Claude Code Workflow: Building a Custom Wi-Fi Diagnostic Suite to Resolve Persistent 5 GHz Stalling

Workflow value: 90/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: advanced
Categories: Quality Control, Context & Memory, Debugging, CLAUDE.md
Original source: r/ClaudeAI post/comment

What problem this solves

Diagnosing and resolving persistent 5 GHz Wi-Fi stalling issues, specifically identifying a problematic channel in a specific location due to non-Wi-Fi interference and optimizing network configuration.

Summary

A user leveraged Claude Code to develop a custom, multi-component Wi-Fi diagnostic suite (iOS app, iOS Shortcut, Python server helper, Python analyzer) to pinpoint the root cause of persistent 5 GHz Wi-Fi stalling. The process involved iterative development, AI-driven code review, simulated fault testing, and API-driven network configuration verification, leading to the identification of a specific problematic channel and an optimized channel plan.

Why it is useful

This workflow is highly valuable as it demonstrates Claude Code's advanced capabilities in assisting with complex, multi-disciplinary problem-solving. It showcases how Claude Code can be used to design, build, test, and iterate on custom diagnostic tools across different platforms (iOS, Python server, network API) to solve a real-world, long-standing technical challenge that would be extremely difficult to resolve manually. The emphasis on AI-driven code review, simulation, and verified network changes highlights a robust and effective methodology for debugging complex systems, making it a powerful example for advanced users.

Workflow

1. Define the problem: Identify persistent Wi-Fi stalling on 5 GHz, especially in specific areas, despite good signal strength.
2. Collaborate with Claude Code to build custom diagnostic tools: Develop an iPhone app for labeling rooms/spots, switching test SSIDs (2.4, 5, dual), and measuring loss, latency, stalls, DNS, and speed, including a soak screen. Create an iOS Shortcut to grab RSSI, noise, and channel data. Build a Python helper on a server for UDP echo, speed test, and read-only polling of the Omada API for AP stats. Develop a Python analyzer to merge phone and AP data and flag likely causes.
3. Perform AI-driven quality control: Use Claude Code for independent review sessions to catch bugs in the developed tools. Run the app in a simulator against various fault scenarios (e.g., loss, stalls, server down) to find defects.
4. Conduct iterative network testing and configuration: Perform measurements across the house, focusing on problem areas (e.g., a specific couch). Analyze merged data to identify specific issues (e.g., high loss on 5 GHz, specific channel interference). Propose network changes (e.g., channel plan adjustments). Implement changes in the Omada controller. Verify changes through the Omada API and log them in a changelog with rollback information.
5. Validate the fix: Conduct "dress rehearsal" tests with the new configuration (e.g., a long soak test at the previously problematic spot, a walk-through with roaming) to confirm resolution of stalling and optimal performance.

Tools / artifacts

• Claude Code
• Custom iPhone app (built with Claude Code)
• Custom iOS Shortcut (built with Claude Code)
• Custom Python helper script (built with Claude Code)
• Custom Python analyzer script (built with Claude Code)
• Omada network setup (APs, controller)
• Omada API (viewer account)
• Xcode (for app installation)
• AirPort Utility (for channel scanning)
• Temporary test SSIDs (2.4 only, 5 only, dual)
• Changelog for network changes

Validation signals

• AI-driven reviews caught real bugs before physical testing.
• Simulator tests against 10 fault scenarios found a real defect.
• Identified specific problem: 92-98% loss on 5 GHz at one couch, 0 upload bytes from phone to AP.
• Confirmed channel-specific issue: moving test SSID off channel 149 resulted in 0% loss on another AP/channel.
• Confirmed not AP-specific: moving the same AP to a different channel (44) resulted in 0% loss at the same spot.
• Identified Omada's nightly WLAN Optimization setting wide channels and parking APs on problematic channels.
• Successful "dress rehearsal" with new channel plan: 29 min at couch with 0 stalls, 6 roams with no stalls during house walk-through.
• Resolved a 10-year frustrating issue across multiple AP brands.

Limitations

• Requires significant technical expertise in iOS development, Python, server setup, and network configuration.
• Specific tools are tied to iOS and Omada, requiring adaptation for other ecosystems.
• The actual code for the custom tools is not provided, requiring users to recreate them with Claude Code.
• Claude Code's directions for Omada UI sometimes required further clarification from the user.

Rate this workflow

Upvote this post if the workflow is useful, reproducible, or worth recommending.

Downvote if it is vague, outdated, unsafe, overhyped, or not reproducible.

Reply if it worked for you, failed, is outdated, or has a better alternative.

This post was generated automatically from the workflow library database.