Human Behavior Os

SKILL.md

---
name: "human-behavior-os"
description: "Diagnoses why specific users behave the way they do using a 7-module analytical framework (Needs, Attention, Trust, Decision, Emotion, Spread, Prediction). Outputs diagnostic analysis and behavioral insights ONLY — never generates persuasive copy, manipulation tactics, or growth-hacking instructions. Invoke ONLY when the user provides a specific scenario or case study and explicitly asks to DIAGNOSE, ANALYZE, or UNDERSTAND root causes of observed behavior patterns. Do NOT invoke for copywriting, campaign design, persuasion strategy, or any request to influence or change user behavior."
---

# Human Behavior OS (行为诊断系统)

## Core Mission

**Understand and explain human behavior.**

This system diagnoses WHY people make decisions. It studies:

- How humans process information and choices
- What drives observable behavior patterns
- Why specific actions occur (or don't occur) in defined scenarios

**This system does NOT:**
- Generate persuasive content or copy
- Design manipulation tactics or "nudges"
- Provide actionable instructions for influencing others
- Execute campaigns or automate outreach

---

## Master Formula (Diagnostic Lens)

```
Behavior = Need × Perceived Value × Trust ÷ Action Friction
```

Use this formula to **explain** observed behavior, not to engineer it.

| Factor | Diagnostic Question |
|--------|---------------------|
| Need intensity | What need is the user trying to satisfy? |
| Perceived value | Does the user perceive value in the available options? |
| Trust level | What trust signals exist or are missing? |
| Action friction | What barriers prevent action? |

---

## What This Skill IS vs. Is NOT

| ✅ This Skill DOES | ❌ This Skill Does NOT |
|---|---|
| Diagnose why users abandon checkout | Write checkout recovery emails |
| Analyze what made content go viral | Design viral content templates |
| Explain low conversion root causes | Create landing page copy |
| Map decision friction points | Design dark patterns or nudges |
| Predict likely next behaviors | Engineer behavior change interventions |
| Identify trust gaps in a funnel | Forge fake testimonials or urgency |
| Classify emotion-behavior links | Amplify fear, envy, or FOMO |

---

## Mandatory Output Format

Every analysis MUST follow this 4-step diagnostic structure:

### Step 1: Observation
What behavior is observed? State facts only — no interpretation.

### Step 2: Hypothesis
What NEEDS, TRUST, FRICTION, or EMOTION factors could explain this behavior?

### Step 3: Evidence Check
What data supports or refutes each hypothesis? Flag assumptions clearly.

### Step 4: Diagnostic Conclusion
What is the most likely root cause? Present as testable hypothesis, not fact.

---

## The 7 Diagnostic Modules

### Module 1: Diagnosing Needs (需求诊断)

**Core Question: What need is the user trying to satisfy?**

#### The 5-Layer Need Framework

| Layer | Needs | Behavioral Signal |
|-------|-------|-------------------|
| **L1 Survival** | Safety, health, money, stability | Risk-averse choices, security-seeking |
| **L2 Efficiency** | Save time, effort, money | Complaints about slowness, complexity |
| **L3 Emotion** | Joy, healing, belonging | Feeling-driven decisions |
| **L4 Identity** | Dignity, status, taste | Status-signaling purchases |
| **L5 Growth** | Self-improvement, freedom | Investment in learning/skill-building |

#### Diagnostic Process

1. **Identify which need layer(s)** are active in the observed behavior
2. **Assess intensity** of each active need (1-10)
3. **Map the gap** between current state and desired state
4. **Check for conflicting needs** (e.g., L1 safety vs. L4 status)

#### Output: Need Diagnosis

```markdown
## Need Diagnosis
- **Active Need Layer(s)**: [Layer(s) with evidence]
- **Need Intensity**: X/10 — [Supporting observations]
- **Need Gap**: [Current state] → [Desired state] (User's perspective)
- **Conflict Detection**: [Any competing needs identified]
- **Diagnostic Hypothesis**: [The user's behavior is driven by...]
```

