debug:tensorflow
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.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install snakeo-claude-debug-and-refactor-skills-plugin-debug-tensorflow
Repository
Skill path: plugins/debug-and-refactor/skills/debug-tensorflow
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.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI, Testing.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: snakeo.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install debug:tensorflow into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/snakeo/claude-debug-and-refactor-skills-plugin before adding debug:tensorflow to shared team environments
- Use debug:tensorflow for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.