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-inferencegit 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-inference)<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.
<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>- NVIDIA SkillSpector warn
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.
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 | $0.00054 | $0.01958 |
| Opus 5 | $0.00027 | $0.00979 |
| Sonnet 5 | $0.00011 | $0.00392 |
| Haiku 4.5 | $0.00005 | $0.00196 |
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.
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={...})
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.
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.
- 12d ago First seen · 312 lines · 54 tokens per session scan A ba8dbc1ccecf
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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