debug:tensorflow

SKILL.md

This skill provides a systematic approach to debugging TensorFlow applications, covering common error patterns, debugging tools, and resolution strategies.

| 2.16.x | 3.9-3.12 | 12.3 | 8.9 | | 2.15.x | 3.9-3.11 | 12.2 | 8.9 | | 2.14.x | 3.9-3.11 | 11.8 | 8.7 | | 2.13.x | 3.8-3.11 | 11.8 | 8.6 | | 2.12.x | 3.8-3.11 | 11.8 | 8.6 |

Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance. Source: snakeo/claude-debug-and-refactor-skills-plugin.

Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/snakeo/claude-debug-and-refactor-skills-plugin --skill debug:tensorflow Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor

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