io.github.nugehs/aiglare

MCPcommunity
v0.2.3io.github.nugehsUnknownUpdated 23d agonpmGitHub

Audit AI/LLM features for governance guardrails: confidence, fallback, validation, human-in-loop.

Lint your AI features for governance guardrails — where can the model do something you can't undo? []( []( [](LICENSE) []( Live site: nugehs.github.io/aiglare-web Point it at any JS/TS repo and it finds every place an LLM/AI output reaches a user or triggers a side-effect (payment, booking, email, database write) — then flags which of those have no confidence handling, no fallback, no output…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
23d agoLast update
Package
Authorio.github.nugehs
LicenseUnknown
Version0.2.3
Sourcemcp-registry
npm@nugehs/aiglare
GitHubnugehs/aiglare
Trust Status
A
95/100Trusted
✓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)+5/5
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain—
Provenance✓ Sigstore-verified · 9643ef9
Dependencies✓ 1 resolved · none vulnerable
Tool surface3 tools · none privileged
Security scan✓ Cleanv0.2.3 · 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

Lint your AI features for governance guardrails — where can the model do something you can't undo? []( []( [](LICENSE) []( Live site: nugehs.github.io/aiglare-web Point it at any JS/TS repo and it finds every place an LLM/AI output reaches a user or triggers a side-effect (payment, booking, email, database write) — then flags which of those have no confidence handling, no fallback, no output validation, and no human-in-the-loop. Most AI incidents aren't model failures. They're governance…

Keywords
mcp