Lead Enrichment System

D 54 completed
Api
containerized / python · tiny
37
Files
8,178
LOC
1
Frameworks
5
Languages

Pipeline State

completed
Run ID
#365435
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
48.58
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47702
Member of a group with 1 similar repo(s) — canonical #29960 view group →
Top concepts (1)
Web Backend
Open data scored by Repobility · https://repobility.com

AI Prompt

Create a containerized lead enrichment system using Python and FastAPI. The system should process and enhance customer data. I need the core logic to handle data enrichment, which involves components like an LLM parser and a main pipeline execution. Please structure it to run via Docker Compose, and ensure it can read configuration from files like YAML or JSON. Include necessary testing scaffolding for various discovery and search functions.
python fastapi containerization data-enrichment api llm backend
Generated by gemma4:latest

Catalog Information

This project is a lead enrichment system designed to enhance and update customer data.

Description

The nikisge__lead-enrichment-system is a Python-based application that aims to improve the quality of customer information by enriching and updating it. It leverages various libraries such as Anthropic, BeautifulSoup, FastAPI, Playwright, Pydantic, and Uvicorn to achieve its goal. However, without further details in the README or description, the exact functionality and features of this system remain unclear.

الوصف

هذا المشروع هو نظام تحسين البيانات المتعلقة بالعملاء، ويهدف إلى تحسين جودة المعلومات عن العملاء من خلال تحديثها وتوسيعها. يستخدم هذا التطبيق لغة بايثون ويتضمن استخدام مكتبات متعددة مثل Anthropic و BeautifulSoup و FastAPI و Playwright و Pydantic و Uvicorn. ومع ذلك، فإن عدم توفر تفاصيل إضافية في README أو الوصف يجعل الوظيفة والخصائص الدقيقة لهذا النظام غير واضحة.

Novelty

3/10

Tags

lead-enrichment customer-data-enhancement data-updating information-quality-improvement

Technologies

anthropic beautifulsoup fastapi playwright pydantic uvicorn

Claude Models

claude-opus-4.6 claude-opus-4.5

Quality Score

D
54.0/100
Structure
41
Code Quality
53
Documentation
34
Testing
40
Practices
76
Security
100
Dependencies
60

Strengths

  • Good test coverage (30% test-to-source ratio)
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Containerized deployment (Docker)

Weaknesses

  • Missing README file \u2014 critical for project understanding
  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 1 bare except/catch blocks swallowing errors
  • 655 duplicate lines detected \u2014 consider DRY refactoring
  • 3 'god files' with >500 LOC need decomposition

Recommendations

  • Add a comprehensive README.md explaining purpose, setup, usage, and architecture
  • 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)
  • Replace bare except/catch blocks with specific exception types

Security & Health

5.3h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (2)
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
Unknown
License
3.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
94.2%
markdown
4.1%
json
1.3%
yaml
0.2%
text
0.1%

Frameworks

FastAPI

Concepts (1)

Source: Repobility analyzer (https://repobility.com)
CategoryNameDescriptionConfidence
Repobility (the analyzer behind this table) · https://repobility.com
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

View File Metrics

Embed Badge

Add to your README:

![Quality](https://repos.aljefra.com/badge/89610.svg)
Quality BadgeSecurity Badge
Export Quality CSVDownload SBOMExport Findings CSV