AgentSkin: Semantic Shorthand Standard (SSS)

AgentSkin is an open-source protocol and reference Model Context Protocol (MCP) server that establishes the Semantic Shorthand Standard (SSS) for Agentic Perception.

The protocol defines a standardized method for recursively pruning high-entropy, human-readable data (HTML, bloated JSON, complex APIs) into low-entropy, deterministic Markdown "Skins." This significantly reduces LLM token consumption (the "Token Tax") and eliminates perceptual drag in autonomous reasoning loops.

The Protocol

The core of AgentSkin is the recursive pruning engine. It operates on a simple, declarative standard:

1. Audit: Intercept raw data payloads.
2. Signal Mapping: Define an array of required, high-density keys.
3. Semantic Pivot: Apply an alias map to standardize inconsistent API schemas into a unified namespace.
4. Flatten: Output a deterministic, hierarchical Markdown string.

Reference Implementation

This repository provides the official Node.js reference implementation of the SSS protocol, exposed as a standard MCP server.

Quickstart (MCP Server)

You can run the AgentSkin reference server directly via npx to provide your local AI assistants (Claude Desktop, Cursor, etc.) with the fetch_optimized_data tool.

npx -y agentskin@latest

Claude Desktop Configuration

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "agentskin": {
      "command": "npx",
      "args": ["-y", "agentskin@latest"]
    }
  }
}

Tools

The reference implementation exposes the following tools to AI agents:

1. fetch_optimized_data

Fetches any API or Web URL and returns a 90% token-optimized "Skin."

• Arguments:
  • url (string, required): The target data source.
  • signals (string[], optional): An array of semantic keys to preserve.
  • aliases (object, optional): A map to rename original keys to standardized signals.

2. skin_reasoning

Optimizes natural language text by removing linguistic noise (hedging, filler).

• Arguments:
  • text (string, required): The natural language string to distill.

Creating a Skin

AgentSkin is a factory for intelligent perception. You provide the mapping; the protocol provides the engine.

When using the fetch_optimized_data tool, provide the signals and aliases parameters to build your own skin.

Example: Weather API Skin

{
  "url": "https://api.weather.gov/gridpoints/TOP/31,80/forecast",
  "signals": ["temperature", "windspeed", "shortforecast"],
  "aliases": {
    "temperature": "temp",
    "shortforecast": "forecast"
  }
}

Architecture

This package is designed as a Local-First, Open Studio.

• All data fetching and pruning happens locally on the host machine.
• User session state, cookies, and network access remain strictly local and private.
• The core engine (skin-engine.js) operates without external dependencies for transformation.

Documentation & Whitepaper

Read the full protocol specification and academic whitepaper at agentskin.dev.

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