---

### Module 2: Diagnosing Attention Patterns (注意力诊断)

**Core Question: What captured (or failed to capture) the user's attention?**

#### Attention Trigger Reference Table

Use this table to **classify** what attention mechanism is at play, not to design one.

| Trigger | Mechanism | Diagnostic Use |
|---------|-----------|---------------|
| **Novelty** | Something new/unexpected | Did novelty drive initial engagement? |
| **Contrast** | Unexpected juxtaposition | Is contrast present in the observed content? |
| **Danger/Risk** | Threat signal | Is risk language driving attention or causing avoidance? |
| **Benefit** | Clear gain promised | Is benefit clarity a factor in engagement level? |
| **Curiosity** | Information gap | Did curiosity drive click-through or exploration? |
| **Conflict** | Opposing forces | Is controversy a factor in attention capture? |
| **Social Proof** | Others' validation | Are social signals present and impactful? |
| **Story** | Narrative arc | Is narrative structure driving sustained attention? |

#### Diagnostic Process

1. **Classify** which attention mechanisms are present in the scenario
2. **Assess effectiveness** — Which mechanisms actually drove observed behavior?
3. **Identify gaps** — What attention drivers are missing that could explain low engagement?

#### Output: Attention Diagnosis

```markdown
## Attention Diagnosis
- **Active Triggers**: [Which mechanisms are present]
- **Effective Triggers**: [Which ones actually drove observed behavior]
- **Attention Gap**: [Missing drivers that may explain low engagement]
- **Hypothesis**: [Attention pattern suggests...]
```

---

### Module 3: Diagnosing Trust Dynamics (信任诊断)

**Core Question: Where does trust exist, and where does it break down?**

#### Trust Source Reference

| Source | Mechanism | Diagnostic Question |
|--------|-----------|---------------------|
| **Facts** | Verifiable data | Are claims verifiable? |
| **Evidence** | Visible proof | Is proof (screenshots, demos) available? |
| **Cases** | Specific examples | Are there relevant success stories? |
| **Authority** | Expert endorsement | Is authority credible to this audience? |
| **Experience** | First-hand trial | Can users try before committing? |
| **Social Proof** | Others' validation | Are reviews/ratings present and authentic? |
| **Consistency** | Track record | Is there evidence of reliability over time? |

#### Diagnostic Process

1. **Audit existing trust signals** in the scenario
2. **Identify trust breakdown points** — Where did users lose confidence?
3. **Classify trust barrier type** — Missing evidence? Inconsistent messaging? Authority mismatch?

#### Output: Trust Diagnosis

```markdown
## Trust Diagnosis
- **Trust Signals Present**: [List what exists]
- **Trust Breakdown Point(s)**: [Where confidence was lost]
- **Barrier Classification**: [Type of trust failure]
- **Hypothesis**: [Trust dynamic indicates...]
```

---

### Module 4: Diagnosing Decision Barriers (决策障碍诊断)

**Core Question: What is preventing the user from taking action?**

#### Decision Equation (Analytical Lens)

```
Action occurs when: Expected Gain > Expected Cost
```
Use this to **explain** past decisions, not to engineer future ones.

#### Decision Friction Taxonomy

| Friction Type | Diagnostic Indicator |
|--------------|---------------------|
| **Analysis paralysis** | Too many options, no clear choice |
| **Status quo bias** | User defaults to "do nothing" |
| **Loss aversion** | Fear of loss outweighs potential gain |
| **Present bias** | Future benefits feel less valuable than current comfort |
| **Social risk** | Concern about others' judgment |

#### Diagnostic Process

1. **Map gains and costs** as the user perceives them
2. **Identify #1 friction point** — What is the primary blocker?
3. **Classify friction type** using the taxonomy above

#### Output: Decision Barrier Diagnosis

```markdown
## Decision Barrier Diagnosis
- **Perceived Gains**: [What user stands to gain]
- **Perceived Costs**: [What user must give up (money, time, effort, risk)]
- **#1 Friction Point**: [Specific blocker with evidence]
- **Friction Classification**: [Type from taxonomy]
- **Diagnostic Hypothesis**: [The user's non-action is explained by...]
```

---

### Module 5: Diagnosing Emotional Drivers (情绪驱动诊断)

**Core Question: What emotions are associated with the observed behavior?**

> ⚠️ **Critical Guardrail**: This module identifies and classifies emotional factors in observed behavior. It does NOT recommend emotional amplification, manipulation, or exploitation. Any output involving Fear, Envy, Urgency, or similar emotions MUST be purely descriptive/diagnostic — never prescriptive.

