Diagnose AI Failures with Teamwork Debug Skill

Diagnose AI Failures with Teamwork Debug Skill

The Teamwork Debug Skill is designed to assist developers in diagnosing failures, regressions, crashes, flakes, or unexpected results within AI systems where the cause is unknown. It enables a systematic analysis approach, assisting in identifying and resolving issues through evidence-based methodologies and careful observation rather than guessing fixes.

What this skill does

• Reproduce or inspect the failure to determine the first bad behavior.
• Formulate hypotheses supported by evidence, using direct observations to isolate the cause if possible.
• Utilize the smallest observation necessary to distinguish between potential causes, employing structured logging for runtime uncertainties.
• Identify the first bad owned boundary and clearly state the supported cause.
• Upon receiving user authorization, implement and verify a minimal fix for the identified issue, ensuring temporary instrumentation is removed post-diagnosis.
• In cases of new evidence revealing different failures, explicitly split the diagnosis scope to prevent silent scope changes.

Who it is for

• Developers who utilize AI coding agents such as Claude Code, Cursor, or Codex.
• Teams involved in AI software development and maintenance requiring a systematic approach to diagnosing unexpected software behaviors.
• Technical leads or QA engineers focusing on improving system reliability through evidence-based diagnostics.

Use cases

• Diagnosing unexpected runtime behaviors in AI-driven applications where the cause is not immediately evident.
• Isolating the root cause of regressions or flaky tests in continuous integration pipelines.
• Supporting collaborative debugging efforts in team environments by providing a structured framework for issue resolution.

Technical details

• Compatible with agent skills for AI platforms such as Claude Code, Cursor, and Codex.
• Emphasizes evidence-based diagnostics with structured logging for runtime analysis.
• Supports probe-minimal diagnostics, where the smallest necessary observation is used to differentiate potential causes.

Source & Licence

This package is built on open-source work published by JinPLu (JinPLu/Teamwork) and distributed under MIT. The original licence text and copyright notice are included in your download.

Personal and commercial use, modification and redistribution are permitted, provided the original copyright and licence notice are retained.

Your purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.

Delivery & Support

• Delivery: instant — a secure download link is emailed to you as soon as payment is confirmed.
• Format: ZIP archive containing the skill files, documentation and the original licence.
• Support: support@mcpcart.com — we aim to reply within 2 business days.
• Updates: updates are included only where stated on this page.

Refunds

This is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.

Claude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.