How philo-r76/project-portfolio works
This repository is a synthetic-data portfolio case study about whether AI-assisted work’s modeled labor capacity value justifies token spend. The documented workflow is to generate task and rate data, load it into SQLite, analyze unit economics by month, department, workflow, model, and user concentration, then present an interactive dashboard and executive readout to inform a controlled-pilot decision. The generation and dashboard-build scripts are omitted as build machinery. No source excerpts were available, so relationships below rely on README documentation only.
Main entry points: dashboard.html Interactive dashboard · index.html Executive readout · usage_events.csv Usage events · model_rates.csv Model rates
Components 9
Reader experience
- Executive readout
index.html - Interactive dashboard
reports/dashboard.html
Analysis
- SQLite schema
sql/schema.sql - Segmented SQL analysis
sql/analysis.sql - SQLite database
Synthetic inputs
- Usage events
data/usage_events.csv - Model rates
data/model_rates.csv - Department rates
data/department_rates.csv
Other components
- Portfolio reader
Connections 10
- Portfolio reader → Executive readout: views
README.md - Executive readout → Interactive dashboard: embeds
README.md - Interactive dashboard → Usage events: reads
README.md - Interactive dashboard → Model rates: reads
README.md - Interactive dashboard → Department rates: reads
README.md - SQLite schema → SQLite database: defines tables
README.md - Usage events → SQLite database: imports
README.md - Model rates → SQLite database: imports
README.md - Department rates → SQLite database: imports
README.md - SQLite database → Segmented SQL analysis: analyzes tables
README.md