Net Detective is a comprehensive network diagnostic suite that translates complex technical data into plain-English explanations of connectivity issues.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install net-detective
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install net-detective using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Net Detective?
Net Detective is a specialized diagnostic toolset within the Openclaw Skills ecosystem designed to move beyond simple ping tests. It provides deep visibility into network health by testing DNS resolution across multiple providers, detecting MTU fragmentation, and measuring throughput. Its primary goal is to empower users and AI agents to identify exactly why a connection is slow or unstable.
Built with portability in mind, the skill uses only the Python standard library, making it highly compatible across macOS and Linux environments. By comparing real-time results against historical baselines, Net Detective can pinpoint whether a performance drop is an anomaly or a persistent infrastructure problem, providing actionable insights rather than just raw data points.
Net Detective Use Cases
- Troubleshooting slow internet speeds or intermittent connection drops.
- Identifying DNS resolution failures that prevent website access.
- Diagnosing packet loss to determine if the issue is local (router/Wi-Fi) or at the ISP level.
- Detecting MTU fragmentation issues often caused by VPNs or PPPoE configurations.
- Establishing a performance baseline for home or office networks to monitor degradation over time.
How Net Detective Works
- The diagnose script orchestrates a series of tests including DNS checks, latency measurements, and optional speed tests.
- The data is collected and structured into a unified JSON format for consistent processing.
- A history script optionally compares the current results against a stored baseline to identify performance regressions.
- A report generator processes the JSON output to create a human-readable Markdown summary.
- The final result is recorded into the history database to improve future diagnostic accuracy within the Openclaw Skills framework.
Net Detective Setup
To integrate Net Detective into your Openclaw Skills workflow, ensure you have Python 3 installed. No external dependencies are required.
# Run a full diagnostic with speed testing
python3 scripts/diagnose.py --speed > /tmp/net-diag.json
# Record the result to establish a baseline
python3 scripts/history.py --record /tmp/net-diag.json
# Generate a plain-English report
python3 scripts/report.py /tmp/net-diag.json
Net Detective Data Schema & Taxonomy
Net Detective organizes its findings using structured JSON and Markdown reports. The following table describes the primary data components:
| Component | Format | Description |
|---|---|---|
| Diagnostic Output | JSON | Raw data containing DNS results, latency, MTU, and speed metrics. |
| Historical Baseline | JSON | Aggregated data used to detect anomalies vs normal network behavior. |
| Final Report | Markdown | A plain-English summary featuring headline findings and actionable steps. |
| Pattern Guide | Markdown | A reference document mapping common failure patterns to technical causes. |
Net Detective Advanced Features
- Multi-provider DNS testing (Google, Cloudflare, and local) to bypass DNS hijacking or outages.
- Intelligent MTU detection to identify packet fragmentation issues.
- Automated anomaly detection when integrated with Openclaw Skills historical tracking.
- Configurable test execution to skip time-consuming tasks like traceroute or bandwidth measurement.
- Plain-English report generation designed specifically for non-technical end-users or AI-to-human communication.
SKILL.md
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