Automates Elastic ML demo setup by translating scripts and data models into config files and guides.
npx skills add https://github.com/ajmeyers42/loom --skill weave-train
Please help me install this Agent Skill. Skill: weave-train Source: https://github.com/ajmeyers42/loom/tree/main/skills/weave-train Command: npx skills add https://github.com/ajmeyers42/loom --skill weave-train
Reads a demo script and data model to automatically configure Elastic ML job configurations: anomaly detection jobs, datafeeds, and an anomaly-injection plan, plus a training data setup for NLP scenes when applicable. It orchestrates the entire ML demo flow from inputs to ready-to-run artifacts, ensuring timing and baselines align with the demo narrative.
Generate a complete ML demo configuration by supplying a demo script and a data model to produce the JSON config and setup guide.
High-intent search queries and answers about installing and using this skill.
You can automate Elastic ML anomaly detection job setup by providing a demo script and data model to generate ready-to-run ML artifacts, including datafeeds, injection plans, and setup guides.
To generate Elastic ML datafeeds and configuration files, you need a demo script and a corresponding data model that define the anomaly detection requirements and timing baselines for your narrative.
Yes, you can align anomaly injection plans with demo script timing by translating the script and data model into ML artifacts, ensuring baselines and injections match the pre-sales demo narrative.
Yes, the automated ML demo configuration approach supports NLP training data setup for applicable scenes, generating the necessary training data prerequisites alongside anomaly detection jobs and datafeeds.
The best way to create an end-to-end ML setup guide is to automate the translation of a demo script and data model into ready-to-use Elastic artifacts, outputting a JSON config and a corresponding setup markdown document.
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