# The Mindscape of Bro Tsong

Project: The Mindscape of Bro Tsong

Current phase: `model_library_mvp`

Current subsystem: Model Library / Model Management MVP

## 1. Project Definition

This project is a file-first MVP for a cognitive model library.

It turns core cognitive models into structured, traceable, callable, and testable model assets.

The first version validates the workflow with two sample models:

- QPI
- Intellectual Archaeology

## 2. What This Project Is

This project is:

- A model asset library
- A model card system
- A source evidence index
- A regression test container
- A minimal model selection demo foundation
- A foundation for a future question-answering / cognitive processing system

## 3. What This Project Is Not

This project is not:

- A full model management platform
- A public SaaS product
- A user-facing application
- A complete knowledge graph
- A full RAG system
- A commercial platform
- A multi-user collaboration system
- A complete question-answering system

## 4. Current MVP Goal

The current MVP tests whether a small number of core cognitive models can be represented as:

- Human-readable model cards
- Machine-readable JSON model specs
- Source article records
- Source evidence excerpts
- Regression test cases
- Minimal selector inputs and outputs

## 5. First Sample Models

### QPI

QPI is a routing model that classifies a user input as:

- Question: lack of information
- Problem: lack of path or method
- Issue: lack of stability, consensus, or dynamic balance

### Intellectual Archaeology

Intellectual Archaeology is a deep modeling model that analyzes a topic through multiple depth layers, from surface application to mechanism, purpose, human capability, and philosophical assumptions.

## 6. Repository Structure

```text
docs/      Project rules, contracts, workflow notes, decisions, non-goals, and handoff templates.
schemas/   JSON Schema files for model specs, source records, source excerpts, and regression cases.
models/    Machine-readable JSON model specifications.
cards/     Human-readable Markdown model cards.
sources/   Source article records and source evidence excerpts.
tests/     Regression cases for model use, misuse, and boundary checks.
selector/  Rule-based selector configuration and examples.
scripts/   Local validation and selector demo scripts.
reports/   Validation reports, extraction notes, and concrete session handoffs.
```

## 7. Data Format

The project uses JSON as the machine-readable source format.

Markdown files are used for human-readable model cards and documentation.

## 8. Validation

All model JSON files should eventually pass schema validation.

Validation should check:

- Required fields
- Enum values
- Unique model IDs
- Source article references
- Source evidence references
- Regression test references

## 9. Minimal Selector

The selector is not a full AI system.

It is a simple demo that recommends candidate models based on:

- Trigger keywords
- Input type match
- Negative triggers
- Pipeline position
- Selection priority

## 10. Development Principles

- Keep the MVP small.
- Prefer files over databases.
- Prefer explicit schema over implicit conventions.
- Prefer traceability over automation.
- Prefer testability over expressive writing.
- Do not expand to many models before the sample models are stable.
- Treat `model_library_mvp` as the current phase, not as a nested project root.

## 11. Related Projects

- `knowledge-vault`: source archive, discussion records, and durable upstream documentation.
- `ccpe-system`: expert-agent, runtime, model, and protocol specification workbench.
- `skills-vault`: canonical source for reusable automation skills.
- `writing-workbench`: deep writing production workspace.
- `video-workbench`: dimensional output workspace for scripts, presentations, and videos.

This repository consumes selected source material and model definitions from the surrounding ecosystem, but it remains the product/system boundary for The Mindscape of Bro Tsong.

## 12. Current Status

Initial project setup.

## 13. Next Steps

1. Confirm directory structure.
2. Confirm schema files.
3. Add QPI model JSON.
4. Add Intellectual Archaeology model JSON.
5. Add human-readable model cards.
6. Add source records and evidence excerpts.
7. Add regression cases.
8. Add validation scripts.
9. Add minimal selector demo.