#### Emotion Reference Table (For Classification Only)

| Emotion | Behavioral Effect | When It Appears |
|---------|-------------------|-----------------|
| **Fear** | Avoidance / protective action | Threat, uncertainty scenarios |
| **Anticipation** | Preparation / pre-commitment | Upcoming events, launches |
| **Surprise** | Stopping / sharing | Unexpected outcomes |
| **Aspiration** | Investment / striving | Self-improvement contexts |
| **Relief** | Commitment / loyalty | Problem-resolution moments |
| **Curiosity** | Exploration / engagement | Information-gap scenarios |
| **Achievement** | Sharing / repetition | Goal-completion moments |

#### Diagnostic Process

1. **Identify dominant emotion(s)** in the scenario — based on observed behavior, not assumption
2. **Map emotion-to-action link** — What behavior does this emotion correlate with?
3. **Check for factual basis** — Is the emotion response proportionate to actual circumstances?

#### Output: Emotion Diagnosis

```markdown
## Emotion Diagnosis
- **Dominant Emotion(s)**: [With behavioral evidence]
- **Emotion-Behavior Link**: [How emotion correlates to observed action]
- **Proportionality Check**: [Is emotional response proportionate to stimulus?]
- **Diagnostic Hypothesis**: [The observed behavior is emotionally driven by...]
```

---

### Module 6: Diagnosing Spread Patterns (传播模式诊断)

**Core Question: Why did (or didn't) this content/idea spread?**

#### Sharing Motive Classification

Use this to **explain** sharing behavior, not to manufacture it.

| Motive | Mechanism | Diagnostic Marker |
|--------|-----------|------------------|
| **Self-expression** | "This reflects who I am" | Opinion/value sharing |
| **Helping others** | "This is useful" | Tips, guides, warnings |
| **Social validation** | "Acknowledge me" | Achievement sharing |
| **Identity signaling** | "I'm knowledgeable" | Expert content sharing |
| **Emotional release** | "I must react" | Strong emotional content |
| **Social currency** | "I know first" | Exclusive/early info |

#### Spreadability Factors

```
Spread Potential = (Emotional Intensity × Identity Relevance) ÷ Sharing Friction
```
Use this formula to **assess** why something spread or failed to spread.

#### Diagnostic Process

1. **Classify sharing motive** — Which motive(s) explain observed sharing (or lack thereof)?
2. **Score spread dimensions** — Emotional intensity, identity relevance, friction
3. **Identify spread blockers** — What prevented sharing if it didn't happen?

#### Output: Spread Pattern Diagnosis

```markdown
## Spread Pattern Diagnosis
- **Sharing Motive(s) Active**: [Classified from observed behavior]
- **Spread Dimension Scores**: Emotion X/10 · Identity Y/10 · Friction Z/10
- **Spread Blocker(s)**: [What prevented sharing, if applicable]
- **Diagnostic Hypothesis**: [Spread pattern indicates...]
```

---

### Module 7: Behavior Prediction (行为预测)

**Core Question: Given the current state, what is the user likely to do next?**

#### Observable Behavior Transitions

| Behavior | Lead Indicators | Prediction Confidence |
|----------|----------------|----------------------|
| **Stay engaged** | High session length, return visits | High (if value/friction favorable) |
| **Leave / churn** | Declining login, feature disuse | Medium-High (if trend established) |
| **Purchase / convert** | Cart addition, price comparison | Medium (depends on friction resolution) |
| **Share / refer** | Peak emotional moment, screenshot behavior | Low-Medium (requires trigger event) |

#### Prediction Framework

```
Step 1: Establish current behavior baseline (from observable data)
Step 2: Assess need satisfaction trajectory (improving, stable, declining?)
Step 3: Measure friction accumulation (new barriers emerging?)
Step 4: Predict next behavior as probability-weighted hypothesis
Step 5: State prediction confidence level and key assumptions
```

> ⚠️ **Constraint**: Predictions are hypotheses based on observable patterns, not certainties. Always state confidence level and assumptions. Never claim predictive accuracy beyond what the data supports.

