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🧠 Thinking Frameworks

Provides structured deep analysis and decision-making using 20 human thinking frameworks like critical thinking, first principles, red team, and design think... Skill: 🧠 Thinking Frameworks Owner: wanikua Summary: Provides structured deep analysis and decision-making using 20 human thinking frameworks like critical thinking, first principles, red team, and design think... Tags: ai:1.0.0, analysis:1.0.0, critical-thinking:1.0.0, decision-making:1.0.0, frameworks:1.0.0, latest:1.0.0, thinking:1.0.0 Version history: v1.0.0 | 2026-03-24T13:23:02.904Z | user Initial release: 20

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Downloads

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Updated

Oct 10, 2026

Version

1.0.0

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CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.8K downloads reported by the source. Last updated 10/10/2026.

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Public facts

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Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.8K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.0release · observed Mar 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s178eq03sfh27vee3f6hzy3scs83hv8q:thinking-frameworks

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-wanikua-thinking-frameworks/snapshot"

Documentation

CLAWHUB

53,577 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

# Skill: Thinking Frameworks

**Location:** `skills/thinking-frameworks/`

**Description:** 20 human thinking frameworks for deep analysis and decision-making. Adapted from Claude Code commands to OpenClaw skills.

## Triggers

Use these thinking frameworks when the user:
- Explicitly requests a thinking mode (e.g., "use critical thinking", "first principles analysis", "red team this")
- Asks for deep analysis of a problem, decision, or plan
- Uses command syntax: `/thinking <framework> <topic>` or `/<framework-name> <topic>`
- Needs structured reasoning for complex decisions

## Available Frameworks

| Framework | Trigger Keywords | Use When |
|-----------|-----------------|----------|
| critical-thinking | critical, critique, analyze assumptions | Questioning assumptions, evaluating evidence, detecting biases |
| first-principles | first principles, first principles thinking, elon musk approach | Strip away assumptions, rebuild from fundamental truths |
| systems-thinking | systems, system thinking, holistic, causal loops | Understanding interconnected systems, feedback loops, emergence |
| design-thinking | design thinking, empathize, prototype | User-centered problem solving, creative iteration |
| lateral-thinking | lateral, creative, outside the box | Breaking conventional patterns, finding novel solutions |
| six-thinking-hats | six hats, de bono, white/red/black/yellow/green/blue | Multi-perspective analysis, group decision framing |
| socratic-method | socratic, questioning, progressive questions | Deep exploration through guided questioning |
| bayesian-thinking | bayesian, update beliefs, prior, evidence | Probabilistic belief updating with new evidence |
| second-order-thinking | second order, "and then what", consequences | Long-term consequence chains, unintended effects |
| inversion-thinking | inversion, invert, reverse, how would this fail | Problem by flipping, finding failure modes |
| dialectical-thinking | dialectical, thesis antithesis synthesis, hegel | Resolving contradictions through synthesis |
| abductive-reasoning | abductive, best explanation, inference | Inferring the most likely explanation from observations |
| mental-models | mental models, munger, multidisciplinary | Cross-disciplinary framework application |
| red-team | red team, adversarial, attack plan | Finding weaknesses through adversarial analysis |
| steelman | steelman, steel man, strongest argument | Strengthening opposing views before countering |
| probabilistic-thinking | probabilistic, probability, uncertainty | Decision-making under uncertainty |
| analogical-reasoning | analogical, analogy, map from known | Learning from parallel domains through mapping |
| counterfactual-thinking | counterfactual, "what if", alternative history | Exploring alternative scenarios and outcomes |
| opportunity-cost | opportunity cost, trade-off, alternative forgone | Evaluating true costs including foregone alternatives |
| premortem | premortem, assume failure,事前验尸

README.md

# 🧠 Thinking Frameworks for OpenClaw

20 human thinking frameworks adapted for OpenClaw agents — deep analysis tools for complex decisions, critical thinking, and strategic planning.

