Skill: gitgoodordietrying/log-analyzer (version 1.0.0)

--- name: log-analyzer description: Parse, search, and analyze application logs across formats. Use when debugging from log files, setting up structured logging, analyzing error patterns, correlating events across services, parsing stack traces, or monitoring log output in real time. metadata: {"clawdbot":{"emoji":"📋","requires":{"anyBins":["grep","awk","jq","python3"]},"os":["linux","darwin","win32"]}} --- # Log Analyzer Parse, search, and debug from application logs. Covers plain text logs, structured JSON logs, stack traces, multi-service correlation, and real-time monitoring. ## When to Use - Debugging application errors from log files - Searching logs for specific patterns, errors, or request IDs - Parsing and analyzing stack traces - Setting up structured logging (JSON) in applications - Correlating events across multiple services or log files - Monitoring logs in real time during development - Generating error frequency reports or summaries ## Quick Search Patterns ### Find errors and exceptions ```bash # All errors in a log file grep -i 'error\|exception\|fatal\|panic\|fail' app.log # Errors with 3 lines of context grep -i -C 3 'error\|exception' app.log # Errors in the last hour (ISO timestamps) HOUR_AGO=$(date -u -d '1 hour ago' '+%Y-%m-%dT%H:%M' 2>/dev/null || date -u -v-1H '+%Y-%m-%dT%H:%M') awk -v t="$HOUR_AGO" '$0 ~ /^[0-9]{4}-[0-9]{2}-[0-9]{2}T/ && $1 >= t' app.log | grep -i 'error' # Count errors by type grep -oP '(?:Error|Exception): \K[^\n]+' app.log | sort | uniq -c | sort -rn | head -20 # HTTP 5xx errors from access logs awk '$9 >= 500' access.log ``` ### Search by request or correlation ID ```bash # Trace a single request across log entries grep 'req-abc123' app.log # Across multiple files grep -r 'req-abc123' /var/log/myapp/ # Across multiple services (with filename prefix) grep -rH 'correlation-id-xyz' /var/log/service-a/ /var/log/service-b/ /var/log/service-c/ ``` ### Time-range filtering ```bash # Between two timestamps (ISO format) awk '$0 >= "2026-02-03T10:00" && $0 <= "2026-02-03T11:00"' app.log # Last N lines (tail) tail -1000 app.log | grep -i error # Since a specific time (GNU date) awk -v start="$(date -d '30 minutes ago' '+%Y-%m-%dT%H:%M')" '$1 >= start' app.log ``` ## JSON / Structured Logs ### Parse with jq ```bash # Pretty-print JSON logs cat app.log | jq '.' # Filter by level cat app.log | jq 'select(.level == "error")' # Filter by time range cat app.log | jq 'select(.timestamp >= "2026-02-03T10:00:00Z")' # Extract specific fields cat app.log | jq -r '[.timestamp, .level, .message] | @tsv' # Count by level cat app.log | jq -r '.level' | sort | uniq -c | sort -rn # Filter by nested field cat app.log | jq 'select(.context.userId == "user-123")' # Group errors by message cat app.log | jq -r 'select(.level == "error") | .message' | sort | uniq -c | sort -rn # Extract request duration stats cat app.log | jq -r 'select(.duration != null) | .duration' | awk '{sum+=$1; count++; if($1>max)max=$1} END {print "count="count, "avg="sum/count, "max="max}' ``` ### Parse mixed-format logs (JSON lines mixed with plain text) ```bash # Extract only valid JSON lines while IFS= read -r line; do echo "$line" | jq '.' 2>/dev/null && continue done < app.log # Or with grep for lines starting with { grep '^\s*{' app.log | jq '.' ``` ## Stack Trace Analysis ### Extract and deduplicate stack traces ```bash # Extract Java/Kotlin stack traces (starts with Exception/Error, followed by \tat lines) awk '/Exception|Error/{trace=$0; while(getline && /^\t/) trace=trace"\n"$0; print trace"\n---"}' app.log # Extract Python tracebacks awk '/^Traceback/{p=1} p{print} /^[A-Za-z].