Clean the context before it hits the model. AgentSkin keeps task-relevant facts and strips low-value API, JSON, and terminal noise.
Agent tools often return far more structure than a task requires. AgentSkin treats context selection as deterministic middleware: classify the source, preserve task-relevant fields, remove known noise, and expose what transformation occurred.
Compression is not accepted as success when required information is lost.
Other public products and experiments from Nichols AI. AgentSkin is the context layer; these are the places it lives beside.
Applied AI systems, agent infrastructure, production software, and business automation.
Turn YouTube videos, webpages, and documents into clean notes without the copy-paste mess.
A public build experiment from the Nichols AI lab, available directly on GitHub.