Human Behavior OS

Analyzes and predicts human behavior using a 7-module system (Needs, Attention, Trust, Decision, Emotion, Spread, Prediction). Invoke when user needs to understand user behavior, optimize conversion, design growth strategies, craft persuasive content, analyze why users act or don't act, improve product adoption, or build behavior-driven campaigns across any domain.

Human Behavior OS (用户行为系统)

Core Mission

Identify, understand, predict, and influence human behavior.

This system does NOT study products, platforms, or traffic.

It studies ONLY: How humans make decisions when facing information, choices, risks, desires, and actions.

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Master Formula

Behavior = Need × Perceived Value × Trust ÷ Action Friction

Factor	Direction	Effect
Need intensity	Higher →	More likely to act
Perceived value	Higher →	More likely to act
Trust level	Higher →	More likely to act
Action friction	Lower →	More likely to act

ALL analysis must reference this formula. No module works in isolation.

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How to Use This Skill

Quick Start (3 Steps)

1. Tell me your scenario — e.g., "Why aren't users upgrading to Pro?", "How to make this landing page convert better?", "Why did this content go viral?"
2. I run the 7-module analysis — Each module produces a structured output
3. You get a complete Behavior Profile — With actionable recommendations

What You Can Ask

• Analysis: "Analyze why users abandon cart at checkout"
• Design: "Design a behavior-driven onboarding flow"
• Optimize: "How to increase referral rate by 3x?"
• Predict: "What will users do after the free trial ends?"
• Create: "Write copy that triggers sharing behavior"
• Diagnose: "Why isn't our content getting engagement?"
• Strategy: "Build a growth strategy for a B2B SaaS product"

Domains This Skill Covers

This skill is domain-agnostic. It works for:

• E-commerce & Retail
• SaaS & B2B
• Content & Media
• Education & Training
• Healthcare & Wellness
• Finance & Insurance
• Social & Community
• Gaming & Entertainment
• Public Policy & Social Impact
• Personal Development & Coaching

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The 7 Modules

Module 1: Need System (需求系统)

Core Question: What does the user truly want?

The 5-Layer Need Hierarchy

Layer	Needs	Examples	Trigger Phrases
L1 Survival	Safety, Health, Money, Stability	"I need to survive", "Can't afford to lose"	Must-have, non-negotiable
L2 Efficiency	Save time, Save effort, Save money	"Too slow", "Too complicated", "Waste of money"	Pain-point driven
L3 Emotion	Joy, Healing, Relaxation, Companionship, Belonging	"I feel lonely", "I need a break"	Feeling-driven
L4 Identity	Dignity, Excellence, Professionalism, Taste, Respect	"I want to look good", "I'm an expert"	Status-driven
L5 Growth	Become stronger, Progress, Freedom, Self-actualization	"I want to level up", "I want to be free"	Aspiration-driven

Analysis Process

1. Identify the primary need layer — Which layer is dominant?
2. Map secondary needs — What supporting needs exist?
3. Assess need intensity — Scale 1-10 for each active need
4. Find the gap — Current state vs. desired state

Output: Need Map

## Need Map
- **Primary Need**: [Layer + Specific Need] (Intensity: X/10)
- **Secondary Needs**: [List with intensities]
- **Need Gap**: [Current → Desired]
- **Need Trigger**: [What event/situation activates this need]

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Module 2: Attention System (注意力系统)

Core Question: Why does the user stop and look?

9 Attention Triggers

Trigger	Mechanism	Best For	Example
Novelty	Something never seen before	New products, ideas	"The first AI that writes like you"
Contrast	Unexpected juxtaposition	Repositioning, differentiation	"The luxury brand that costs less"
Danger	Threat or risk signal	Security, health, finance	"Your data is exposed"
Benefit	Clear gain promised	Sales, conversion	"Save $500 today"
Curiosity	Information gap	Content, education	"Why 90% of startups fail"
Conflict	Opposing forces	Debate, controversy	"Rich people don't work 9-5"
Result	Proven outcome	Case studies, testimonials	"How I went from 0 to 1M users"
Secret	Hidden knowledge	Exclusive content	"The strategy Amazon doesn't want you to know"
Story	Narrative arc	Brand, emotional connection	"She was about to give up, then..."

Analysis Process

1. Audit current attention hooks — What's currently grabbing attention?
2. Score each trigger potential — Which triggers fit this scenario?
3. Design the attention sequence — First 3 seconds, first 30 seconds, first 3 minutes
4. Test pattern interrupt — Does it break the user's autopilot?

Output: Attention Trigger Library

## Attention Trigger Library
- **Primary Trigger**: [Trigger name] — [Why it works here]
- **Secondary Triggers**: [List with rationale]
- **Attention Arc**: [3-second hook → 30-second deepen → 3-minute commit]
- **Pattern Interrupt Score**: X/10

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Module 3: Trust System (信任系统)

Core Question: Why does the user believe?

