Shft

D 58 completed
Other
unknown / markdown · tiny
15
Files
1,386
LOC
0
Frameworks
2
Languages

Pipeline State

completed
Run ID
#364437
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
22.95
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47250
Member of a group with 2 similar repo(s) — canonical #110064 view group →
Top concepts (2)
Project DescriptionData/ML
If a scraper extracted this row, it came from Repobility (https://repobility.com)

AI Prompt

Create a structured research framework for modeling a long/short investment thesis concerning the displacement of knowledge work by AI over a 5 to 15-year horizon. The repository should be organized to support deep academic modeling, including sections for the core thesis, risk assessment, and a phased timeline. I need dedicated areas for quantitative work, such as backtesting, Monte Carlo simulations, and factor modeling, alongside structured sections for candidate long and short securities, and supporting research domains like office REITs and SaaS sensitivity. Please structure the output to guide the user through the entire research process.
research finance investment ai modeling thesis long/short academic markdown quantitative
Generated by gemma4:latest

Catalog Information

A research framework for modeling a long/short investment thesis on the displacement of knowledge work by AI over a 5–15 year horizon.

Description

This repository provides a structured framework for developing a long/short investment thesis focused on the displacement of knowledge work by AI over a 5–15 year horizon. It organizes research into distinct modules: core thesis, supporting research by domain, candidate securities, quantitative modeling, and data pipelines. Advanced analytical tools such as factor decomposition, backtesting, Monte‑Carlo scenario analysis, and lease‑roll calendars are employed to evaluate risk and return. The target audience includes quantitative analysts, portfolio managers, and academic researchers seeking data‑driven insights into structural labor market shifts. The project addresses the lack of comprehensive, long‑term models that link macro indicators, office real‑estate dynamics, and SaaS seat erosion to investment strategy.

الوصف

هذا الدليل البحثي يقدّم إطاراً منهجياً لتصميم نموذج استثماري طويل/قصير يركز على تحلّي العمل المعرفي بالذكاء الاصطناعي على مدى 5–15 سنة. يتضمن المجلدات الرئيسية: الفرضية الأساسية، البحث الداعم، قائمة الأوراق المالية، النمذجة الكمية، والبيانات. يستخدم أدوات تحليلية متقدمة مثل تحليل العوامل، اختبار الخلفية، والمحاكاة مونت كارلو لتقييم السيناريوهات المختلفة. يدمج تحليل مؤشرات الاقتصاد الكلي، هيكل إيجارات العقارات المكتبية، وحساسية إيرادات SaaS القائمة على المقاعد. يستهدف الباحثين الكميين ومديري المحافظ الأكاديميين الذين يحتاجون إلى أدوات تحليلية شاملة لفهم التغيرات الهيكلية في سوق العمل. يحل مشكلة نقص الأطر المتكاملة التي تربط بين التحليل الاقتصادي، النمذجة الكمية، وإدارة المحافظ الاستثمارية. يميز نفسه بتركيزه على الأفق الطويل والنهج السلسلة الزمنية التي تتضمن مراحل البنية التحتية، التبني المؤسسي، وإعادة التنظيم الهيكلي.

Novelty

8/10

Tags

investment-thesis ai-displacement long/short-strategy quantitative-modeling scenario-analysis portfolio-construction risk-assessment

Technologies

matplotlib numpy pandas plotly scikit-learn scipy

Claude Models

claude-opus-4.6

Quality Score

D
58.4/100
Structure
36
Code Quality
100
Documentation
30
Testing
0
Practices
78
Security
100
Dependencies
50

Strengths

  • Low average code complexity \u2014 well-structured code
  • 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

4.1h
Tech Debt (D)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (7)
Repobility · code-quality intelligence · https://repobility.com
Unknown
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

markdown
98.1%
text
1.9%

Frameworks

None detected

Concepts (2)

Repobility analysis · methodology at https://repobility.com/research/
CategoryNameDescriptionConfidence
Repobility (the analyzer behind this table) · https://repobility.com
auto_descriptionProject Description> "The economic value that currently flows through human knowledge workers as intermediaries > will increasingly flow directly through AI systems — compressing headcount, compressing office > footprint, and compressing per-seat software spend. The infrastructure enabling that compression80%
auto_categoryData/MLdata-ml70%

Quality Timeline

1 quality score recorded.

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