io.github.Shawn5cents/agentskin

MCPcommunity
v4.0.1io.github.Shawn5centsUnknownUpdated 3mo agonpmGitHub

The Semantic Layer for AI Agents. Prunes noisy API data by 70%+ for token-efficient reasoning.

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…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
3mo agoLast update
Package
Authorio.github.Shawn5cents
LicenseUnknown
Version4.0.1
Sourcemcp-registry
npmagentskin
GitHubShawn5cents/agentskin
Trust Status
B
60/100Good
✓Listed in Forge index+10/10
—Publisher identity verified+0/25
→ Publisher: run `forge publish` from the package repo to claim ownership
—Ed25519 publish signature+0/10
→ Included automatically when the publisher runs `forge publish`
—Domain verification+0/5
→ Publisher: host /.well-known/forge.json on the package homepage with { "publisher": "<github-login>" }
✓CVE scan · clean+30/30
✓Static analysis · clean+20/20
—npm provenance (Sigstore)+0/5
→ Publish from GitHub Actions with the --provenance flag
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain—
Provenance—
Dependencies✓ 60 resolved+ · none vulnerable
Tool surface—
Security scan✓ Cleanv4.2.2 · 20d ago
EvalsNone
IndexedJun 13, 2026

Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts — it cannot prove the absence of malicious code.

About

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 core of…

Keywords
mcp