Log Debugger

システムログを分析してエラーの根本原因を特定し、段階的に深堀りしていくデバッグ専門家スキル。 アプリケーションログ、システムログ、クラウドサービスログなど様々な形式に対応。 5 Whys、タイムライン分析、Fishbone分析などのRCA（根本原因分析）手法を用いて 問題の本質を突き止め、再発防止策まで提案する。

Use when analyzing system logs to find error root causes, debugging application issues, or performing technical post-mortem analysis with log data. For organizational incident management processes (post-incident review, corrective action plans, incident reports without log data), use incident-rca-specialist instead.

Triggers: “analyze this log”, “find the root cause”, “debug this error”, “why is this failing”, “log analysis”, “what caused this crash”, “troubleshoot this issue”

No API Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

このスキルは、システムログを体系的に分析し、エラーの根本原因を特定するデバッグ専門家です。

対応ログタイプ:

  • アプリケーションログ（Python/Java/Node.js例外、スタックトレース）
  • システムログ（Linux syslog, journald, Windows Event Log）
  • クラウドサービスログ（AWS CloudWatch, Azure Monitor, GCP Logging）
  • Webサーバーログ（Apache, Nginx）
  • Kubernetesログ

2. Prerequisites

  • API Key: None required
  • Python 3.9+ recommended

3. Quick Start

Invoke this skill by describing your analysis needs to Claude.

4. How It Works

Follow the skill’s SKILL.md workflow step by step, starting from a small validated input.

5. Usage Examples

  • Use Log Debugger when you need a structured workflow rather than an ad-hoc answer.
  • Start with a small representative input before applying the workflow to production data or assets.
  • Review the helper scripts and reference guides to tailor the output format to your project.

6. Understanding the Output

  • A structured response or artifact aligned to the skill’s workflow.
  • Reference support from 4 guide file(s).
  • Script-assisted execution using 1 helper command(s) where applicable.
  • Reusable output that can be reviewed, refined, and incorporated into a wider project workflow.

7. Tips & Best Practices

  • Begin with the smallest realistic sample input so you can validate the workflow before scaling up.
  • Keep skills/log-debugger/SKILL.md open while working; it remains the authoritative source for the full procedure.
  • Review the most relevant reference files first instead of scanning every guide: log_format_guide.md, debugging_strategies.md, rca_methodology.md.
  • Run helper scripts on test data before using them on final assets or production-bound inputs: log_analyzer.py.
  • Preserve intermediate outputs so you can explain assumptions, diffs, and follow-up actions clearly.

8. Combining with Other Skills

  • Combine this skill with adjacent skills in the same category when the work spans planning, implementation, and review.
  • Browse the broader category for neighboring workflows: category index.
  • Use the English skill catalog when you need to chain this workflow into a larger end-to-end process.

9. Troubleshooting

  • Re-check prerequisites first: missing runtime dependencies and unsupported file formats are the most common failures.
  • If a helper script is involved, run it with a minimal sample input before applying it to a full dataset or repository.
  • Compare your input shape against the reference files to confirm expected fields, sections, or metadata are present.
  • Confirm the expected Python version and required packages are installed in the active environment.
  • When output looks incomplete, inspect the script arguments and rerun with explicit input/output paths.

10. Reference

References:

  • skills/log-debugger/references/debugging_strategies.md
  • skills/log-debugger/references/log_format_guide.md
  • skills/log-debugger/references/log_patterns.md
  • skills/log-debugger/references/rca_methodology.md

Scripts:

  • skills/log-debugger/scripts/log_analyzer.py