8.7 KiB
Model Mining Mode
1. Purpose
Model Mining Mode extracts cognitive models from source material.
Source material may include:
Long-form essays
Academic-style prose
Notes
Drafts
Discussions
Agent appendices
Old prompts
Knowledge base documents
The goal is not to summarize the text.
The goal is to identify reusable cognitive structures that can become:
Model Cards
Skills
Agents
Runtime components
Model Index entries
2. When to Use Model Mining Mode
Use Model Mining Mode when the user asks to:
Extract models from an article
Find hidden models
Create Model Cards
Build Model Index
Compress writing into models
Identify reusable cognitive structures
Turn essays into AI-usable knowledge assets
Also use this mode when a self-contained Agent contains a large theoretical appendix.
3. Core Principle
Model Mining should behave like lossless compression.
Remove:
Rhetorical bulk
Repeated explanation
Academic completeness overhead
Decorative examples
Non-essential digressions
Preserve:
Generative structure
Core assumptions
Mechanism
Causal logic
Scope
Boundary
Failure mode
Falsifiability
Useful terminology
Original intellectual flavor
4. What Counts as a Model
A cognitive model should usually have:
A core problem
A scope
Core assumptions
A mechanism
A way of generating or explaining outcomes
Inputs
Outputs
Failure modes
Boundary conditions
Possible falsification
A model may be explicit or implicit.
5. What Does Not Automatically Count as a Model
Do not treat every interesting idea as a model.
Distinguish models from:
Claim
Metaphor
Theme
Taxonomy
Writing style
Opinion
Example
Analogy
Procedure without mechanism
Checklist without theory
Some of these may become part of a model, but they are not automatically models.
6. Model Types
Classify extracted models as:
Foundational Model
Intermediate Model
Applied Model
Workflow Model
Implicit Extracted Model
Candidate Model
6.1 Foundational Model
A deep underlying model that supports many other models.
Examples:
Cognition theory
Entropy / anti-entropy assumptions
Complex system assumptions
Causal generation principles
6.2 Intermediate Model
A mid-level model that organizes a domain or reasoning pattern.
Examples:
Cognitive Imaging
Giant Cognition
Cognitive Prism
6.3 Applied Model
A model designed for a specific use case.
Examples:
Article critique model
Strategic risk model
Argument repair model
6.4 Workflow Model
A model that naturally becomes a repeatable process.
Examples:
Five-step analysis process
Review committee procedure
Knowledge extraction pipeline
6.5 Implicit Extracted Model
A model that was not explicitly named by the author but can be reconstructed from repeated logic.
Mark these as candidate unless confirmed by the user.
7. Model Mining Workflow
Follow this sequence:
1. Read source material
2. Identify explicit named models
3. Identify repeated conceptual mechanisms
4. Identify implicit model candidates
5. Separate models from claims, metaphors, and themes
6. Extract assumptions
7. Extract mechanism
8. Extract scope
9. Extract procedure if any
10. Extract failure modes
11. Define falsification boundary
12. Classify model type
13. Propose Model Card
14. Propose Model Index entry
15. Recommend Skill or Agent conversion
16. List human review questions
8. Explicit Model Extraction
An explicit model may be signaled by:
Named framework
Numbered stages
Defined principles
Repeated terminology
Declared scope
Process diagram
Model-like appendix
Theory section
For explicit models, preserve the author's naming unless there is a strong reason to adjust.
9. Implicit Model Extraction
An implicit model may be signaled by:
Repeated explanatory structure
Recurring causal logic
Stable metaphor with mechanism
Consistent diagnostic lens
Repeated problem-solving pattern
Hidden taxonomy
Unstated decision rule
For implicit models:
Mark as Candidate Model.
Give a provisional name.
State the extraction basis.
List uncertainty.
Ask for user confirmation before canonical indexing.
