io.github.Anarkitty1/semantic-frame

MCPcommunity
v0.2.1io.github.Anarkitty1UnknownUpdated 6mo agoGitHub

Token-efficient semantic compression for numerical data. 95%+ token reduction.

Token-efficient semantic compression for numerical data. Semantic Frame converts raw numerical data (NumPy, Pandas, Polars) into natural language descriptions optimized for LLM consumption. Instead of sending thousands of data points to an AI agent, send a 50-word semantic summary. LLMs are terrible at arithmetic. When you send raw data like to GPT-4 or Claude: Token waste: 1000 data points =…

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6mo agoLast update
Package
Authorio.github.Anarkitty1
LicenseUnknown
Version0.2.1
Sourcemcp-registry
GitHubAnarkitty1/semantic-frame
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✓ Cleanv0.4.0 · 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

Token-efficient semantic compression for numerical data. Semantic Frame converts raw numerical data (NumPy, Pandas, Polars) into natural language descriptions optimized for LLM consumption. Instead of sending thousands of data points to an AI agent, send a 50-word semantic summary. LLMs are terrible at arithmetic. When you send raw data like to GPT-4 or Claude: Token waste: 1000 data points = ~2000 tokens Hallucination risk: LLMs guess trends instead of calculating them Context overflow: Large…

Keywords
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