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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 repository.

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Install

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Claude Code

Copy the skills folder into Claude Code’s skills directory: 
# 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

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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.

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Available skills

SkillWhen to use
atai-newton-fusion-modelCall 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.
atai-newton-omega-modelGet 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.
atai-newton-omega-model-data-prepClean, 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.

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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):
  • 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 — wildfire detection across 1,200+ ALERTCalifornia cameras with Newton vision (per-camera smoke/fire/haze + zone Q&A).
  • earthquake-demo — real-time USGS feed → text reasoning over structured seismic data (aftershock sequences, clustering, regional-risk ranking).
  • grid-demo — California power grid (CAISO supply/demand) → text reasoning on duck-curve dynamics, ramp, renewable share, and grid-stress risk.
  • 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, with atai-newton-omega-model-data-prep for the reference set):
  • 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 — 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 — 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.
If you build something with these skills, open a PR against agent-skills to share your patterns — the empirical gotchas in each SKILL.md came from real production runs and we’d love more.