Ai Legal Intake

D 57 completed
Other
unknown / javascript · tiny
13
Files
2,510
LOC
1
Frameworks
4
Languages

Pipeline State

completed
Run ID
#376472
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
29.96
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47745
Member of a group with 1 similar repo(s) — canonical #96362 view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility analyzer · published findings · https://repobility.com

AI Prompt

Create a comprehensive AI-powered lead intake system assessment for a law firm client. I need the structure to demonstrate two implementation approaches: one using n8n workflows and another using code. For the coded approach, please use JavaScript with Express for the backend, and also include a Python FastAPI example. The system must incorporate a two-layer qualification engine, handle errors with retry and dead letter queue logic, and use OpenAI function calling to ensure structured JSON output. Please organize the output to include the main assessment document, architecture diagrams (Mermaid format), and the necessary code files for both the JS/Express and Python implementations.
javascript python express fastapi ai n8n lead-intake automation openai backend
Generated by gemma4:latest

Catalog Information

This is my skills assessment for the AI & Automations Integration Specialist role at Lion Head. I've designed a complete AI-powered lead intake system for law firm clients, presenting two implementation approaches to demonstrate both no-code automation and coding skills.

Description

This is my skills assessment for the AI & Automations Integration Specialist role at Lion Head. I've designed a complete AI-powered lead intake system for law firm clients, presenting two implementation approaches to demonstrate both no-code automation and coding skills.

Novelty

3/10

Tags

javascript python express fastapi ai n8n lead-intake automation openai backend

Technologies

express openai

Claude Models

claude-opus-4-6

Quality Score

D
56.7/100
Structure
41
Code Quality
75
Documentation
59
Testing
0
Practices
73
Security
100
Dependencies
60

Strengths

  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No tests found \u2014 high risk of regressions
  • No CI/CD configuration \u2014 manual testing and deployment

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.6h
Tech Debt (D)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (4)
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
Unknown
License
4.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

javascript
35.6%
json
26.9%
markdown
22.7%
python
14.8%

Frameworks

Express

Concepts (2)

All metrics by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Open data scored by Repobility · https://repobility.com
auto_descriptionProject DescriptionThis is my skills assessment for the AI & Automations Integration Specialist role at Lion Head. I've designed a complete AI-powered lead intake system for law firm clients, presenting two implementation approaches to demonstrate both no-code automation and coding skills.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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