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.
npx skills add voxel51/fiftyone-skills --skill fiftyone-dataset-importgit clone --depth 1 https://github.com/voxel51/fiftyone-skillsWrote 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.
[](https://agentmods.dev/skills/voxel51/fiftyone-skills/fiftyone-dataset-import)<a href="https://agentmods.dev/skills/voxel51/fiftyone-skills/fiftyone-dataset-import"><img src="https://agentmods.dev/badge/skills/voxel51/fiftyone-skills/fiftyone-dataset-import/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.
<a href="https://agentmods.dev/skills/voxel51/fiftyone-skills/fiftyone-dataset-import"><img src="https://agentmods.dev/badge/skills/voxel51/fiftyone-skills/fiftyone-dataset-import.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 21 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.07721 |
| Opus 5 | $0.00048 | $0.03861 |
| Sonnet 5 | $0.00019 | $0.01544 |
| Haiku 4.5 | $0.00010 | $0.00772 |
Grade A, and why
fiftyone-dataset-import 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 750 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Universal Dataset Import for FiftyOne
Key Directives
ALWAYS follow these rules:
1. Scan folder FIRST
Before any import, deeply scan the directory to understand its structure:
# Use bash to explore
find /path/to/data -type f | head -50
ls -la /path/to/data
2. Auto-detect everything
Detect media types, label formats, and grouping patterns automatically. Never ask the user to specify format if it can be inferred.
3. Detect multimodal groups
Look for patterns that indicate grouped data:
- Scene folders containing multiple media files
- Filename patterns with common prefixes (e.g.,
scene_001_left.jpg,scene_001_right.jpg) - Mixed media types that should be grouped (images + point clouds)
4. Detect and install required packages
Named autonomous-driving devkit formats (PandaSet, nuScenes, Waymo Open, Argoverse, KITTI 3D,
Lyft L5, A2D2) need an external Python package. Check with pip show <package>, ask the user
before installing, then verify with a smoke-test import. Full package table, directory-pattern
detection, and the complete conversion workflow are in
SPECIALIZED-3D-FORMATS.md.
No package is required to import or render MCAP (.mcap/.bag/.rrd) files. The file
extension alone sets media_type == "multimodal", and channel decoding and rendering happen
client-side in the FiftyOne App. Do not tell users to pip install mcap for this. The only
optional use for a Python-side package is reading channel schemas before opening the App, covered
in Step 9D below.
Additional packages for 3D processing: open3d (PCD conversion), pyntcloud, laspy
(LAS/LAZ). For Hugging Face Hub: huggingface_hub, pyarrow, Pillow.
5. Confirm before importing
Present findings to user and explicitly ask for confirmation before creating the dataset. Always end your scan summary with a clear question like:
- "Proceed with import?"
- "Should I create the dataset with these settings?"
Wait for user response before proceeding. Do not create the dataset until the user confirms.
What ships with it
7 files 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.
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.
- 2d ago Changed · -363 lines · +19 tokens per session 6d1ece259c5b
- 12d ago First seen · 1,113 lines · 77 tokens per session scan A 2a73a8dc3c19
fiftyone-dataset-import is a skill published in the GitHub repository voxel51/fiftyone-skills (39 stars, last pushed today), licensed Apache-2.0. It adds 96 tokens to every session and 7,721 once invoked, about $0.0005 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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