*Error/{if(p) print "---"; p=0}' app.log # Extract Node.js stack traces (Error + indented "at" lines) awk '/Error:/{trace=$0; while(getline && /^ at /) trace=trace"\n"$0; print trace"\n---"}' app.log # Deduplicate: group by root cause (first line of trace) awk '/Exception|Error:/{cause=$0} /^\tat|^ at /{next} cause{print cause; cause=""}' app.log | sort | uniq -c | sort -rn ``` ### Python traceback parser ```python #!/usr/bin/env python3 """Parse Python tracebacks from log files and group by root cause.""" import sys import re from collections import Counter def extract_tracebacks(filepath): tracebacks = [] current = [] in_trace = False with open(filepath) as f: for line in f: if line.startswith('Traceback (most recent call last):'): in_trace = True current = [line.rstrip()] elif in_trace: current.append(line.rstrip()) # Exception line ends the traceback if re.match(r'^[A-Za-z]\w*(Error|Exception|Warning)', line): tracebacks.append('\n'.join(current)) in_trace = False current = [] return tracebacks if __name__ == '__main__': filepath = sys.argv[1] if len(sys.argv) > 1 else '/dev/stdin' traces = extract_tracebacks(filepath) # Group by exception type and message causes = Counter() for trace in traces: lines = trace.split('\n') cause = lines[-1] if lines else 'Unknown' causes[cause] += 1 print(f"Found {len(traces)} tracebacks, {len(causes)} unique causes:\n") for cause, count in causes.most_common(20): print(f" {count:4d}x {cause}") ``` ## Real-Time Monitoring ### Tail and filter ```bash # Follow log file, highlight errors in red tail -f app.log | grep --color=always -i 'error\|warn\|$' # Follow and filter to errors only tail -f app.log | grep --line-buffered -i 'error\|exception' # Follow JSON logs, pretty-print errors tail -f app.log | while IFS= read -r line; do level=$(echo "$line" | jq -r '.level // empty' 2>/dev/null) if [ "$level" = "error" ] || [ "$level" = "fatal" ]; then echo "$line" | jq '.' fi done # Follow multiple files tail -f /var/log/service-a/app.log /var/log/service-b/app.log # Follow with timestamps (useful when log doesn't include them) tail -f app.log | while IFS= read -r line; do echo "$(date '+%H:%M:%S') $line" done ``` ### Watch for specific patterns and alert ```bash # Beep on error (terminal bell) tail -f app.log | grep --line-buffered -i 'error' | while read line; do echo -e "\a$line" done # Count errors per minute tail -f app.log | grep --line-buffered -i 'error' | while read line; do echo "$(date '+%Y-%m-%d %H:%M') ERROR" done | uniq -c ``` ## Log Format Parsing ### Common access log (Apache/Nginx) ```bash # Parse fields: IP, date, method, path, status, size awk '{print $1, $9, $7}' access.log # Top IPs by request count awk '{print $1}' access.log | sort | uniq -c | sort -rn | head -20 # Top paths by request count awk '{print $7}' access.log | sort | uniq -c | sort -rn | head -20 # Slow requests (response time in last field, microseconds) awk '{if ($NF > 1000000) print $0}' access.log # Requests per minute awk '{split($4,a,":"); print a[1]":"a[2]":"a[3]}' access.log | uniq -c # Status code distribution awk '{print $9}' access.log | sort | uniq -c | sort -rn # 4xx and 5xx with paths awk '$9 >= 400 {print $9, $7}' access.log | sort | uniq -c | sort -rn | head -20 ``` ### Custom delimited logs ```bash # Pipe-delimited: timestamp|level|service|message awk -F'|' '{print $2, $3, $4}' app.log # Tab-delimited awk -F'\t' '$2 == "ERROR" {print $1, $4}' app.log # CSV logs python3 -c " import csv, sys with open(sys.argv[1]) as f: for row in csv.DictReader(f): if row.get('level') == 'error': print(f\"{row['timestamp']} {row['message']}\") " app.csv ``` ## Setting Up Structured Logging ### Node.js (pino — fast JSON logger) ```javascript // npm install pino const pino = require('pino'); const logger = pino({ level: process.env.LOG_LEVEL || 'info', // Add standard fields to every log line base: { service: 'my-api', version: '1.2.0' }, }); // Usage logger.info({ userId: 'u123', action: 'login' }, 'User logged in'); logger.error({ err, requestId: req.id }, 'Request failed'); // Output: {"level":30,"time":1706900000000,"service":"my-api","userId":"u123","action":"login","msg":"User logged in"} // Child logger with bound context const reqLogger = logger.child({ requestId: req.id, userId: req.user?.id }); reqLogger.info('Processing order'); reqLogger.error({ err }, 'Order failed'); ``` ### Python (structlog) ```python # pip install structlog import structlog structlog.configure( processors=[ structlog.processors.TimeStamper(fmt="iso"), structlog.processors.add_log_level, structlog.processors.JSONRenderer(), ], ) logger = structlog.get_logger(service="my-api") # Usage logger.info("user_login", user_id="u123", ip="1.2.3.4") logger.error("request_failed", request_id="req-abc", error=str(e)) # Output: {"event":"user_login","user_id":"u123","ip":"1.2.3.4","level":"info","timestamp":"2026-02-03T12:00:00Z","service":"my-api"} ``` ### Go (zerolog) ```go import ( "os" "github.com/rs/zerolog" "github.com/rs/zerolog/log" ) func init() { zerolog.TimeFieldFormat = zerolog.TimeFormatUnix log.Logger = zerolog.New(os.Stdout).With(). Timestamp(). Str("service", "my-api"). Logger() } // Usage log.Info().Str("userId", "u123").Msg("User logged in") log.Error().Err(err).Str("requestId", reqID).Msg("Request failed") ``` ## Error Pattern Reports ### Generate error frequency report ```bash #!/bin/bash # error-report.sh - Summarize errors from a log file LOG="${1:?Usage: error-report.sh <logfile>}" echo "=== Error Report: $(basename "$LOG") ===" echo "Generated: $(date -u '+%Y-%m-%dT%H:%M:%SZ')" echo "" total=$(wc -l < "$LOG") errors=$(grep -ci 'error\|exception\|fatal' "$LOG") warns=$(grep -ci 'warn' "$LOG") echo "Total lines: $total" echo "Errors: $errors" echo "Warnings: $warns" echo "" echo "--- Top 15 Error Messages ---" grep -i 'error\|exception' "$LOG" | \ sed 's/^[0-9TZ:.+\-]* //' | \ sed 's/\b[0-9a-f]\{8,\}\b/ID/g' | \ sed 's/[0-9]\{1,\}/N/g' | \ sort | uniq -c | sort -rn | head -15 echo "" echo "--- Errors Per Hour ---" grep -i 'error\|exception' "$LOG" | \ grep -oP '\d{4}-\d{2}-\d{2}T\d{2}' | \ sort | uniq -c echo "" echo "--- First Occurrence of Each Error Type ---" grep -i 'error\|exception' "$LOG" | \ sed 's/^[0-9TZ:.+\-]* //' | \ sort -u | head -10 ``` ### JSON log error report with Python ```python #!