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name: log-analyzer description: | Production log analysis and diagnostics skill. Use this skill when asked to:

• Fetch logs from a monitoring API
• Analyze log patterns and errors
• Diagnose production issues
• Generate log summaries and statistics license: MIT compatibility: python3, requests allowed-tools: Bash(python3:*) Read Write

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Log Analyzer Skill

A production monitoring skill for fetching, analyzing, and diagnosing issues from application logs.

Available Scripts

1. Fetch Logs (fetch_logs.py)

Fetches logs from a REST API endpoint.

# Fetch last 100 logs
python3 scripts/fetch_logs.py --endpoint "http://localhost:8080/api/logs" --limit 100

# Fetch logs with time range
python3 scripts/fetch_logs.py --endpoint "http://localhost:8080/api/logs" \
  --start "2024-01-15T00:00:00" --end "2024-01-15T23:59:59"

# Fetch logs by severity
python3 scripts/fetch_logs.py --endpoint "http://localhost:8080/api/logs" \
  --level ERROR

Options:

• --endpoint - REST API URL for logs (required)
• --limit - Maximum number of logs to fetch (default: 100)
• --start - Start time (ISO 8601 format)
• --end - End time (ISO 8601 format)
• --level - Filter by log level (DEBUG, INFO, WARN, ERROR)
• --output - Output file (default: stdout)

2. Parse Logs (parse_logs.py)

Parses log files in various formats.

# Parse JSON logs
python3 scripts/parse_logs.py --format json logs.json

# Parse text logs with pattern
python3 scripts/parse_logs.py --format text --pattern "%(timestamp)s %(level)s %(message)s" app.log

# Parse and filter by level
python3 scripts/parse_logs.py logs.json --level ERROR

Supported Formats:

• json - JSON lines format
• text - Plain text with pattern
• auto - Auto-detect format

3. Analyze Logs (analyze.py)

Analyzes logs for patterns, errors, and anomalies.

# Full analysis
python3 scripts/analyze.py logs.json

# Error analysis only
python3 scripts/analyze.py logs.json --errors-only

# Generate summary
python3 scripts/analyze.py logs.json --summary

# Find patterns
python3 scripts/analyze.py logs.json --patterns

Analysis Types:

• Error frequency and patterns
• Response time anomalies
• Request volume trends
• Common error messages
• Suggested diagnostics

Example Workflow

1. Fetch recent logs:

   python3 scripts/fetch_logs.py --endpoint "http://api.example.com/logs" \
     --limit 500 --output recent_logs.json
   

2. Analyze for errors:

   python3 scripts/analyze.py recent_logs.json --errors-only
   

3. Get summary:

   python3 scripts/analyze.py recent_logs.json --summary
   

Log Format Reference

See references/log_formats.md for supported log formats and examples.

Tips

• Always start by fetching recent logs with a reasonable limit
• Use --errors-only for quick triage
• Use --summary to understand overall health
• Check --patterns to find recurring issues