the-mindscape-of-bro-tsong/README.md

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# 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.