القائمة
The workload tracker system — scoring engine, team operations, and the Josefina deployment context.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
The Aurelion cognitive architecture — five modules that together form a complete autonomous agentic AI system.
Chase-Key's cognitive identity — who he is, how he thinks, and how to work with him effectively.
The Stonecrest world, the Darklight Chambers library, and the Memoria Engine — CK's creative universe that shapes the architecture of everything.
The RELL compliance audit engine — architecture, philosophy, active modules, and development status.
The Aurelion cognitive architecture — five modules that together form a complete autonomous agentic AI system.
Chase-Key's cognitive identity — who he is, how he thinks, and how to work with him effectively.
| 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.json