What is building-guidelines-agent?

53/100
Trust Score (D)
⚠️ Use Caution

building-guidelines-agent is a legal that A full-stack AI-powered application that answers user questions about construction and building regulations.. It has a Nerq Trust Score of 53/100 (D). 0 GitHub stars. Published by devdaviddr. Last analyzed September 2026.

Why This Score

Trust & Safety Overview

53
TRUST SCORE
D
GRADE
0
STARS
0
DOWNLOADS

What building-guidelines-agent Does

building-guidelines-agent is a agent in the legal category. A full-stack AI-powered application that answers user questions about construction and building regulations.. It is published by devdaviddr and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.

Who Should Use building-guidelines-agent

building-guidelines-agent is suitable for evaluation and non-critical use. Review the trust score breakdown before using in production.

Details

Authordevdaviddr
Categorylegal
LicenseNot specified
Typeagent
SourceView on GitHub
Security Score0/100
Activity Score1/100

How to Get Started

Check the trust score before installing:

curl nerq.ai/v1/preflight?target=building-guidelines-agent

Setup guide · Full safety report · Production review · Is it safe?

Safer Alternatives

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ai-legal-compliance-assistant56312
lawglance58220
DISC-Law-SFT59164
InstantLegalDotAI58152

Frequently Asked Questions

What is building-guidelines-agent used for?
building-guidelines-agent is a legal tool. A full-stack AI-powered application that answers user questions about construction and building regulations..
Is building-guidelines-agent free?
License: Check project page. building-guidelines-agent has 0 GitHub stars.
Is building-guidelines-agent safe?
building-guidelines-agent has a Nerq Trust Score of 53/100 (D). Use with caution.
What are alternatives to building-guidelines-agent?
Top alternatives: PDR_AI_v2, ai-legal-compliance-assistant, lawglance. See full comparison.

Last updated September 2026. Trust scores based on automated analysis of public data.

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