7 Trust Sources

Source	Mechanism	Speed	Durability	Example
Facts	Verifiable data	Medium	High	"3,000+ peer-reviewed studies"
Evidence	Visible proof	Fast	High	Screenshots, recordings, demos
Cases	Specific examples	Fast	Medium	"Company X increased revenue 40%"
Authority	Expert endorsement	Fast	Medium	"Recommended by Dr. Smith"
Experience	First-hand trial	Slow	Very High	Free trial, sample, test drive
Social Proof	Others' validation	Fast	Medium	"50,000+ users", reviews, ratings
Consistency	Track record over time	Slow	Very High	"10 years of continuous updates"

Trust Building Sequence

Instant Trust (0-5s) → Authority + Social Proof
Rapid Trust (5-60s) → Evidence + Cases
Deep Trust (1-30min) → Experience + Consistency
Lasting Trust (30d+) → Facts + Consistency

Analysis Process

1. Audit current trust assets — What trust signals exist?
2. Identify trust gaps — Where does trust break down?
3. Design trust sequence — Match trust sources to user journey stages
4. Calculate Trust Score — 0-100 scale

Output: Trust Building Map

## Trust Building Map
- **Current Trust Score**: X/100
- **Trust Gaps**: [Where trust breaks down]
- **Trust Sequence**: [Stage → Trust Source → Implementation]
- **Trust Multipliers**: [What would 2x trust quickly]

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Module 4: Decision System (决策系统)

Core Question: Why does the user act?

Decision Formula

Action occurs when: Expected Gain > Expected Cost
Where:
  Expected Gain = Perceived Benefit × Probability of Success
  Expected Cost = Price + Time + Effort + Risk + Opportunity Cost

5 Decision Factors

Factor	Questions	Optimization
Gain	What do I get?	Make benefits concrete, measurable, immediate
Risk	What could go wrong?	Guarantees, reversibility, social proof
Cost	What do I pay?	Free trials, bundles, payment plans
Time	How long until I see results?	Quick wins, progress indicators
Complexity	How hard is it to start?	Simplify, template, guided flow

Decision Friction Points

1. Analysis paralysis — Too many options
2. Status quo bias — Change feels risky
3. Loss aversion — Fear of losing outweighs desire to gain
4. Present bias — Future rewards feel less valuable
5. Social risk — "What will others think?"

Analysis Process

1. Map the decision equation — Gains vs. Costs for this user
2. Identify the #1 friction point — What stops the decision?
3. Design the decision nudge — How to tip the equation
4. Reduce to one choice — Simplify the decision

Output: Decision Impact Model

## Decision Impact Model
- **Expected Gain Score**: X/10 — [Breakdown]
- **Expected Cost Score**: X/10 — [Breakdown]
- **Decision Ratio**: Gain:Cost = X:Y
- **#1 Friction Point**: [Specific blocker]
- **Decision Nudge**: [Specific intervention to tip the equation]

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Module 5: Emotion System (情绪系统)

Core Question: How does emotion drive behavior?

8 High-Frequency Emotions

Emotion	Behavioral Effect	Trigger	Use When
Fear	Avoid/Protect	Threat, uncertainty	Security, insurance, urgency
Anticipation	Pursue/Prepare	Upcoming event, possibility	Product launches, pre-sales
Surprise	Stop/Share	Unexpected outcome	Viral content, announcements
Envy	Desire/Compete	Others' success	Social products, luxury
Aspiration	Strive/Invest	Ideal self vision	Education, self-improvement
Relief	Commit/Stay	Problem solved	After-purchase, onboarding
Curiosity	Explore/Learn	Information gap	Content, discovery
Achievement	Share/Repeat	Goal accomplished	Gamification, milestones

Emotion-Behavior Mapping

Fear → Urgent action (buy insurance, install security)
Anticipation → Pre-commitment (sign up, waitlist)
Surprise → Sharing (tell friends, post online)
Envy → Comparison purchase (upgrade, buy)
Aspiration → Investment (course, premium)
Relief → Loyalty (stay, renew)
Curiosity → Engagement (click, read, explore)
Achievement → Advocacy (review, refer)

Analysis Process

1. Identify the dominant emotion — What is the user feeling?
2. Map the emotion sequence — What emotion leads to what action?
3. Design the emotional arc — Beginning → Middle → End
4. Amplify or dampen — Increase desired emotions, reduce blockers

Output: Emotion Drive Model

## Emotion Drive Model
- **Primary Emotion**: [Emotion] → [Target Behavior]
- **Emotion Sequence**: [Emotion A → Emotion B → Action]
- **Emotional Arc**: [Start: X → Middle: Y → End: Z]
- **Emotion Amplifiers**: [What intensifies the target emotion]

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Module 6: Spread System (传播系统)

Core Question: Why does the user share?

6 Sharing Motives

Motive	Mechanism	Content Type	Example
Self-expression	"This is who I am"	Opinions, values, taste	Sharing a political article
Helping others	"This is useful"	Tips, guides, warnings	"Save this recipe!"
Social validation	"Please acknowledge me"	Achievements, milestones	"Just finished a marathon!"
Identity building	"This makes me look good"	Expertise, exclusivity	Sharing an industry report
Emotional release	"I can't hold this in"	Funny, outrage, awe	Viral memes
Social currency	"I know something you don't"	Insider info, early access	"Beta testing the new iPhone"

Shareability Formula

Shareability = (Emotional Intensity × Relevance to Identity) ÷ Sharing Friction

Analysis Process

1. Identify the sharing motive — Why would someone share this?
2. Assess shareability — Score each dimension
3. Reduce sharing friction — One-click share, pre-written messages
4. Design the share trigger — What moment triggers sharing?

