Cross-DCC AI Skill: Error Reports & Diagnostics for Codex

Cross-DCC AI Skill: Error Reports & Diagnostics for Codex

The Cross-DCC AI Skill: Error Reports & Diagnostics for Codex provides developers and technical teams with essential tools for troubleshooting error conditions in Digital Content Creation (DCC) environments. It generates comprehensive error reports, captures diagnostic data, and monitors tool performance across various DCC applications such as Maya, Blender, Houdini, Unreal Engine, and standalone Python. This skill is an infrastructure component that assists in identifying the causes of tool failures.

What this skill does

• dcc_diagnostics__error_report: Initiate diagnostics with this tool. It provides a detailed bundle including log tails, failed job summaries, and process snapshots to assess error conditions.
• dcc_diagnostics__audit_log: Access audit logs to review sandbox-level denials and recent tool invocations for more context on issues.
• dcc_diagnostics__tool_metrics: Evaluate and diagnose tool performance, identifying any tools that are consistently slow or failing.
• dcc_diagnostics__screenshot: Capture the current visual state of the DCC application to assist with error confirmation and documentation.
• dcc_diagnostics__process_status: Monitor the health of the DCC process to determine if it remains operational during troubleshooting.

Who it is for

This skill is designed for developers and teams utilizing AI coding agents such as Claude Code, Cursor, and Codex who need to ensure robust debugging capabilities in their DCC workflows. It is particularly beneficial for technical artists, software engineers, and IT support staff involved in troubleshooting and maintaining DCC applications.

Use cases

• Troubleshooting vague or uninformative error messages in digital content creation tools.
• Improving diagnostic workflows in multi-application DCC environments.
• Documenting error conditions for later analysis or audit purposes.
• Monitoring process health and performance metrics across different DCC tools.

Technical details

• Developed with the aid of the `dcc-mcp-core` library, facilitating cross-DCC environment support.
• Operates independently of DCC-specific APIs, ensuring broad compatibility across multiple platforms and software versions.
• Integration-ready with AI coding environments, enhancing the troubleshooting capabilities of deployed agents.

Source & Licence

This package is built on open-source work published by dcc-mcp (dcc-mcp/dcc-mcp-core) 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.