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Nemo Evaluator Sdk

Listed
davila7·vlatest·backend·MIT

The Nemo Evaluator SDK allows developers to quickly assess and compare generated content using NVIDIA's powerful language models, streamlining 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 davila7/claude-code-templates --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

davila7

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

    • cli-tool/components/skills/ai-research/evaluation-nemo-evaluator/references/execution-backends.md:113SSH private keys or config
      ssh_key_path: ~/.ssh/id_rsa
    • cli-tool/components/skills/ai-research/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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/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

The Nemo Evaluator SDK provides a specialized framework for assessing AI model performance using the Nemo evaluation suite, enabling developers to benchmark accuracy and reliability across diverse tasks. It integrates seamlessly with existing workflows to generate structured reports on model behavior without requiring manual test case creation.

When to use it

  • Validating the output quality of generative models before deploying them to production environments.
  • Comparing performance metrics between different AI agents or fine-tuned versions of a base model.
  • Automating regression testing to ensure new model updates do not degrade previous capabilities.
  • Generating standardized compliance reports for regulated industries requiring strict adherence to evaluation benchmarks.

Key capabilities

  • Execution of the Nemo evaluation suite for comprehensive model assessment.
  • Automated generation of performance metrics and accuracy scores.
  • Structured reporting formats suitable for integration into CI/CD pipelines.
  • Support for diverse task types including text generation, classification, and reasoning.

Example prompts

  • "Run a full Nemo evaluation suite on my latest model checkpoint and summarize the key accuracy metrics."
  • "Compare the performance of Model A and Model B using the Nemo evaluator SDK and highlight areas where Model A underperforms."
  • "Generate a detailed regression report showing how recent code changes have impacted the model's reasoning capabilities based on Nemo benchmarks."

Tips & gotchas

Ensure your environment has the necessary dependencies installed to run the full Nemo suite, as missing libraries can lead to incomplete evaluation results. Be aware that evaluation time may increase significantly with larger datasets, so consider sampling strategies for rapid iteration cycles.