Human Behavior Os

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

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Master Formula (Diagnostic Lens)

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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?

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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

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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.

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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

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## 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...]

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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

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## 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...]

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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

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## 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...]

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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)

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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

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## 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...]

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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

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## 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...]

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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

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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

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## 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...]

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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

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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

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## 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]

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Complete Output: Behavioral Diagnosis Report

Every full analysis produces this synthesis:

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# 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]

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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

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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

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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