AgentSkin introduces the Semantic Shorthand Standard (SSS), a protocol designed to optimize how autonomous AI agents perceive and process structured data. The internet is built for human visual consumption, resulting in high-entropy data payloads (bloated JSON, nested HTML) that degrade LLM reasoning performance and exponentially increase token consumption.
AgentSkin provides a deterministic mechanism to prune this noise, delivering low-entropy, high-signal data directly to the agent's context window.
The AgentSkin protocol is distributed as an open-source Model Context Protocol (MCP) server. By installing the reference server locally, any agent (Claude, Cursor, etc.) gains the ability to autonomously define and fetch high-density Semantic Skins.
npx -y agentskin@latest
The architecture is strictly Local-First. Data retrieval and recursive pruning occur securely on the host machine.
The reference implementation includes robust security measures to protect against common attack vectors in AI agent pipelines.
Blocks requests to private network ranges including IPv4 (127.x.x.x, 10.x.x.x, 172.16-31.x.x, 192.168.x.x, 169.254.x.x) and IPv6 variants (::1, ::ffff:, fe80:). Zone IDs are stripped and validated before processing.
Prevents access to cloud provider metadata services (metadata.google.internal, metadata.azure.com, kubernetes.default.svc) that could expose cloud credentials or configuration.
30 requests/minute sliding window per client to prevent abuse and ensure fair resource allocation in multi-agent environments.
All tool inputs are validated with Zod schema validation. Type coercion ensures signals, aliases, and apply_reasoning parameters are properly typed before processing.
HTML-extracted URLs are sanitized to block dangerous schemes (javascript:, data:) that could enable XSS attacks through the MCP tool interface.
30-second processing timeout prevents resource exhaustion from maliciously large payloads.
The security implementation is fully open-source and includes 77 tests for continuous validation.
The reference implementation delivers verifiable, deterministic compression results.
Average compression ratio of 66-86% for typical API responses. Token savings vary by data structure complexity and signal specificity.
Standard 5-token platform fee applies per skin generation, enabling predictable cost modeling for autonomous agent pipelines.
Sub-100ms transformation latency for payloads under 100KB. 30-second maximum timeout for large payloads.