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 agentmods add commands/voxel51/fiftyone-skills/quickstartgit 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/commands/voxel51/fiftyone-skills/quickstart)<a href="https://agentmods.dev/commands/voxel51/fiftyone-skills/quickstart"><img src="https://agentmods.dev/badge/commands/voxel51/fiftyone-skills/quickstart.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00029 | $0.00709 |
| Opus 5 | $0.00015 | $0.00354 |
| Sonnet 5 | $0.00006 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
quickstart 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 6d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FiftyOne Quickstart
Welcome to FiftyOne! This quickstart will guide you through your first workflow.
Prerequisites Check
First, let me verify your setup by listing available datasets:
Use the list_datasets MCP tool to check the connection.
If this fails, run:
pip install fiftyone-mcp-server
Then check /fiftyone:help for environment configuration.
Choose Your Path
Option 1: User Quickstart
Build and explore a computer vision dataset.
Step 1: Load a Dataset
You can either:
-
Use the quickstart dataset (built-in):
Load the FiftyOne quickstart dataset -
Import your own dataset:
Use /fiftyone:fiftyone-dataset-import to import my dataset from /path/to/data -
Import from Hugging Face Hub:
Use /fiftyone:fiftyone-dataset-import to import the dataset from huggingface.co/datasets/username/dataset-name
Step 2: Run Model Inference
Apply a model to your dataset:
Use /fiftyone:fiftyone-dataset-inference to run object detection on my dataset
This will run a Zoo model (like YOLO or Faster R-CNN) and add predictions to your samples.
Step 3: Explore in the App
Launch the FiftyOne App to visualize your data and predictions:
Launch the FiftyOne App with my dataset
From here you can:
- Browse samples and labels
- Filter by predictions or ground truth
- Identify model errors
Next Steps
Once you're comfortable, try:
/fiftyone:fiftyone-find-duplicates- Clean your dataset/fiftyone:fiftyone-model-evaluation- Evaluate prediction quality/fiftyone:fiftyone-embeddings-visualization- Explore data distribution/fiftyone:fiftyone-create-notebook- Generate a full ML pipeline notebook
Option 2: Developer Quickstart
Create a custom FiftyOne plugin.
Step 1: Start Plugin Development
Use /fiftyone:fiftyone-develop-plugin to create a new plugin
The skill will guide you through:
- Defining your plugin's purpose
- Choosing between operators (actions) or panels (UI)
- Generating the plugin structure
- Installing and testing locally
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.
- 6d ago First seen · 121 lines · 29 tokens per session scan A 4aa66dd85038
quickstart is a command published in the GitHub repository voxel51/fiftyone-skills (39 stars, last pushed 20d ago), licensed Apache-2.0. It adds 29 tokens to every session and 709 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.