Network Diagnostics

ネットワーク品質を総合的に診断し、ボトルネックの特定と根本原因の深堀りまで行うスキル。 接続情報、レイテンシ、ダウンロード速度、HTTP接続タイミング、経路解析を自動実行し、 品質閾値に基づく総合評価を日本語レポートとして出力する。 外部依存なし（OS標準ツールのみ使用）、macOSおよびLinux対応。

Use when diagnosing network quality, troubleshooting slow connections, identifying network bottlenecks, measuring latency/bandwidth/jitter, or generating network health reports.

「ネットワーク診断」「network diagnostics」「ネットワークが遅い」 「latency check」「bandwidth test」「traceroute analysis」

No API Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Network Diagnostics

2. Prerequisites

  • API Key: None required
  • Python 3.9+ recommended

3. Quick Start 

python3 scripts/network_diagnostics.py -o /tmp/network_diag.json

4. How It Works

Phase 1: COLLECT (Data Collection)

Run the diagnostics script to collect all network metrics as JSON:

python3 scripts/network_diagnostics.py -o /tmp/network_diag.json

CLI Options:

Option Description Default
-o FILE Output file (default: stdout) stdout
-t host,label Add custom target (repeatable) -
--skip-traceroute Skip traceroute false
--skip-speed Skip download speed tests false
--ping-count N Ping packet count 10

Collected Data:

  1. Connection Info - Interface, type (Ethernet/Wi-Fi), IP, gateway, DNS, ISP, MAC, MTU
  2. Ping Tests - Gateway + 8.8.8.8 + 1.1.1.1 (+ custom targets) → avg/min/max/jitter/loss
  3. HTTP Timing - DNS resolution, TCP connect, TLS handshake, TTFB, total
  4. Download Speed - Cloudflare + OVH + Hetzner CDN endpoints → Mbps

See the skill’s SKILL.md for the full end-to-end workflow.

5. Usage Examples

  • Use Network Diagnostics 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 2 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/network-diagnostics/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: network_quality_thresholds.md, deep_dive_procedures.md.
  • Run helper scripts on test data before using them on final assets or production-bound inputs: network_diagnostics.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/network-diagnostics/references/deep_dive_procedures.md
  • skills/network-diagnostics/references/network_quality_thresholds.md

Scripts:

  • skills/network-diagnostics/scripts/network_diagnostics.py