Introduced in v16.6.0

The Persona Questionnaire system maps behavioral questions to trait values. It creates differentiated profiles instead of defaulting traits to 0.5 (neutral baseline).

Why This Matters

AI-generated profiles often default many traits to 0.5. This creates "average" personas that miss real user diversity.

The questionnaire system:

Maps behavioral answers to trait values (0-1 scale)
Uses 5 distinct behavioral levels per trait (0, 0.25, 0.5, 0.75, 1.0)
Applies research-backed correlations between traits
Provides behavioral descriptions based on academic research

Quick Start

CLI Usage

# Interactive 8-question questionnaire
npx cbrowser persona-questionnaire start

# Comprehensive 26-trait questionnaire
npx cbrowser persona-questionnaire start --comprehensive --name "my-persona"

# Look up trait behavior at specific value
npx cbrowser persona-questionnaire lookup --trait patience --value 0.25

# List all available traits
npx cbrowser persona-questionnaire list-traits

MCP Tools

Three MCP tools are available for Claude integration:

Tool	Description
`persona_questionnaire_get`	Generate questionnaire questions
`persona_questionnaire_build`	Build trait profile from answers
`persona_trait_lookup`	Look up behaviors for trait value

Programmatic Usage

import {
  generatePersonaQuestionnaire,
  buildTraitsFromAnswers,
  getTraitBehaviors,
  getTraitLabel,
} from 'cbrowser';

// Generate questionnaire
const questionnaire = generatePersonaQuestionnaire({ comprehensive: false });

// Build traits from answers
const traits = buildTraitsFromAnswers({
  patience: 0.25,
  riskTolerance: 0.75,
  curiosity: 1.0,
});

// Look up specific trait behavior
const behaviors = getTraitBehaviors('patience', 0.25);
console.log(behaviors.label);       // "Low"
console.log(behaviors.behaviors);   // ["Gets frustrated quickly", ...]

The 25 Cognitive Traits

The trait system models full user behavior:

Trait	Research Basis	What It Models
patience	UX research	Time before abandoning on friction
riskTolerance	Prospect theory	Willingness to try unfamiliar actions
comprehension	Cognitive load theory	UI pattern understanding
persistence	Motivation research	Retry behavior after failures
curiosity	Exploration theory	Feature discovery tendency
workingMemory	Cognitive psychology	Multi-step task handling
readingTendency	Eye-tracking studies	Text consumption behavior
resilience	Positive psychology	Bounce-back from errors
selfEfficacy	Bandura (1977)	Belief in problem-solving ability
satisficing	Simon (1956)	Accept "good enough" vs optimize
trustCalibration	Fogg (2003)	Trust in unfamiliar interfaces
interruptRecovery	Mark et al. (2005)	Resume after distractions
decisionFatigue	Baumeister	Decision quality over time
internalAttribution	Attribution theory	Self-blame for failures
externalAttribution	Attribution theory	System-blame for failures
techSavviness	Digital literacy	Familiarity with UI patterns
attentionSpan	Attention research	Focus duration
impulsivity	Behavioral psychology	Quick vs deliberate actions
errorRecovery	Human factors	Speed of correcting mistakes
visualProcessing	Cognitive science	Image vs text preference
spatialMemory	Navigation research	UI layout recall
patternRecognition	Expertise research	UI element identification
riskPerception	Risk psychology	Danger assessment
socialProof	Cialdini (1984)	Influence of others' actions
authorityTrust	Trust research	Trust in official sources

Trait Reference Matrix

Each trait has 5 behavioral levels with specific descriptions:

Example: Patience Trait

Value	Label	Behaviors
0	Very Low	Abandons at first sign of friction, expects instant results
0.25	Low	Gets frustrated quickly, skips slow-loading content
0.5	Moderate	Waits reasonable time, tolerates some friction
0.75	High	Patient with delays, reads loading messages
1.0	Very High	Extremely patient, waits through any delay

Example: Self-Efficacy Trait

Value	Label	Behaviors
0	Very Low	"I can't do this", gives up immediately on challenge
0.25	Low	Doubts ability, abandons 40% faster than average
0.5	Moderate	Normal confidence, persists through minor challenges
0.75	High	Confident problem-solver, sees obstacles as puzzles
1.0	Very High	"I'll figure this out", never attributes failure to self

Trait Correlations

The system automatically applies research-backed correlations:

Primary Trait	Correlated Trait	Relationship
Low selfEfficacy	High internalAttribution	Self-blame for failures
Low selfEfficacy	Low resilience	Slower bounce-back
Low patience	Low resilience	Less tolerance for errors
High curiosity	Higher exploration	More feature discovery
Low trustCalibration	Longer evaluation time	Scrutinizes CTAs

Questionnaire Formats

Quick Questionnaire (8 traits)

Core traits for basic persona differentiation:

patience, riskTolerance, comprehension, persistence
curiosity, workingMemory, readingTendency, resilience

Comprehensive Questionnaire (26 traits)

All cognitive traits for detailed behavioral modeling.

Custom Selection

Request specific traits:

const questionnaire = generatePersonaQuestionnaire({
  traits: ['patience', 'selfEfficacy', 'trustCalibration'],
});

AskUserQuestion Integration

For Claude sessions, use the formatForAskUserQuestion function:

import { formatForAskUserQuestion, generatePersonaQuestionnaire } from 'cbrowser';

const questionnaire = generatePersonaQuestionnaire({ comprehensive: false });
const askUserFormat = formatForAskUserQuestion(questionnaire.questions);

// Returns format compatible with Claude's AskUserQuestion tool

CLI Interactive Mode

$ npx cbrowser persona-questionnaire start --name "anxious-newbie"

=== CBrowser Persona Questionnaire ===
Creating persona: anxious-newbie (8 core traits)

[1/8] Patience
How does this user handle waiting for pages or actions?
  1. Very impatient - abandons at first delay
  2. Somewhat impatient - frustrated by normal load times
  3. Average - tolerates reasonable waits
  4. Patient - waits through delays calmly
  5. Very patient - unlimited patience
> 2

...

=== Persona Created ===
Saved to: ~/.cbrowser/personas/anxious-newbie.json

Using Custom Personas

After creating a persona:

import { runCognitiveJourney } from 'cbrowser';

const result = await runCognitiveJourney({
  persona: 'anxious-newbie',  // Uses saved persona
  startUrl: 'https://example.com',
  goal: 'complete signup',
});

Or with inline traits:

const customTraits = buildTraitsFromAnswers({
  patience: 0.25,
  selfEfficacy: 0.25,
  trustCalibration: 0.25,
});

const result = await runCognitiveJourney({
  persona: 'custom',
  customTraits,
  startUrl: 'https://example.com',
  goal: 'complete signup',
});

Research Citations

The trait system is grounded in academic research:

Bandura, A. (1977) - Self-Efficacy: Toward a Unifying Theory of Behavioral Change
Kahneman, D. & Tversky, A. - Prospect Theory and Decision Making
Simon, H. (1956) - Satisficing and Administrative Behavior
Fogg, B.J. (2003) - Persuasive Technology and Trust
Mark, G. et al. (2005) - No Task Left Behind: Interrupt Recovery
Baumeister, R. - Ego Depletion and Decision Fatigue
Nielsen, J. - Usability Engineering and User Behavior
Cialdini, R. (1984) - Influence: The Psychology of Persuasion

Persona Questionnaire