Summary
When your experiment code breaks, this walks through a structured debugging process: categorize the error (syntax vs runtime vs logic), analyze the root cause from tracebacks, apply targeted fixes with retry logic, then reflect on what went wrong to prevent it next time. It's built around a state machine that escalates from minimal edits to full rewrites after repeated failures. The workflow handles common ML debugging scenarios like device mismatches, shape errors, and the dreaded 0% accuracy bug. Honestly, the real value is in forcing you to slow down and categorize instead of randomly tweaking code until something works.
Install to Claude Code
npx -y skills add lingzhi227/agent-research-skills --skill code-debugging --agent claude-codeInstalls into .claude/skills of the current project.
Plug Mailtrap into your AI workflow and let it handle the email.
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Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
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Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
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one prompt install →Files
SKILL.md
Select a file.
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Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
