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Debug:Tensorflow

Checked
snakeo·vlatest·ai-ml

Identifies and suggests fixes for TensorFlow code errors, leveraging snakeo's expertise in debugging deep learning models.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add snakeo/claude-debug-and-refactor-skills-plugin --skill debug:tensorflow -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

snakeo

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

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  • Pinned to a commit: passed

    Scanned snakeo/claude-debug-and-refactor-skills-plugin at e2e79df88e.

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Commit
e2e79df88e
Manifest
plugins/debug-and-refactor/skills/debug-tensorflow/SKILL.md
Scanned
1 file, 633 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill assists in debugging TensorFlow code. It can analyze error messages, identify potential causes of issues within TensorFlow models and training loops, and suggest corrections or alternative approaches to resolve errors. The tool aims to streamline the debugging process for developers working with TensorFlow.

When to use it

  • You're encountering cryptic error messages during TensorFlow model training.
  • Your TensorFlow code is producing unexpected results or NaN values.
  • You need help understanding a complex TensorFlow stack trace.
  • You are unsure how to resolve an issue related to tensor shapes or data types in your TensorFlow graph.

Key capabilities

  • Error message analysis
  • Root cause identification
  • Suggestion of corrections
  • TensorFlow model debugging

Example prompts

  • "I'm getting a 'ValueError: Shapes (...,) and (...) are incompatible' error in my TensorFlow training loop. Can you help me debug it?"
  • "My TensorFlow model is producing NaN values during training. What could be the cause?"
  • "Can you analyze this TensorFlow stack trace and tell me what might be going wrong?"

Tips & gotchas

The skill requires providing relevant code snippets or error messages for accurate analysis. It's most effective when dealing with specific, well-defined TensorFlow debugging challenges.