debug

Solid

Systematic debugging via logs, health checks, hypothesis-driven investigation. Triggers: debug, error, trace root cause, fix bug, reproduce symptom, investigation.

AI & Automation 155 stars 19 forks Updated 2 days ago MIT

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Quality Score: 93/100

Stars 20%
73
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Debug Helper $ARGUMENTS Systematic debugging for application issues. ## Project context - Recent logs: !`docker compose logs --tail 20 2>/dev/null || tail -20 logs/*.log 2>/dev/null || echo "no-logs-found"` ## Automated Error Parsing Pipe error output through the error parser for structured diagnosis: ```bash # Pipe from failing command your_command 2>&1 | python3 "$(dirname "$0")/scripts/error-parser.py" # Or from a log file cat /var/log/app/error.log | python3 scripts/error-parser.py ``` The script outputs JSON with: - **language**: detected language (python/node/go/php) - **error_type**: extracted error class (e.g., ModuleNotFoundError) - **message**: the error message text - **category**: classification (import, reference, type, connection, timeout, memory, permission, syntax) - **stack_frames**: parsed file/line/function from the stack trace - **files_to_check**: unique files from the trace, ordered by relevance - **common_causes**: likely root causes for this error category Use the parsed output to focus investigation on the right files and hypotheses. --- ## Methodology — The Iron Law ``` NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST ``` Random fixes waste time and create new bugs. Quick patches mask underlying issues. Complete each phase before proceeding to the next. ### Phase 1 — Root Cause Investigation Read error messages and stack traces completely. Reproduce reliably (or gather more data — don't guess). Check recent changes (`git diff`, new de...

Details

Author
softspark
Repository
softspark/ai-toolkit
Created
2 months ago
Last Updated
2 days ago
Language
Python
License
MIT

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