Prospect Explorer
F 45 completed
Web App
containerized / python · tiny
31
Files
9,223
LOC
1
Frameworks
4
Languages
Pipeline State
completedRun ID
#372803Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
50.21Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #52266
Member of a group with 1 similar repo(s) — canonical #89897 view group →
Top concepts (1)
Web Backend
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🧪 Code Distillation
Browse all specs →AI Prompt
I want to build a prospect explorer tool using Python and FastAPI. This application should allow businesses to explore and analyze potential customers. It needs to handle data from CSV files like `companies.csv` and `customers.csv`. Include functionality for sequential data enrichment and analysis, perhaps involving steps like fetching accounts, embedding data, and calculating similarity, as suggested by the various scripts. The entire application should be containerized using a Dockerfile.
python fastapi data-analysis crm prospecting docker csv backend
Generated by gemma4:latest
Catalog Information
The prospect-explorer project is a tool for businesses to explore and analyze potential customers.
Description
Prospect Explorer is a web application that enables users to discover and analyze potential customers. It leverages machine learning algorithms to provide insights on customer behavior, preferences, and demographics. The tool aims to help businesses make informed decisions about their target audience and marketing strategies.
الوصف
يعد مستكشف المشتريات أداة لشركات الاستكشاف والتحليل للمستخدمين المحتملين. يستفيد المستخدم من خوارزميات التعلم الآلي لتوفير نظرة عامة حول سلوك العملاء، تفضيلاتهم، وبيانات démographiques. يهدف الأداة إلى مساعدة الشركات على اتخاذ قرارات مدروسة بشأن جمهورها المستهدف ومستراتيجيات التسويق.
Novelty
5/10Tags
customer-analysis machine-learning data-insights business-intelligence marketing-strategy customer-discovery target-audience
Technologies
fastapi numpy openai pandas uvicorn
Claude Models
claude-opus-4.6
Quality Score
F
44.6/100
Structure
30
Code Quality
74
Documentation
29
Testing
0
Practices
51
Security
82
Dependencies
60
Strengths
- 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 tests found \u2014 high risk of regressions
- No CI/CD configuration \u2014 manual testing and deployment
- Potential hardcoded secrets in 1 files
- 363 duplicate lines detected \u2014 consider DRY refactoring
- 2 '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)
- Move hardcoded secrets to environment variables or a secrets manager
Security & Health
4.6h
Tech Debt (B)
A
OWASP (100%)
FAIL
Quality Gate
Repobility — the code-quality scanner for AI-generated software · https://repobility.com
A
Risk (13)
Unknown
License
5.2%
Duplication
Languages
Frameworks
FastAPI
Concepts (1)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
LLM Insights
A FastAPI-based web service that lets businesses explore and analyze potential customers using data insights and machine learning.structured_summary
infopurpose: A FastAPI-based web service that lets businesses explore and analyze potential customers using data insights and machine
primary_domain: web-backend
reference_quality70
reuse_potential: high
Repobility analyzer · published findings · https://repobility.com
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