io.github.Octid-io/osmp

MCPcommunity
v1.3.3io.github.Octid-ioUnknownUpdated 1mo agoGitHub

Agentic AI instruction encoding. 86.8% vs JSON. Inference-free decode. Any channel.

Agentic AI mesh without the cloud. OSMP (Octid Semantic Mesh Protocol) is an open encoding standard for agentic AI instruction and computation exchange. It works across any channel — from a 51-byte LoRa radio packet to a high-throughput cloud inference pipeline — using the same grammar, the same dictionary, and the same decode logic. No cloud required. No inference at the decode layer. No central…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
1mo agoLast update
Package
Authorio.github.Octid-io
LicenseUnknown
Version1.3.3
Sourcemcp-registry
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
DependenciesNot audited
Tool surface
Security scan✓ Cleanv1.3.4 · 19d 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

Agentic AI mesh without the cloud. OSMP (Octid Semantic Mesh Protocol) is an open encoding standard for agentic AI instruction and computation exchange. It works across any channel — from a 51-byte LoRa radio packet to a high-throughput cloud inference pipeline — using the same grammar, the same dictionary, and the same decode logic. No cloud required. No inference at the decode layer. No central authority. 35 bytes on the wire. Decoded by dictionary lookup, not inference. Fits a single LoRa…

Keywords
mcp