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

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.