base44-troubleshooter
Troubleshoot production issues using backend function logs. Use when investigating app errors, debugging function calls, or diagnosing production problems in Base44 apps.
Works with
--- name: base44-troubleshooter description: Troubleshoot production issues using backend function logs. Use when investigating app errors, debugging function calls, or diagnosing production problems in Base44 apps. license: MIT --- # Troubleshoot Production Issues ## Prerequisites Verify authentication before fetching logs: ```bash npx base44 whoami ``` If not authenticated or token expired, instruct user to run `npx base44 login`. Resolve app context in one of these ways: ```bash # From a linked local project cat base44/.app.jsonc # Or explicitly npx base44 logs --app-id app_123 ``` ## Available Commands | Command | Description | Reference | |---------|-------------|-----------| | `base44 logs` | Fetch function logs for this app | [project-logs.md](references/project-logs.md) | ## Troubleshooting Flow ### 1. Check Recent Errors Start by pulling the latest errors across all functions: ```bash npx base44 logs --level error ``` ### 2. Drill Into a Specific Function If you know which function is failing: ```bash npx base44 logs --function <function_name> --level error ``` If you are outside the project directory, pass the app explicitly: ```bash npx base44 logs --app-id app_123 --function <function_name> --level error ``` ### 3. Inspect a Time Range Correlate with user-reported issue timestamps: ```bash npx base44 logs --function <function_name> --since <start_time> --until <end_time> ``` ### 4. Analyze the Logs - Look for stack traces and error messages in the output - Check timestamps to correlate with user-reported issues - Use `--limit` to fetch more entries if the default 50 isn't enough
More Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