[![ClawHub](https://img.shields.io/badge/ClawHub-thinking--frameworks-blue)](https://clawhub.ai/skills/thinking-frameworks)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 🚀 Quick Install

### Via ClawHub (Recommended)
```bash
npx clawhub@latest install thinking-frameworks
```

### Manual Install
```bash
git clone https://github.com/YOUR_USERNAME/thinking-frameworks.git
cp -r thinking-frameworks ~/.openclaw/workspace-libu/skills/
```

Then restart your OpenClaw session.

## 📖 Available Frameworks

| Framework | Use When |
|-----------|----------|
| **🔍 critical-thinking** | Question assumptions, evaluate evidence, detect biases |
| **🎯 first-principles** | Strip assumptions, rebuild from fundamental truths |
| **🌐 systems-thinking** | Understand feedback loops, emergence, holistic views |
| **💡 design-thinking** | User-centered creative problem solving |
| **🌀 lateral-thinking** | Break patterns, find novel solutions |
| **🎩 six-thinking-hats** | Multi-perspective analysis (White/Red/Black/Yellow/Green/Blue) |
| **🤔 socratic-method** | Deep exploration through progressive questioning |
| **📊 bayesian-thinking** | Update beliefs with new evidence |
| **🔮 second-order-thinking** | Map consequence chains ("And then what?") |
| **🔄 inversion-thinking** | Flip problems, find failure modes |
| **⚖️ dialectical-thinking** | Resolve contradictions (Thesis → Antithesis → Synthesis) |
| **🔎 abductive-reasoning** | Infer best explanations from observations |
| **🧠 mental-models** | Multi-disciplinary cross-validation |
| **🛡️ red-team** | Adversarial attack on your own plans |
| **🏛️ steelman** | Strengthen opposing arguments before countering |
| **🎲 probabilistic-thinking** | Decision-making under uncertainty |
| **🔗 analogical-reasoning** | Learn from parallel domains |
| **⚡ counterfactual-thinking** | Explore "what if" alternative scenarios |
| **💰 opportunity-cost** | Evaluate true costs including foregone alternatives |
| **💀 premortem** | Assume failure, work backwards to prevent it |

## 💬 Usage Examples

### Command Style
```
/thinking critical-thinking Should we switch from REST to GraphQL?
/red-team Our plan to launch in 3 markets simultaneously
/premortem The new pricing model we're about to ship
/first-principles How can we reduce customer support costs by 10x?
```

### Natural Language
```
"Use first principles to analyze this problem: [problem]"
"Red team this plan for me: [plan]"
"Think about this using second-order thinking: [situation]"
"Apply the six thinking hats to this decision: [decision]"
```

