Brilliant — I’m moving ahead. You’re about to get a full 200‑file mental‑model pack (100 general + 100 hybrid‑estate), all in clean Markdown, all structured for agentic reasoning, all ready for Obsidian.
Before I start generating the full set, here’s how I’ll deliver it so you don’t get a 200‑file wall of text in a single message.
✅ How I Will Deliver the 200‑File Pack (Clean, Modular, Usable)
To keep this structured and easy to integrate into your vault:
I will deliver the pack in batches of 10 models per message, in this order:
General Pack (100 models)
• Batch 1: Models 1–10 • Batch 2: Models 11–20 • … • Batch 10: Models 91–100
Estate Hybrid Pack (100 models)
• Batch 11: Models 1–10 • Batch 12: Models 11–20 • … • Batch 20: Models 91–100
This gives you:
• Clean, readable chunks • Easy copy‑paste into Obsidian • Zero formatting loss • Predictable structure • No message overload
And you can drop each batch directly into your vault as you receive it.
🚀 Starting Now: General Pack — Models 1–10
Below is Batch 1 of the General Mental Models Pack.
Each file is standalone Markdown, ready to paste into:
Mental Models (General)/
📄 01 — First Principles
Definition
Break a problem down to its fundamental truths and reason upward from there.
When to Use
• Complex or ambiguous problems • Legacy systems with accumulated assumptions • High‑stakes decisions • When intuition feels constrained by “how things are done”
How It Improves Reasoning
It removes inherited assumptions and reveals simpler, more powerful solutions.
Steps
- Identify the core truths.
- Strip away assumptions.
- Rebuild the solution from the ground up.
Example
Instead of asking “How do I improve my energy bill?”, ask: “What are the fundamental drivers of energy consumption?”
Prompts
• “Break this down using first principles.” • “Identify hidden assumptions in this problem.”
📄 02 — Inversion
Definition
Solve a problem by thinking about the opposite of what you want.
When to Use
• When stuck • When risks matter more than gains • When preventing failure is easier than achieving success
How It Improves Reasoning
Reveals blind spots and failure modes.
Steps
- Define the opposite outcome.
- List ways to achieve that opposite.
- Avoid those actions.
Example
Instead of “How do I make this system great?”, ask: “How could I make it terrible?”
Prompts
• “Invert this problem and analyze failure modes.”
📄 03 — Second‑Order Thinking
Definition
Consider the consequences of consequences.
When to Use
• Automation • Policy changes • Systemic decisions
How It Improves Reasoning
Prevents unintended side effects.
Steps
- Identify first‑order effects.
- Map second‑order effects.
- Evaluate tradeoffs.
Example
Automating irrigation may increase water use unless paired with soil‑moisture feedback.
Prompts
• “Map first‑ and second‑order effects of this decision.”
📄 04 — Probabilistic Thinking
Definition
Reason in terms of likelihoods instead of certainties.
When to Use
• Forecasting • Risk decisions • Ambiguous data
How It Improves Reasoning
Reduces overconfidence and binary thinking.
Steps
- Estimate probabilities.
- Compare scenarios.
- Choose the highest‑expected‑value path.
Example
“Rain tomorrow” becomes “40% chance of rain; plan accordingly.”
Prompts
• “Give a probabilistic breakdown of outcomes.”
📄 05 — Bayesian Updating
Definition
Update beliefs as new evidence arrives.
When to Use
• Monitoring systems • Diagnostics • Iterative decisions
How It Improves Reasoning
Prevents outdated assumptions.
Steps
- Establish prior belief.
- Gather evidence.
- Update belief proportionally.
Example
If solar output is lower than expected, update your model of shading.
Prompts
• “Update the prior belief given this new evidence.”
📄 06 — Occam’s Razor
Definition
Prefer the simplest explanation that fits the data.
When to Use
• Debugging • Diagnostics • Conflicting hypotheses
How It Improves Reasoning
Avoids overcomplication.
Steps
- List explanations.
- Remove unnecessary assumptions.
- Choose the simplest viable one.
Example
A sensor reading is off — likely calibration, not hardware failure.
Prompts
• “Rank explanations by simplicity and plausibility.”
📄 07 — Hanlon’s Razor
Definition
Never attribute to malice what can be explained by error or ignorance.
When to Use
• Interpersonal conflict • System failures • Miscommunication
How It Improves Reasoning
Reduces emotional misinterpretation.
Steps
- Identify the event.
- List non‑malicious explanations.
- Evaluate likelihoods.
Example
A contractor didn’t reply — likely busy, not hostile.
Prompts
• “Apply Hanlon’s Razor to interpret this situation.”
📄 08 — Systems Thinking
Definition
Understand how components interact within a whole.
When to Use
• Automation • Energy systems • Multi‑agent workflows
How It Improves Reasoning
Reveals hidden interdependencies.
Steps
- Identify components.
- Map interactions.
- Analyze feedback loops.
Example
Energy usage depends on weather, occupancy, insulation, and appliance cycles.
Prompts
• “Map the system and identify leverage points.”
📄 09 — Feedback Loops
Definition
Processes where outputs feed back into inputs.
When to Use
• Automation • Monitoring • Control systems
How It Improves Reasoning
Predicts runaway effects or stabilizing forces.
Steps
- Identify loop type.
- Map reinforcing vs balancing effects.
- Adjust system design.
Example
Heating increases temperature → thermostat reduces heating.
Prompts
• “Identify feedback loops in this system.”
📄 10 — Constraints Analysis
Definition
Identify the limiting factor that governs system performance.
When to Use
• Optimization • Project planning • Throughput analysis
How It Improves Reasoning
Focuses effort where it matters most.
Steps
- Identify all constraints.
- Find the binding constraint.
- Optimize or remove it.
Example
A workflow is limited by a single slow API.
Prompts
• “Identify the binding constraint in this workflow.”