> ## Documentation Index
> Fetch the complete documentation index at: https://docs.archetypeai.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Skills

> Use the official Archetype AI Agent Skills with your coding agent (Claude Code, Cursor, Codex, and others) to build on Newton faster.

## What are Agent Skills?

Agent Skills are packaged, reusable instructions that teach an AI coding agent how to handle a specific Newton task — for example, *"evaluate the assembly steps shown in this video,"* *"extract structured JSON from this dashboard screenshot,"* or *"embed these vibration windows and flag the anomalies."* Each skill is a folder containing a `SKILL.md` (plus optional runnable reference scripts and tests) that the agent loads on demand.

Skills give agents the institutional knowledge you'd otherwise have to repeat in every prompt: which endpoint to call, what the request body looks like, and the empirical gotchas we've already verified against production. They're the difference between *"explain the Direct Query API"* (you do the synthesis) and *"run this clip through Newton and tell me if the assembly passed"* (the agent already knows how).

The canonical source is the [archetypeai/agent-skills](https://github.com/archetypeai/agent-skills) repository.

## Install

### Claude Code

Copy the skills folder into Claude Code's skills directory:

```bash theme={"system"}
# Clone the repo
git clone https://github.com/archetypeai/agent-skills.git
cd agent-skills

# Install globally (available in every project)
cp -r skills/* ~/.claude/skills/

# Or install into a single project
cp -r skills/* /path/to/your-project/.claude/skills/
```

Then invoke any skill in Claude Code with a slash command:

```
/atai-newton-fusion-model
/atai-newton-omega-model
/atai-newton-omega-model-data-prep
```

### Other agents

The `SKILL.md` format is portable. The same files work with any agent runtime that loads skills from a directory — Cursor, Codex, Gemini CLI, Cline, and the broader skills ecosystem. See each agent's docs for the directory it reads from.

## Available skills

| Skill                                                                                                                                 | When to use                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
| ------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`atai-newton-fusion-model`](https://github.com/archetypeai/agent-skills/tree/main/skills/atai-newton-fusion-model)                   | Call the Newton C 2.6 fusion model on `/query` with text, image, or video in one stateless POST — no session lifecycle, no batch job, no SSE. Covers the request shape per modality (including both video paths: `.mp4` + `max_frames`, and client-sampled frames + `query_metadata`), multi-image mode, generation parameters, JSON-output prompting, and the verified gotchas. Built on the [official python client](https://github.com/archetypeai/python-client). |
| [`atai-newton-omega-model`](https://github.com/archetypeai/agent-skills/tree/main/skills/atai-newton-omega-model)                     | Get time-series embeddings from the Omega encoder (`OmegaEncoder::omega_embeddings_1_4`) on `/query` — send a window of sensor readings, get back a 768-d vector per channel (one call per channel, fanned out in parallel). Covers the request/response shape, the 16–1024 window-length range, `normalize_input`, and a client-side n-shot KNN classification example. Built on the [official python client](https://github.com/archetypeai/python-client).         |
| [`atai-newton-omega-model-data-prep`](https://github.com/archetypeai/agent-skills/tree/main/skills/atai-newton-omega-model-data-prep) | Clean, split, and featurize multivariate time-series before the Omega model — gap-aware blocking + imputation, an out-of-time train/test split, and joint-state (X, y) featurization. Use to build a leakage-free n-shot reference set for `atai-newton-omega-model`.                                                                                                                                                                                                 |

More skills are going through review and will be added to this table as they land.

## Example projects

These public repos demonstrate the patterns covered by the skills above — clone them as starting templates:

**Vision & text reasoning** ([`atai-newton-fusion-model`](https://github.com/archetypeai/agent-skills/tree/main/skills/atai-newton-fusion-model)):

* [traffic-demo](https://github.com/archetypeai/archetypeai-traffic-demo) — live Caltrans CCTV traffic analysis; samples a burst of frames per interval and sends them as one multi-frame `/query` clip to reason over flow, incidents, and conditions.
* [wildfire-demo](https://github.com/archetypeai/archetypeai-wildfire-demo) — wildfire detection across 1,200+ ALERTCalifornia cameras with Newton vision (per-camera smoke/fire/haze + zone Q\&A).
* [earthquake-demo](https://github.com/archetypeai/archetypeai-earthquake-demo) — real-time USGS feed → text reasoning over structured seismic data (aftershock sequences, clustering, regional-risk ranking).
* [grid-demo](https://github.com/archetypeai/archetypeai-grid-demo) — California power grid (CAISO supply/demand) → text reasoning on duck-curve dynamics, ramp, renewable share, and grid-stress risk.
* [wifi-demo](https://github.com/archetypeai/archetypeai-wifi-demo) — "is anyone home?" occupancy inference from residential WiFi device-telemetry (GHOST-IoT) — per-window verdict + per-device classification shown against ground truth.

**Time-series embeddings + local KNN** ([`atai-newton-omega-model`](https://github.com/archetypeai/agent-skills/tree/main/skills/atai-newton-omega-model), with [`atai-newton-omega-model-data-prep`](https://github.com/archetypeai/agent-skills/tree/main/skills/atai-newton-omega-model-data-prep) for the reference set):

* [swat-demo-direct-query](https://github.com/archetypeai/archetypeai-swat-demo-direct-query) — 6-stage water-treatment-plant anomaly dashboard: per-channel Omega embeddings + client-side KNN, with PCA-2/UMAP-2 embedding views and per-stage accuracy badges (plus `/query` text reasoning for operator suggestions).
* [wind-turbine-demo](https://github.com/archetypeai/archetypeai-wind-turbine-demo) — Penmanshiel wind-farm SCADA: Omega embeddings + local KNN against a leakage-free n-shot library precomputed offline, detecting a real frequency-converter fault against a healthy peer turbine.
* [drilling-demo](https://github.com/archetypeai/archetypeai-drilling-demo) — drilling state classification from 14 North Sea (Volve) wells: Omega embeddings + local KNN against a leakage-free n-shot library built from held-out reference wells, with live accuracy vs ACTC ground truth.

<Tip>
  If you build something with these skills, open a PR against [agent-skills](https://github.com/archetypeai/agent-skills) to share your patterns — the empirical gotchas in each `SKILL.md` came from real production runs and we'd love more.
</Tip>