fiftyone-dataset-inference

fiftyone-dataset-inference is a skill for Claude Code from voxel51/fiftyone-skills. It costs 54 tokens per session (1,958 once invoked), scanned A, original, Apache-2.0.

A workflow for running machine-learning models on datasets managed by FiftyOne, a tool for viewing and organising computer-vision data. It supports detection, classification, segmentation, and embedding tasks, and finds models from available sources.

In plain words
What is it for?
Use it to apply a suitable model to a FiftyOne dataset, inspect the dataset first, save prediction results in named fields, and close the FiftyOne app afterward.
Why use it?
It provides a defined process for checking that a dataset exists, choosing where predictions should be stored, and reviewing the data before running inference. Inference means using a trained model to produce predictions from data.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the fiftyone plugin — 18 skills, 2 commands, 1 MCP server shipped together

Good fit Use it to apply a suitable model to a FiftyOne dataset, inspect the dataset first, save prediction results in named fields, and close the FiftyOne app afterward.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/voxel51/fiftyone-skills/fiftyone-dataset-inference
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add voxel51/fiftyone-skills --skill fiftyone-dataset-inference
Clone the repo
git clone --depth 1 https://github.com/voxel51/fiftyone-skills

Made for: Claude Code.

Or install fiftyone, the plugin that ships this one along with the rest of its 18 skills, 2 commands, 1 MCP server.

Its marketplace also offers this one on its own, as the plugin fiftyone-dataset-inference/plugin install fiftyone-dataset-inference after adding the marketplace above.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for fiftyone-dataset-inference

README.md
[![agentmods](https://agentmods.dev/badge/skills/voxel51/fiftyone-skills/fiftyone-dataset-inference/github.svg)](https://agentmods.dev/skills/voxel51/fiftyone-skills/fiftyone-dataset-inference)
Your own site
<a href="https://agentmods.dev/skills/voxel51/fiftyone-skills/fiftyone-dataset-inference"><img src="https://agentmods.dev/badge/skills/voxel51/fiftyone-skills/fiftyone-dataset-inference/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for fiftyone-dataset-inference

Your own site · 80×15
<a href="https://agentmods.dev/skills/voxel51/fiftyone-skills/fiftyone-dataset-inference"><img src="https://agentmods.dev/badge/skills/voxel51/fiftyone-skills/fiftyone-dataset-inference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,958 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high YARA Match · line 3
    YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).
    Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.01958
Opus 5 $0.00027 $0.00979
Sonnet 5 $0.00011 $0.00392
Haiku 4.5 $0.00005 $0.00196

Measured 12d ago against content hash ba8dbc1ccecf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

fiftyone-dataset-inference scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/fiftyone-dataset-inference/SKILL.md · 312 lines

How it starts

The opening of the file, as written. The whole thing — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run Model Inference on FiftyOne Datasets

Key Directives

ALWAYS follow these rules:

1. Check if dataset exists first

list_datasets()

If the dataset doesn't exist, use the fiftyone-dataset-import skill to load it first.

2. Set context before operations

set_context(dataset_name="my-dataset")

3. Launch App for inference

The App must be running to execute inference operators:

launch_app(dataset_name="my-dataset")

4. Ask user for field names

Always confirm with the user:

  • Which model to use
  • Label field name for predictions (e.g., predictions, detections, embeddings)

5. Close app when done

close_app()

Workflow

Step 1: Verify Dataset Exists

list_datasets()

If the dataset is not in the list:

  • Ask the user for the data location
  • Use the fiftyone-dataset-import skill to import the data first
  • Return to this workflow after import completes

Step 2: Load Dataset and Review

set_context(dataset_name="my-dataset")
dataset_summary(name="my-dataset")

Review:

  • Sample count
  • Media type
  • Existing label fields

Step 3: Launch App

launch_app(dataset_name="my-dataset")

Step 4: Discover and Apply Model

Ask the user about the task, model, or type of data they're using (detection, classification, segmentation, embeddings, or a specific model name); note users may give a 'tool name' (see Path B). Then determine the path:

Path A — Zoo model (most common)

ALWAYS first fetch the live model list — never assume what's available:

get_operator_schema(operator_uri="@voxel51/zoo/apply_zoo_model")

Pick the right model from the schema's model enum, then apply:

execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "<model-name-from-schema>",
        "label_field": "predictions"
    }
)

Path B — Plugin operator

If the user mentions a specific tool (e.g. CLIP similarity, SAM, a third-party model), check installed operators first:

list_operators(builtin_only=False)

Find the matching operator, inspect its schema, then execute it:

get_operator_schema(operator_uri="@org/plugin/operator")
execute_operator(operator_uri="@org/plugin/operator", params={...})

Read the full file on GitHub · 312 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 312 lines · 54 tokens per session scan A ba8dbc1ccecf

Subscribe to this mod's changes

fiftyone-dataset-inference is a skill published in the GitHub repository voxel51/fiftyone-skills (39 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 1,958 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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