10. Model Extraction Fields
For each candidate model, extract:
Model Name
Aliases
Source Material
Model Type
Core Problem
Scope
Core Assumptions
Mechanism
Procedure
Inputs
Outputs
Failure Modes
Falsification Boundary
Related Models
Possible Skills
Possible Agents
Runtime Usage
Confidence Level
Review Status
11. Falsification Boundary
Every strong model should define what would make it fail.
Ask:
What would this model predict should not happen?
What input is outside its scope?
What observation would weaken it?
Where does it become overgeneralized?
What failure mode turns it into pseudoscience?
If no falsification boundary can be found, mark as:
Weak candidate
Metaphor
Theme
Unbounded explanatory frame
12. Model vs Skill
A Model explains or generates.
A Skill executes.
Example:
Cognitive Imaging Model:
Defines the theory of capture, darkroom, enlarger, exposure, development.
Cognitive Imaging Skill:
Applies that model to an input and produces an analysis report.
When a model has a stable procedure, recommend Skill conversion.
13. Model vs Agent
A Model is not a persona.
An Agent may use a Model.
Example:
Cognitive Imaging Model:
The conceptual structure.
Cognitive Imaging Specialist:
The agent role that applies the model in interaction with the user.
When a model benefits from a specialized human-facing role, recommend Agent conversion.
14. Model vs Runtime
A Runtime orchestrates work across stages, agents, skills, state, and human decisions.
A model may become part of Runtime when it governs a workflow.
Example:
Review Committee Runtime may use:
- Zhangliao Red-Team Agent
- Cognitive Imaging Agent
- Cognitive Prism Agent
- Synthesis Agent
- Knowledge Archivist
15. Model Mining Report Format
Use this format:
# Model Mining Report
## 1. Source Material
Title:
Path:
Author:
Date:
Source Type:
## 2. Extraction Summary
## 3. Explicit Models
## 4. Implicit Candidate Models
## 5. Non-Model Ideas
## 6. Recommended Model Cards
## 7. Recommended Model Index Entries
## 8. Skill Conversion Opportunities
## 9. Agent Conversion Opportunities
## 10. Runtime Usage Opportunities
## 11. Human Review Questions
16. Candidate Model Card Draft
Use this short draft format during extraction:
# Candidate Model Card: {Model Name}
## Model Type
## Source Material
## Core Problem
## Scope
## Core Assumptions
## Mechanism
## Procedure
## Inputs
## Outputs
## Failure Modes
## Falsification Boundary
## Related Models
## Possible Skills
## Possible Agents
## Confidence
## Review Status
17. Confidence Levels
Use:
High
Medium
Low
High confidence:
The model is explicit, named, and structurally complete.
Medium confidence:
The model is strongly implied but not fully formalized.
Low confidence:
The model is a plausible extraction but needs user confirmation.
18. Review Status
Use:
candidate
draft
active
rejected
merged
deprecated
archived
Do not mark a model as active without user approval.
19. Extraction Log
When extracting from source material, propose an extraction log entry:
Source:
Date:
Extracted Models:
Confidence:
Review Status:
Open Questions:
Next Action:
20. Anti-Over-Extraction Rule
Do not over-extract.
A long article may contain:
One strong model
Several weak candidate models
Many claims
Several metaphors
Some procedures
Do not convert everything into Model Cards.
21. Anti-Flattening Rule
Do not reduce models to bland summaries.
Preserve:
Key terms
Metaphors with mechanism
Contradictions or tensions
Sharp distinctions
Original conceptual architecture
22. Human Review Questions
Ask human review questions when:
Model name is uncertain.
Scope is unclear.
The model may overlap with another model.
The extraction is implicit.
The falsification boundary is weak.
The model should maybe merge with another model.
The model's status should be active or candidate.
23. Final Response
When Model Mining is complete, final response should include:
Number of explicit models found
Number of implicit candidate models found
Recommended Model Cards
Recommended Model Index updates
Skill conversion candidates
Agent conversion candidates
Human review questions
Next action
24. Final Rule
Model Mining is not summarization.
It is the extraction of reusable generative structure from thought.
The output should help the user turn long-form thinking into maintainable cognitive infrastructure.