io.github.AhmeedGamil/semhood

MCPcommunity
v0.1.0io.github.AhmeedGamilUnknownUpdated 1mo agoGitHub

AST-based semantic code search; results ship with their call graph (calls + callers).

Stop grepping. Find the exact code your AI agent needs by intent, not keywords. semhood is an AST-aware semantic code search engine that retrieves code by what it does, complete with call-graph context and optional LLM enrichment. Runs fully offline with zero API keys — or plug in cloud embeddings — Voyage's code-specialized models, or OpenAI's strong general-purpose (natural-language) embeddings…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
1mo agoLast update
Package
Authorio.github.AhmeedGamil
LicenseUnknown
Version0.1.0
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✓ Cleanv0.1.0 · 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

Stop grepping. Find the exact code your AI agent needs by intent, not keywords. semhood is an AST-aware semantic code search engine that retrieves code by what it does, complete with call-graph context and optional LLM enrichment. Runs fully offline with zero API keys — or plug in cloud embeddings — Voyage's code-specialized models, or OpenAI's strong general-purpose (natural-language) embeddings — for higher-quality retrieval. Optional LLM enrichment adds a logic summary and developer queries…

Keywords
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