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.
knowledge_assets/  Stable long-term reusable knowledge distilled from local project artifacts.
ccra_review_bundle/  Per-round CCRA review packages; each round must live in its own dated subdirectory.

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 pass the local schema before they are treated as stable.

Validation should check:

• Required fields
• Enum values
• Unique model IDs
• Source article references
• Source evidence references
• Regression test references
• Model/card indexes
• Markdown card required sections
• Regression case coverage

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.

See PROJECTS.md for the operative cross-repository boundary map, including request channels for ccpe-system and skills-vault.

12. Current Status

Foundation repair in progress for:

• QPI
• Intellectual Archaeology / 思想考古学

Current foundation assets include:

• GPT plan localization protocol.
• File taxonomy for canonical, generated, review archive, and temporary files.
• Model extraction rules.
• Model card contract.
• Model extraction workflow.
• JSON schemas for model cards, source articles, source excerpts, regression cases, and indexes.
• Machine-readable model index.
• Human-readable card index.
• Validation scripts for model library contracts and card headings.
• Source article and source excerpt indexes.
• Regression cases for model use, boundary behavior, and misuse.
• Rule-based selector configuration and examples.
• A standard-library validation script that writes reports/validation_report.md.
• A standard-library index rebuild/check script that writes reports/index_rebuild_report.md.
• ChatGPT handoff rules.
• Long-term knowledge asset rules and knowledge_assets/ documents, including 09_数据治理与模型调用机制说明.md.

Current QPI and Intellectual Archaeology model contents pass the local contract and remain draft pending product review.

Indexes follow docs/INDEX_MAINTENANCE_PROTOCOL.md: every asset change must synchronize models/model_index.json and cards/card_index.md, and handoff/release points must run a full rebuild or check.

CCRA review packages are archived by round under ccra_review_bundle/round-NN_YYYY-MM-DD_topic/. Do not place new review bundle files directly in ccra_review_bundle/; create or update the current round directory instead.

13. Next Steps

1. Submit the Round 03.1 selector/no-call/regression patch for GPT / CCRA review.
2. Keep QPI and Intellectual Archaeology at draft / B / pending until Owner / CCRA review accepts stronger status.
3. Run a QPI blind-test round with new unlabelled inputs after Round 03.1 passes.
4. Defer any third-model expansion until the current QPI selector and case layer are accepted.
5. Route missing reusable extraction, inspection, or stability-scoring tools to requirements/skills-vault/ or requirements/ccpe/ instead of improvising local platform features.