Logfile Analyzer

Analyze application logs to produce actionable error digests with pattern detection, severity classification, trend analysis, and remediation recommendations...

Security Scan

The skill's code, instructions, and requirements are consistent with a local log-file analysis tool; it asks for no unrelated credentials or installs and operates on files you pass it.

Purpose & Capability

Name/description match the included Python analyzer (scripts/analyze_logs.py). The script and SKILL.md focus on parsing log files, grouping errors, trend detection, and recommendations — all coherent with a logfile analyzer. No unrelated binaries, environment variables, or cloud credentials are requested.

Instruction Scope

Instructions direct the agent/user to run the included script against local log files or directories (examples use /var/log). This is expected for the stated purpose. Note: the tool will read any file paths you provide (logs can contain sensitive data), so feeding it arbitrary system paths grants it access to those files.

Install Mechanism

No install spec — instruction-only with an included pure-Python (stdlib) script. No downloads, no external package registry dependencies, and no archives to extract.

Credentials

The skill declares no required environment variables or credentials and the included script imports only stdlib modules. There are no obvious requests for unrelated secrets or external service tokens.

Persistence & Privilege

always:false and no install behavior that modifies other skills or system-wide settings. The skill does not request permanent presence or privileged agent-wide configuration.

Assessment

This appears to be a straightforward local log analyzer, but consider the following before installing or running it: 1) Logs often contain sensitive data (API keys, PII, auth tokens) — run the tool only on files you intend to analyze and preferably on a copy or in a sandbox. 2) Review the included script (scripts/analyze_logs.py) yourself — it runs locally and will read whatever paths you pass to it. 3) The skill has no provenance information (no homepage) and is sold/packaged by an unknown owner — if this matters for your environment, prefer tools from known sources or audit the code. 4) For automated runs (cron/CI), give the process least privilege (read-only access to specific log directories) and avoid sending raw outputs to external endpoints unless you control them.

Log Analyzer

Parse application logs into actionable error digests with pattern grouping, severity classification, trend detection, and remediation recommendations.

Quick Start

# Analyze a single log file
python3 scripts/analyze_logs.py /var/log/app.log

# Analyze all logs in a directory
python3 scripts/analyze_logs.py /var/log/myapp/

# Last 24 hours only, errors and above
python3 scripts/analyze_logs.py /var/log/app.log --since 24h --severity error

# JSON output for programmatic use
python3 scripts/analyze_logs.py /var/log/app.log --output json

# Markdown report with trends
python3 scripts/analyze_logs.py /var/log/app.log --output markdown --trends

# Ignore noisy patterns
python3 scripts/analyze_logs.py /var/log/app.log --ignore "healthcheck" --ignore "GET /favicon"

Supported Formats (Auto-Detected)

• JSON structured — Bunyan, Winston, Pino, structlog, any {"level": ..., "msg": ...} format
• Syslog — RFC 3164 (Mar 28 02:31:00 host service: msg)
• Apache/Nginx access — Combined log format
• Nginx error — 2026/03/28 02:31:00 [error] ...
• Python tracebacks — Multi-line traceback collection
• Docker — ISO 8601 timestamps with container output
• Generic timestamped — [2026-03-28 02:31:00] LEVEL: message

Force format with --format <name> if auto-detection fails.

What It Does

1. Parses log entries with format auto-detection
2. Classifies severity (TRACE → DEBUG → INFO → WARN → ERROR → FATAL)
3. Normalizes messages (replaces UUIDs, IPs, timestamps, paths with placeholders)
4. Groups similar errors by fingerprint to find recurring patterns
5. Ranks by severity and frequency
6. Detects trends with --trends (hourly frequency buckets)
7. Recommends fixes for 15+ known error patterns (OOM, connection refused, disk full, timeouts, SSL issues, rate limits, etc.)

Options

Flag	Default	Description
--format	auto	Force log format
--since	all	Time filter (1h, 24h, 7d, or ISO date)
--severity	warn	Minimum severity to report
--top	20	Number of top patterns to show
--output	text	Output format: text, json, markdown
--trends	off	Show hourly frequency trends
--ignore	none	Regex patterns to exclude (repeatable)
-q	off	Summary only, skip individual entries

Exit Codes

• 0 — No errors found
• 1 — Errors found (warn/error level)
• 2 — Fatal/critical entries found

Use in CI/CD pipelines to fail builds on log errors.

Workflow

Incident Investigation

1. Run with --since 1h --severity error --trends to see recent errors with frequency
2. Review top patterns — the most frequent errors are usually the root cause
3. Check recommendations for known patterns
4. Use --output json to feed into monitoring dashboards

Periodic Health Check

1. Run with --since 24h --output markdown for a daily report
2. Compare pattern counts across days to spot trends
3. Set up as cron job for automated daily digests

Deep Dive

1. Run with --severity debug to see full picture
2. Use --ignore to filter out known noise
3. Check references/error-patterns.md for detailed remediation steps on specific error types

Error Pattern Reference

For detailed remediation guidance on specific error types (memory, network, database, SSL, etc.), see references/error-patterns.md.