating an incident:
- Filter by time window (e.g., last 30 minutes before the alert)
- Filter by severity (ERROR, FATAL, WARN)
- Extract unique error messages and group by frequency
- Identify the first occurrence of each error type

### 3. Correlate events

Look for causal chains:
- What was the FIRST error in the time window?
- Are there upstream errors that preceded downstream failures?
- Do errors correlate with deployment events, config changes, or traffic spikes?
- Track request IDs or correlation IDs across services

### 4. Summarize findings

Present results as:
```
## Incident Timeline
- HH:MM:SS — First error: [description]
- HH:MM:SS — Error cascade begins: [description]
- HH:MM:SS — Service degradation detected

## Root Cause
[Clear explanation of what went wrong]

## Error Distribution
| Error Type | Count | First Seen | Last Seen |
|-----------|-------|------------|-----------|
| ConnectionTimeout | 1,247 | 14:02:03 | 14:31:45 |
| NullPointerException | 89 | 14:02:15 | 14:28:00 |

## Recommended Actions
1. [Immediate fix]
2. [Follow-up investigation]
```

### 5. Handle large log files

For files too large to read at once:
- Start with the last 500 lines to see recent state
- Search for ERROR/FATAL/EXCEPTION keywords
- Use grep patterns to narrow scope before deep reading
- Sample periodically to detect patterns over time

## Examples

### Example 1: API Error Spike

**Input:** "Analyze these application logs — we're seeing 500 errors spike in the last hour"

**Log content:**
```
2026-02-17 13:58:22.001 INFO  [http-8080-12] c.a.OrderService - Processing order #98712
2026-02-17 13:58:22.450 ERROR [http-8080-12] c.a.OrderService - Failed to process order #98712: Connection refused to payment-service:8443
2026-02-17 13:58:22.451 ERROR [http-8080-12] c.a.ApiController - 500 Internal Server Error: PaymentServiceUnavailable
2026-02-17 13:58:23.100 WARN  [health-check] c.a.HealthMonitor - payment-service health check failed (attempt 3/3)
2026-02-17 13:58:23.101 ERROR [health-check] c.a.HealthMonitor - Circuit breaker OPEN for payment-service
```

**Output:**
```
## Incident Timeline
- 13:58:22 — First connection refused to payment-service:8443
- 13:58:23 — Health check confirms payment-service is down
- 13:58:23 — Circuit breaker tripped

## Root Cause
payment-service became unreachable at port 8443, causing all order processing
to fail with ConnectionRefused errors. The circuit breaker opened after 3
consecutive health check failures.

## Recommended Actions
1. Check payment-service pod status and recent deployments
2. Verify network policies haven't changed for port 8443
3. Review payment-service logs for OOM kills or crash loops
```

### Example 2: Memory Leak Detection

**Input:** "Our service keeps restarting every 6 hours, here are the logs around the last crash"

**Log content:**
```
2026-02-17 08:00:01.000 INFO  Heap usage: 512MB / 2048MB (25%)
2026-02-17 10:00:01.000 INFO  Heap usage: 1024MB / 2048MB (50%)
2026-02-17 12:00:01.000 INFO  Heap usage: 1720MB / 2048MB (84%)
2026-02-17 13:45:30.000 WARN  GC overhead limit exceeded, heap: 1980MB / 2048MB
2026-02-17 13:47:12.000 ERROR java.lang.OutOfMemoryError: Java heap space
2026-02-17 13:47:12.001 ERROR   at com.app.cache.SessionCache.put(SessionCache.java:142)
```

**Output:**
```
## Root Cause
Memory leak in SessionCache — heap grows linearly from 25% to 97% over ~6 hours
before OOM kill. The SessionCache.put() method at line 142 is the allocation hotspot.

## Error Distribution
| Metric | Value |
|--------|-------|
| Growth rate | ~250MB/hour |
| Time to OOM | ~6 hours |
| Leak source | SessionCache.put (line 142) |

## Recommended Actions
1. Review SessionCache eviction policy — entries may not be expiring
2. Add max-size bound to the cache (e.g., Caffeine with maximumSize)
3. Enable heap dump on OOM: -XX:+HeapDumpOnOutOfMemoryError
```

## Guidelines

- Always establish a timeline first — chronological order reveals causality
- Look for the FIRST error, not the most frequent — cascading failures amplify
- Correlation IDs and request IDs are your best friend for distributed systems
- Don't ignore WARN-level messages — they often precede errors
- When logs are from multiple services, group by service first, then correlate
- Be specific about line numbers, timestamps, and error counts
- Suggest concrete next steps, not generic "investigate further"