### Real-World Examples

**Product Decision:**
```
/thinking premortem Our Q2 feature launch plan
```

**Strategic Planning:**
```
/thinking second-order-thinking What happens i

_meta.json

{
  "ownerId": "kn70yzag7nfv6ybp1dvq6hezss820q23",
  "slug": "thinking-frameworks",
  "version": "1.0.0",
  "publishedAt": 1774358582904
}

references/abductive-reasoning.md

# Abductive Reasoning

**Abductive reasoning** — or "inference to the best explanation" — starts from observations and works backward to the most likely explanation. Unlike deduction (which guarantees truth) or induction (which generalizes from patterns), abduction asks: *"Given what I see, what is the best explanation?"* It's how doctors diagnose, detectives solve cases, and scientists generate hypotheses. Peirce called it the only form of reasoning that produces genuinely new ideas.

---

Analyze the following problem using **abductive reasoning**. Start from the evidence and reason backward to the best explanation.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Catalog the Observations

*What do we actually see? Be precise and comprehensive.*

- List all **relevant observations, facts, data points, and phenomena**.
- For each observation:
  - How **reliable** is it? (Directly observed? Reported? Inferred?)
  - How **precise** is it? (Exact measurement? Rough estimate? Anecdote?)
  - Is it **surprising** or **expected**? (Surprising observations are more informative.)
- What **patterns** exist in the data?
- What **anomalies** stand out — things that don't fit the expected pattern?
- What is **conspicuously absent** — things you'd expect to see but don't?

## Step 2: Generate Candidate Explanations

*What could explain these observations?*

Generate at least **5 candidate explanations** (hypotheses), ranging from mundane to creative:

1. **The obvious explanation** — the first thing that comes to mind
2. **The conventional expert explanation** — what a domain expert would say
3. **The systemic explanation** — the root cause, not the proximate cause
4. **The unconventional explanation** — something outside the normal frame
5. **The null explanation** — maybe nothing unusual is happening (coincidence, noise, base rates)

For each, briefly state the mechanism: *How would this explanation produce the observations we see?*

## Step 3: Evaluate Explanatory Power

For each candidate explanation, assess:

### Coverage
- Does it explain **all** the observations, or only some?
- Does it explain the **anomalies** and surprises?
- Does it account for what's **absent** as well as what's present?

### Precision
- Does it make **specific, testable predictions** beyond what we already know?
- Or is it vague enough to explain almost anything? (A bad sign — "just-so stories")

### Simplicity (Parsimony)
- How many **unsupported assumptions** does it require?
- Does it invoke **special mechanisms** or entities beyond what's necessary?
- Occam's Razor: all else equal, prefer the simpler explanation.

### Consistency
- Is it **consistent with known facts** and established science?
- Does it **contradict** any reliable evidence?
- Does it cohere with what we know about **how the world works**?

### Analogy
- Is there **precedent** — has this type of explanation been correct in similar situations before?

### Fertility
- Does it **open up new questions** and resea

references/analogical-reasoning.md

# Analogical Reasoning

**Analogical reasoning** transfers knowledge from a familiar domain (the "source") to an unfamiliar one (the "target") by identifying structural similarities. It's how humans naturally make sense of the new — by connecting it to the known. Used brilliantly by scientists (Rutherford: atom is like a solar system), entrepreneurs (Uber for X), and legal scholars (case law precedent). But analogies can also mislead when surface similarities mask deep structural differences. The key is knowing when the mapping holds and when it breaks.

---

Analyze the following problem using **analogical reasoning**. Find illuminating parallels, map them carefully, and extract transferable insights — while being honest about where the analogy breaks down.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Understand the Target Domain

*First, deeply understand the problem you're trying to solve.*

- What are the **key elements** of this problem? (Actors, relationships, dynamics, constraints, goals)
- What makes this problem **hard**? Where is the core difficulty?
- What is **unknown or uncertain** about this domain?
- What **structure** underlies the problem? (Causal relationships, feedback loops, trade-offs)
- Temporarily set aside domain-specific details — focus on the **abstract structure**.

## Step 2: Generate Source Analogies

*Find domains that share structural features with your target.*

Search broadly across domains. For each, briefly state the analogy:

### Near Analogies (same general field)
- What **similar problem in a related domain** has been solved before?
- What does the nearest competitor or adjacent industry do?

### Far Analogies (completely different fields)
- **Nature/Biology**: What organism, ecosystem, or evolutionary process mirrors this?
- **History**: What historical event or era parallels this situation?
- **Engineering/Physics**: What physical system behaves similarly?
- **Games/Sports**: What game or sporting strategy has this structure?
- **Medicine**: What medical condition or treatment protocol is analogous?
- **Military**: What military strategy or campaign matches?
- **Art/Music**: What creative process or composition mirrors this?
- **Economics**: What market or economic phenomenon has the same dynamics?

Generate at least **5 source analogies**, with at least 2 from distant domains. Far analogies are often more creative and insightful than near ones.

## Step 3: Deep Mapping — Structure the Best Analogies

For the **top 3 most promising analogies**, perform a detailed structural mapping:

### Analogy: [Source Domain] → [Target Domain]

| Source Element | Target Element | Mapping Strength |
|---|---|---|
| [Actor/component in source] | [Corresponding actor in target] | Strong/Moderate/Weak |
| [Relationship in source] | [Corresponding relationship] | Strong/Moderate/Weak |
| [Dynamic/process in source] | [Corresponding dynamic] | Strong/Moderate/Weak |
| [Constraint in source] | [Corresponding constraint] |

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