/usr/bin/env python3 """Generate error summary from JSON log files.""" import json import sys from collections import Counter, defaultdict from datetime import datetime def analyze_logs(filepath): errors = [] levels = Counter() errors_by_hour = defaultdict(int) with open(filepath) as f: for line in f: try: entry = json.loads(line.strip()) except (json.JSONDecodeError, ValueError): continue level = entry.get('level', entry.get('severity', '')).lower() levels[level] += 1 if level in ('error', 'fatal', 'critical'): msg = entry.get('message', entry.get('msg', entry.get('event', 'unknown'))) ts = entry.get('timestamp', entry.get('time', '')) errors.append({'message': msg, 'timestamp': ts, 'entry': entry}) # Group by hour try: hour = ts[:13] # "2026-02-03T12" errors_by_hour[hour] += 1 except (TypeError, IndexError): pass # Group errors by message error_counts = Counter(e['message'] for e in errors) print(f"=== Log Analysis: {filepath} ===\n") print("Level distribution:") for level, count in levels.most_common(): print(f" {level:10s} {count}") print(f"\nTotal errors: {len(errors)}") print(f"Unique error messages: {len(error_counts)}\n") print("Top 15 errors:") for msg, count in error_counts.most_common(15): print(f" {count:4d}x {msg[:100]}") if errors_by_hour: print("\nErrors by hour:") for hour in sorted(errors_by_hour): bar = '#' * min(errors_by_hour[hour], 50) print(f" {hour} {errors_by_hour[hour]:4d} {bar}") if __name__ == '__main__': analyze_logs(sys.argv[1]) ``` ## Multi-Service Log Correlation ### Merge and sort logs from multiple services ```bash # Merge multiple log files, sort by timestamp sort -m -t'T' -k1,1 service-a.log service-b.log service-c.log > merged.log # If files aren't individually sorted, use full sort sort -t'T' -k1,1 service-*.log > merged.log # Merge JSON logs, add source field for f in service-*.log; do service=$(basename "$f" .log) jq --arg svc "$service" '. + {source: $svc}' "$f" done | jq -s 'sort_by(.timestamp)[]' ``` ### Trace a request across services ```bash # Find all log entries for a correlation/request ID across all services REQUEST_ID="req-abc-123" grep -rH "$REQUEST_ID" /var/log/services/ | sort -t: -k2 # With JSON logs for f in /var/log/services/*.log; do jq --arg rid "$REQUEST_ID" 'select(.requestId == $rid or .correlationId == $rid)' "$f" 2>/dev/null done | jq -s 'sort_by(.timestamp)[]' ``` ## Log Rotation and Large Files ### Working with rotated/compressed logs ```bash # Search across rotated logs (including .gz) zgrep -i 'error' /var/log/app.log* # Search today's and yesterday's logs zgrep -i 'error' /var/log/app.log /var/log/app.log.1 # Decompress, filter, and recompress zcat app.log.3.gz | grep 'ERROR' | gzip > errors-day3.gz ``` ### Sampling large files ```bash # Random sample of 1000 lines shuf -n 1000 huge.log > sample.log # Every 100th line awk 'NR % 100 == 0' huge.log > sample.log # First and last 500 lines { head -500 huge.log; echo "--- TRUNCATED ---"; tail -500 huge.log; } > excerpt.log ``` ## Tips - Always search for a **request ID or correlation ID** first — it narrows the haystack faster than timestamps or error messages. - Use `--line-buffered` with `grep` when piping from `tail -f` so output isn't delayed by buffering. - Normalize IDs and numbers before grouping errors (`sed 's/[0-9a-f]\{8,\}/ID/g'`) to collapse duplicates that differ only by ID. - For JSON logs, `jq` is indispensable. Install it if it's not available: `apt install jq` / `brew install jq`. - Structured logging (JSON) is always worth the setup cost. It makes every analysis task easier: filtering, grouping, correlation, and alerting all become `jq` one-liners. - When debugging a production issue: get the **time window** and **affected user/request ID** first, then filter logs to that scope before reading anything. - `awk` is faster than `grep | sort | uniq -c` pipelines for large files. Use it for counting and aggregation.


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