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Nemo Evaluator Sdk

Listed
zechenzhangagi·vlatest·backend·MIT

The Nemo Evaluator SDK allows developers to quickly assess and compare model performance using Nemo, streamlining AI development workflows.

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 orchestra-research/ai-research-skills --skill nemo-evaluator-sdk -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

zechenzhangagi

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No credential access: failed

    Touches credential material in 2 places.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

    What it found

    • 11-evaluation/nemo-evaluator/references/execution-backends.md:113SSH private keys or config
      ssh_key_path: ~/.ssh/id_rsa
    • 11-evaluation/nemo-evaluator/references/execution-backends.md:336SSH private keys or config
      chmod 600 ~/.ssh/id_rsa

  • 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

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned zechenzhangagi/ai-research-skills at 773a52944b.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
773a52944b
Manifest
11-evaluation/nemo-evaluator/SKILL.md
Scanned
5 files, 1,762 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.
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About this skill

What it does

The zechenzhangagi-nemo-evaluator-sdk skill provides tools for evaluating and analyzing AI models, specifically within the NVIDIA Nemo framework. It allows users to run evaluation pipelines, generate reports on model performance, and compare different model configurations. This SDK streamlines the process of assessing AI models' accuracy, efficiency, and overall quality.

When to use it

  • Model Development: Evaluate newly trained or fine-tuned models to ensure they meet desired performance benchmarks before deployment.
  • A/B Testing: Compare multiple model versions to determine which performs best on a given task.
  • Performance Monitoring: Regularly assess deployed models for degradation in accuracy or efficiency over time.
  • Research & Experimentation: Analyze the impact of different training techniques or architectural changes on model performance.

Key capabilities

  • Evaluation pipeline execution
  • Report generation
  • Model comparison functionality
  • Integration with NVIDIA Nemo framework

Example prompts

  • "Run the standard evaluation pipeline for this speech recognition model."
  • "Generate a report comparing the accuracy of models A and B on the validation dataset."
  • "Evaluate this new text-to-speech model against the baseline model."

Tips & gotchas

  • Requires familiarity with the NVIDIA Nemo framework.
  • Ensure you have the necessary dependencies installed for proper execution.