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The workload tracker system — scoring engine, team operations, and the Josefina deployment context.
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| name | workload-domain |
| version | 1.0.0 |
| description | The workload tracker system — scoring engine, team operations, and the Josefina deployment context. |
| author | z3rosl33p |
| tags | ["workload","tracker","scoring","team","operations","josefina","queueing"] |
| last_updated | "2026-03-22T00:00:00.000Z" |
The workload tracker is a standalone web application for tracking team workload, scoring task complexity, and surfacing overload signals before they become problems. It was built inside rell-engine and has since been extracted as its own deployable product.
Locations:
rell-eco/rell-engine/engine/workload_engine.py — Core enginerell-eco/rell-workload/ — Standalone deploymentrell-workload/JOSEFINA_START_HERE.bat — The workload tracker was deployed for a specific user named Josefina. She is the primary day-to-day operator. The .bat file exists to make startup zero-friction for a non-technical operator.
When working on workload features, always consider:
The workload engine scores individual tasks and produces aggregate team load signals.
Config: rell-workload/config/scoring.json
Team Roster: rell-workload/config/team-roster.json
Scoring factors include task complexity, priority, time sensitivity, and team member current load. Outputs a normalized score per assignment and a team-level load index.
rell-workload/
├── run_web.py ← Start the web server
├── engine/
│ ├── workload_engine.py ← Core scoring logic
│ └── excel_parser.py ← Intake from Excel files
├── web/
│ ├── workload_api.py ← REST API
│ └── workload_pdf.py ← PDF report generation
├── config/
│ ├── scoring.json ← Scoring weights and thresholds
│ └── team-roster.json ← Team members and capacity
├── data/intake/ ← Drop Excel files here
├── Dockerfile ← Container deployment
└── fly.toml ← Fly.io production config
Run locally:
cd rell-eco/rell-workload
python run_web.py
The workload tracker was built as a case study for external validation of the RELL engine approach. See:
rell-eco/docs/case-study_workload-tracker.mdThe workload tracker is also embedded inside rell-engine where it provides:
data/workload/scoring_config.jsonprofiles/workload/workload-tracker.json and team-roster.jsonThe Aurelion cognitive architecture — five modules that together form a complete autonomous agentic AI system.
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Chase-Key's cognitive identity — who he is, how he thinks, and how to work with him effectively.