#### Output: Behavior Prediction Report

```markdown
## Behavior Prediction
- **Current Baseline**: [Observed state with data points]
- **Predicted Next Behavior**: [Most likely action] (Confidence: X%)
- **Alternative Scenarios**: [Other possibilities with probabilities]
- **Key Assumptions**: [What this prediction depends on]
- **Leading Indicators to Watch**: [Signals that confirm or refute prediction]
```

---

## Complete Output: Behavioral Diagnosis Report

Every full analysis produces this synthesis:

```markdown
# Behavioral Diagnosis Report

## Summary
[One-paragraph diagnosis of the core issue]

## Module Findings
1. **Needs**: [From Module 1]
2. **Attention**: [From Module 2]
3. **Trust**: [From Module 3]
4. **Decision Barriers**: [From Module 4]
5. **Emotions**: [From Module 5]
6. **Spread**: [From Module 6]
7. **Prediction**: [From Module 7]

## Primary Root Cause Hypothesis
[Single most likely explanation — stated as testable hypothesis]

## Suggested Investigation Paths
(Not recommendations for action — paths for further data gathering)
- Path A: [What data would confirm/refute the hypothesis]
- Path B: [Alternative hypothesis to investigate]

## Key Metrics to Observe
[Metrics that will validate or invalidate this diagnosis over time]
```

---

## Analysis Modes

### Mode A: Full Diagnosis (Default)
Run all 7 modules → Complete Behavioral Diagnosis Report
Use when: Comprehensive understanding needed, new case analysis

### Mode B: Targeted Diagnosis
Run specific modules only
Use when: Focused question on one dimension (e.g., "Why is trust failing?")

### Mode C: Comparative Diagnosis
Analyze 2+ scenarios or user segments in parallel
Use when: Comparing why Scenario A worked but Scenario B didn't

### Mode D: Predictive Focus
Focus on Module 7 with supporting context from other modules
Use when: Forecasting future behavior from current patterns

---

## Quality Standards

1. **Observation over prescription** — Describe what IS happening, not what SHOULD happen
2. **Hypotheses over answers** — State conclusions as testable propositions, not facts
3. **Evidence-based** — Distinguish clearly between observed data and assumed causes
4. **Flag assumptions** — Every diagnosis should explicitly state its assumptions
5. **Cross-module coherence** — Findings across modules should be consistent; flag contradictions
6. **No manipulation language** — Never use terms like "nudge", "trigger", "exploit", "hack", "engineer" in prescriptive contexts

---

## Sub-Scenario Routing

When the user's request matches a specific domain, **Read** the corresponding reference file from `references/` in this skill's directory and apply the diagnostic framework within it. All reference files follow the same diagnostic-only standard — they provide domain-specific lenses for behavioral analysis, not operational playbooks.

### Routing Table

| Domain | Reference File | Route When User Asks About |
|--------|---------------|---------------------------|
| E-commerce purchase behavior | `references/ecommerce-conversion.md` | Why users do/don't complete purchases, cart abandonment analysis, pricing perception |
| Content spread dynamics | `references/content-viral-spread.md` | Why content did/didn't spread, sharing pattern analysis |
| SaaS user lifecycle | `references/saas-growth-retention.md` | User engagement trends, retention/churn pattern analysis |
| Copy & communication effect | `references/persuasive-copywriting.md` | Why specific copy performed well/poorly, message reception analysis |
| Product adoption patterns | `references/product-adoption.md` | Feature adoption rates, habit formation observation, activation analysis |
| Community dynamics | `references/community-engagement.md` | Participation patterns, group behavior evolution, engagement distribution |