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log-analysis

Name: log-analysis
Author: benchflow-ai

by benchflow-ai890173GitHub

Log file analysis including reading, searching, filtering, and pattern matching. Use when investigating issues in logs, searching for errors, or analyzing application behavior.

Output Preview

output_preview.md

# Log Analysis Report: `app.log`

## Executive Summary
- **Time Range**: 2024-03-15 14:00 to 2024-03-15 15:30
- **Total Entries**: 12,847
- **Critical Issues**: 3 ERROR patterns identified

## Error Analysis

### Pattern 1: Database Connection Timeout (HIGH PRIORITY)
**Frequency**: 47 occurrences in last 90 minutes
**First Occurrence**: 2024-03-15 14:23:11
**Latest Occurrence**: 2024-03-15 15:28:44
**Pattern**:

2024-03-15 14:23:11 ERROR [DatabasePool-1] Connection timeout after 30000ms 2024-03-15 14:23:11 WARN [DatabasePool-1] Retrying connection (attempt 1/3) 2024-03-15 14:23:41 ERROR [DatabasePool-1] Connection failed: ECONNREFUSED

**Root Cause**: Database server under load, connection pool exhausted

### Pattern 2: API Rate Limit Exceeded (MEDIUM PRIORITY)
**Frequency**: 12 occurrences
**Affected Endpoints**:
- `/api/v1/users/search` (8 occurrences)
- `/api/v1/orders` (4 occurrences)

### Pattern 3: Memory Warning (LOW PRIORITY)
**Frequency**: 3 occurrences
**Memory Usage**: 85%, 87%, 89%

## Statistics Breakdown

| Log Level | Count | Percentage |
|-----------|-------|------------|
| INFO      | 9,842 | 76.6%      |
| WARN      | 2,901 | 22.6%      |
| ERROR     | 104   | 0.8%       |
| FATAL     | 0     | 0.0%       |

## Time Distribution

14:00-14:30: ████████░░ 320 errors/hour 14:30-15:00: ██████████ 410 errors/hour ← PEAK 15:00-15:30: ██████░░░░ 210 errors/hour


## Recommendations
1. **Immediate**: Increase database connection pool size from 50 to 100
2. **Short-term**: Implement exponential backoff for database retries
3. **Monitoring**: Set alert for error rate > 5/hour
4. **Investigate**: Check `/api/v1/users/search` endpoint for inefficient queries

## Search Queries Used
```bash
# Extract all errors
log_errors --file app.log --levels ERROR,FATAL

# Search for timeout patterns
log_search --file app.log --pattern "timeout|timed.out" --context 3

# Filter by peak time
log_filter --file app.log --start-time "2024-03-15 14:30" --end-time "2024-03-15 15:00"

# Get statistics
log_stats --file app.log --time-bucket "30min"

10/10Security

Low security risk, safe to use

Clarity

Practicality

Quality

Maintainability

Innovation

Development

Compatible Agents

Claude Code

~/.claude/skills/

Codex CLI

~/.codex/skills/

Gemini CLI

~/.gemini/skills/

OpenCode

~/.opencode/skills/

OpenClaw

~/.openclaw/skills/

GitHub Copilot

~/.copilot/skills/

Cursor

~/.cursor/skills/

Windsurf

~/.codeium/windsurf/skills/

Cline

~/.cline/skills/

Roo Code

~/.roo/skills/

Kiro

~/.kiro/skills/

Junie

~/.junie/skills/

Augment Code

~/.augment/skills/

Warp

~/.warp/skills/

Goose

~/.config/goose/skills/

SKILL.md

Log Analysis Skill

Version: 1.0.0 Purpose: Log file analysis and pattern discovery

Triggers

Trigger	Examples
Read	"read logs", "view log", "ログ表示"
Search	"search logs", "find error", "エラー検索"
Errors	"show errors", "エラー一覧"
Analyze	"analyze logs", "ログ分析"

Integrated MCP Tools

Tool	Purpose
`log_read`	Read log file contents
`log_search`	Search for patterns
`log_tail`	Last N lines of log
`log_stats`	Log statistics
`log_errors`	Extract error entries
`log_filter`	Filter by level/time
`log_aggregate`	Aggregate log entries

Workflow: Error Investigation

Phase 1: Initial Scan

Step 1.1: Check Recent Logs

Use log_tail with:
- file: Log file path
- lines: 100

Step 1.2: Get Error Summary

Use log_errors with:
- file: Log file path
- levels: ["ERROR", "FATAL", "CRITICAL"]

Phase 2: Pattern Search

Step 2.1: Search for Specific Error

Use log_search with:
- file: Log file path
- pattern: "Exception|Error|Failed"
- context: 3 (lines before/after)

Step 2.2: Filter by Time

Use log_filter with:
- file: Log file path
- start_time: "2024-01-01 10:00"
- end_time: "2024-01-01 11:00"

Phase 3: Analysis

Step 3.1: Statistics

Use log_stats to get:
- Total entries
- Entries by level
- Error frequency
- Time distribution

Step 3.2: Aggregate Patterns

Use log_aggregate to find:
- Repeated errors
- Common patterns
- Trending issues

Common Log Formats

Apache/Nginx

IP - - [timestamp] "METHOD /path HTTP/1.1" status size

Application (JSON)

{"timestamp":"...","level":"ERROR","message":"..."}

Syslog

Mon DD HH:MM:SS hostname process[pid]: message

Search Patterns

Pattern	Matches
`ERROR\|WARN`	Error or warning
`Exception.*`	Exception with message
`\d{3}`	HTTP status codes
`timeout\|timed out`	Timeout issues
`connection refused`	Connection failures

Best Practices

✅ GOOD:

Start with recent logs
Filter by time first
Look for patterns, not just single errors
Check error frequency

❌ BAD:

Read entire large log files
Search without time bounds
Focus on single error instance
Ignore warning patterns

Checklist

Source: https://github.com/benchflow-ai/SkillsBench#registry-terminal_bench_2.0-full_batch_reviewed-terminal_bench_2_0_log-summary-date-ranges-environment-skills-log-analysis

Content curated from original sources, copyright belongs to authors

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WORKS WITH

Claude

Codex

Gemini