fiftyone-dataset-import
Import Vision Datasets into FiftyOne
Dataset imports often fail when media, annotations, and multimodal folders use different formats. This skill guides scanning, detection, package setup, import, grouping, and validation in FiftyOne.
Stop for confirmation before installing.
Review the plan and obtain explicit user consent before changing files.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "fiftyone-dataset-import" from https://skillstore.io/skills/adonaivera-fiftyone-dataset-import.md and its manifest at https://skillstore.io/api/skills/adonaivera-fiftyone-dataset-import/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "fiftyone-dataset-import". Import a COCO image dataset from my local folder.
Expected outcome:
- Scan summary lists image count, annotation file, and detected COCO structure.
- Proposed import settings include dataset name, label field, and source paths.
- The assistant asks for confirmation before creating the dataset.
Using "fiftyone-dataset-import". Load an autonomous driving dataset with cameras and LiDAR.
Expected outcome:
- Detected slices include front camera, side cameras, and LiDAR.
- Required packages and point cloud conversion steps are listed.
- Validation compares expected groups, samples, and label fields.
Using "fiftyone-dataset-import". Troubleshoot a failed FiftyOne import.
Expected outcome:
- The response checks paths, supported extensions, label format, and existing dataset names.
- It suggests a smaller test import before retrying the full dataset.
Security Audit
High RiskMost static hits are Markdown backticks, code fences, examples, or benign FiftyOne references. Confirmed issues are limited to package installation guidance and Python import checks that can execute third-party code. A high-risk semantic issue remains because the skill recommends pickle.load on dataset files.
Confirmed security concerns (1)
Capability review items (18)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Risk Factors
โ๏ธ External commands (155)
๐ Network access (13)
๐ Filesystem access (1)
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<iframe src="https://skillstore.io/embed/skills/adonaivera-fiftyone-dataset-import.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA ยท BibTeX ยท CFF)
APA citation
AdonaiVera. (2026). fiftyone-dataset-import security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/adonaivera-fiftyone-dataset-import/audits/8BibTeX citation
@techreport{adonaivera-adonaivera-fiftyone-dataset-import-2026,
author = {AdonaiVera},
title = {fiftyone-dataset-import security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/adonaivera-fiftyone-dataset-import/audits/8},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "fiftyone-dataset-import security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
- name: "AdonaiVera"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/adonaivera-fiftyone-dataset-import/audits/8"
identifiers:
- type: other
value: "skillstore:adonaivera-fiftyone-dataset-import:audit:8"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: MediumWhat You Can Build
Import Standard Detection Datasets
Load image datasets with COCO, YOLO, VOC, CVAT, or KITTI labels into a new FiftyOne dataset.
Create Multimodal Driving Datasets
Group camera images, LiDAR point clouds, and labels by scene for autonomous driving review.
Validate Dataset Migration Results
Compare imported sample counts, labels, groups, and slices against the source files before analysis.
Try These Prompts
Scan my image dataset at [path], detect labels, propose a FiftyOne import plan, and wait for my confirmation.
Inspect [path], identify the media types and annotation format, then recommend the safest FiftyOne import settings.
Analyze [path] for camera, LiDAR, and scene grouping patterns. Propose group slices and a validation checklist.
Build a FiftyOne import plan for [dataset name] at [path], including required packages, conversion steps, and label mapping.
Best Practices
- Scan the folder and present findings before creating a dataset.
- Ask for confirmation before installing packages or overwriting datasets.
- Validate sample counts, labels, and groups after every import.
Avoid
- Do not import before confirming detected formats with the user.
- Do not flatten multimodal scenes that should stay grouped.
- Do not load untrusted pickle files without a safe review process.
Frequently Asked Questions
Does this skill require FiftyOne?
Can it import YOLO and COCO datasets?
Can it handle point clouds?
Will it install packages automatically?
Can it overwrite existing datasets?
Is it safe for untrusted annotation files?
Developer Details
Author
AdonaiVeraLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
6eec4a59f63ff83440a84c18d1aef79743bc714e
Maintenance freshness
7/18/2026
Usage
6 downloads ยท 378 views
File structure
๐ SKILL.md