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Fiftyone Model Evaluation

Listed
voxel51·vlatest·ai-ml·Apache-2.0

Evaluate object detection models using fiftyone's interactive visual review and quantitative metrics for rapid iteration.

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 voxel51/fiftyone-skills --skill fiftyone-model-evaluation -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

voxel51

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 pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

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

    What it found

    • skills/fiftyone-model-evaluation/README.md:8remote script piped into a shell
      curl -sL skil.sh | sh -s -- voxel51/fiftyone-skills

  • 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 credential access: passed

    Reads no keys, tokens, keychains or credential files.

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

  • Pinned to a commit: passed

    Scanned voxel51/fiftyone-skills at e28a9a3d45.

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

Commit
e28a9a3d45
Manifest
skills/fiftyone-model-evaluation/SKILL.md
Scanned
2 files, 612 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

The fiftyone-model-evaluation skill allows AI agents to evaluate machine learning models using the Fifty One platform. It facilitates interactive model review, annotation, and performance analysis directly within a visual interface. This enables users to gain deeper insights into model behavior and identify areas for improvement through detailed examination of predictions on datasets.

When to use it

  • Debugging Model Performance: Investigate why a model is performing poorly in specific scenarios by visually inspecting its outputs.
  • Dataset Bias Detection: Identify potential biases within your training data that might be influencing model predictions.
  • Annotation Quality Assessment: Evaluate the quality of annotations used for training and refine annotation guidelines.
  • Model Comparison: Compare the performance of different models side-by-side using a consistent visual evaluation framework.

Key capabilities

  • Interactive Model Review
  • Visual Annotation Tools
  • Performance Analysis
  • Dataset Exploration
  • Integration with Fifty One platform

Example prompts

  • "Evaluate my object detection model on the 'traffic_data' dataset."
  • "Show me instances where my segmentation model has low confidence scores."
  • "Compare the performance of Model A and Model B on the validation set."

Tips & gotchas

  • Requires a working installation of Fifty One.
  • Familiarity with the Fifty One platform is recommended to effectively utilize this skill’s visual interface.