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newton-machine-state-from-file

Stream time-series CSV data to Archetype AI's Machine State Lens for n-shot state classification.

Updated Apr 9, 2026
Search Tags:#streaming#csv#anomaly-detection#lens#timeseries#machine-state
One-click install
npx skills add https://github.com/archetypeai/newton-earthquake-demo --skill newton-machine-state-from-file
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: newton-machine-state-from-file
Source: https://github.com/archetypeai/newton-earthquake-demo/tree/main/.claude/skills/newton-machine-state-from-file
Command: npx skills add https://github.com/archetypeai/newton-earthquake-demo --skill newton-machine-state-from-file

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streaming time-series CSV data into Archetype AI's Machine State Lens enables automated state classification and anomaly detection on time-series datasets without manual data wrangling.

Core Features & Use Cases

  • Stream time-series CSV data into the Archetype AI Machine State Lens for real-time state classification.
  • Support both Python and JavaScript/Web implementations for flexible deployment.
  • Perform n-shot state recognition from files to quickly compare healthy vs anomalous sequences.

Quick Start

Upload a CSV file containing time-series data and run the machine-state lens to stream data and classify states.

Frequently Asked Questions about newton-machine-state-from-file

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I stream CSV time-series data for real-time machine state classification?▼

Streaming CSV time-series data for machine state classification requires an API client to upload files, configure lens parameters, stream in windows, and consume SSE results. This enables automated n-shot state recognition without manual data wrangling.

What is n-shot state recognition from time-series files?▼

N-shot state recognition from time-series files compares healthy versus anomalous sequences by streaming CSV sensor data into a machine state lens. This approach classifies different operational states automatically to detect anomalies in industrial equipment.

Can I use Python or JavaScript to stream time-series CSV files for anomaly detection?▼

Yes, both Python and JavaScript implementations support streaming time-series CSV files for anomaly detection. They utilize an API client to upload CSVs, configure lens parameters, stream data in windows, and consume Server-Sent Events (SSE) results.

How do I detect industrial sensor anomalies using time-series CSV data?▼

Detect industrial sensor anomalies by streaming time-series CSV data into a machine state lens for n-shot state classification. The lens compares real-time streaming windows against known states to identify operational deviations indicating equipment anomalies.

Does streaming time-series data for anomaly detection require an API client?▼

Streaming time-series data for anomaly detection requires an API client to upload CSVs, configure lens parameters, manage streaming windows, and consume SSE results. This client facilitates automated state classification without manual data wrangling.

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