Output: Spread Trigger

## Spread Trigger
- **Primary Sharing Motive**: [Motive] — [Why it applies]
- **Shareability Score**: X/10
- **Share Trigger Moment**: [When users are most likely to share]
- **Sharing Friction Points**: [What makes sharing hard]
- **Viral Coefficient Estimate**: K = X.X

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Module 7: Behavior Prediction System (行为预测系统)

Core Question: What happens next?

8 Predictable Behaviors

Behavior	Predictors	Lead Signals	Intervention Window
Stay	High value, low friction	Session length, return rate	First 3 visits
Leave	Unmet need, high friction	Bounce rate, time on page	Before exit intent
Purchase	Clear gain, high trust	Add to cart, price comparison	Checkout flow
Follow/Subscribe	Anticipation, identity	Content engagement, profile visits	After value delivery
Comment	Emotional trigger, social need	Read time, scroll depth	After content consumption
Share	High emotion, identity boost	Share button hover, screenshot	Peak emotional moment
Repurchase	Satisfaction + new need	Usage frequency, support tickets	End of subscription period
Churn	Declining value, competing option	Login frequency drop, feature disuse	2-4 weeks before churn

Prediction Framework

Step 1: Identify current behavior stage
Step 2: Assess need satisfaction level
Step 3: Measure friction accumulation
Step 4: Predict next behavior (probability-weighted)
Step 5: Design intervention for desired outcome

Analysis Process

1. Map the user journey — Where is the user now?
2. Score behavior signals — What data indicates intent?
3. Calculate transition probabilities — What happens next?
4. Design the intervention — How to steer toward desired behavior?

Output: Behavior Prediction Report

## Behavior Prediction Report
- **Current Stage**: [Where the user is]
- **Next Likely Behavior**: [Predicted action] (Probability: X%)
- **Alternative Behaviors**: [Other possibilities with probabilities]
- **Key Signals**: [What to watch for]
- **Intervention Window**: [When to act]
- **Recommended Intervention**: [Specific action to take]

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Complete Output: User Behavior Profile

Every analysis MUST produce this final synthesis:

# User Behavior Profile

## 1. Need Map
[From Module 1]

## 2. Emotion Map
[From Module 5]

## 3. Trust Map
[From Module 3]

## 4. Decision Path
[From Module 4]

## 5. Spread Path
[From Module 6]

## 6. Behavior Prediction
[From Module 7]

## 7. Action Recommendations
### Quick Wins (This Week)
- [Action 1]
- [Action 2]

### Medium-Term (This Month)
- [Action 1]
- [Action 2]

### Strategic (This Quarter)
- [Action 1]
- [Action 2]

## 8. Key Metrics to Track
- [Metric 1]: [Target value]
- [Metric 2]: [Target value]

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Analysis Modes

Mode A: Full Analysis (Default)

Run all 7 modules → Complete Behavior Profile Use when: Comprehensive understanding needed, new project, major decisions

Mode B: Targeted Analysis

Run specific modules only Use when: Focused question, quick optimization, specific bottleneck

Mode C: Comparative Analysis

Run analysis for 2+ user segments or scenarios, compare Use when: A/B testing, segment strategy, competitive analysis

Mode D: Predictive Analysis

Focus on Module 7 with supporting data from other modules Use when: Churn prevention, conversion optimization, growth forecasting

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Quality Standards

1. Specific over vague — "Users feel frustrated during checkout" > "Users have negative emotions"
2. Data-backed when possible — Always ask for data if available
3. Actionable always — Every insight must lead to at least one action
4. Prioritized recommendations — Rank by impact × effort
5. Cross-module thinking — Never analyze a module in isolation

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Sub-Scenario Routing

When the user's request matches a specific domain or scenario, Read the corresponding reference file from references/ in this skill's directory and follow the instructions within. Do NOT attempt to recall the reference content from memory — always load the file to ensure the full, up-to-date instructions are applied.

Routing Table

Reference	File	Route When
ecommerce-conversion	references/ecommerce-conversion.md	Task involves e-commerce optimization, cart abandonment, purchase behavior, pricing strategy
content-viral-spread	references/content-viral-spread.md	Task involves content marketing, viral mechanics, social media engagement, sharing optimization
saas-growth-retention	references/saas-growth-retention.md	Task involves SaaS metrics, user onboarding, retention, churn prevention, subscription optimization
persuasive-copywriting	references/persuasive-copywriting.md	Task involves writing copy, landing pages, email campaigns, ad creative, conversion copy
product-adoption	references/product-adoption.md	Task involves new product launch, feature adoption, habit formation, user activation
community-engagement	references/community-engagement.md	Task involves community building, user engagement, social dynamics, group behavior