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@35ffd55
by shingo imotasimota/agent-skills85 stars
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Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. Reads the 'why' from real user operation logs. Works with Field/Echo for persona validation.

Use this Skill: https://skilld.dev/gh/simota/agent-skills/trace

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referencepersona-integration.md

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Persona Integration Patterns

Detailed collaboration patterns with Field and Echo.


Integration Overview

┌──────────────────────────────────────────────────────────┐
│                    PERSONA LIFECYCLE                      │
│                                                          │
│  ┌────────────┐    ┌────────────┐    ┌────────────┐     │
│  │ Field │ →  │   Trace    │ →  │   Echo     │     │
│  │  Creates   │    │ Validates  │    │ Simulates  │     │
│  └────────────┘    └────────────┘    └────────────┘     │
│        ↑                  │                  │           │
│        └──────────────────┴──────────────────┘           │
│                    Feedback Loop                         │
└──────────────────────────────────────────────────────────┘

Pattern A: Field → Trace (Segmentation)

Use Field-defined personas to segment sessions for analysis.

Input Format

PERSONA_DEFINITION:
  source: Field
  persona:
    name: "Cautious Comparison Shopper"
    id: "CCS-001"

    # Identifiable characteristics
    behavioral_markers:
      - views_multiple_products: ">3 products before cart"
      - compares_prices: "Visits competitor sites"
      - reads_reviews: "Scrolls to review section"
      - long_consideration: ">5 min on product page"

    # Technical markers (for filtering)
    technical_markers:
# ...

Trace Processing

SEGMENTATION_PROCESS:
  1. Filter sessions by technical_markers
  2. Score sessions against behavioral_markers
  3. Classify sessions with confidence score

  output:
    persona: "Cautious Comparison Shopper"
    sessions_matched: 1247
    confidence_distribution:
      high: 45%    # 4/4 markers match
      medium: 35%  # 3/4 markers match
      low: 20%     # 2/4 markers match

Output Format

SEGMENT_ANALYSIS:
  persona: "Cautious Comparison Shopper"
  analysis_period: "2025-01-01 to 2025-01-31"
  sessions_analyzed: 1247

  behavior_patterns:
    expected_vs_actual:
      - marker: "views_multiple_products"
        expected: ">3 products"
        actual_average: 4.7
        match: true

      - marker: "reads_reviews"
        expected: "Scrolls to review section"
        actual_rate: 67%
# ...

Pattern B: Trace → Field (Validation)

Validate and update persona definitions based on real data analysis.

Validation Report Format

PERSONA_VALIDATION_REPORT:
  persona: "Cautious Comparison Shopper"
  validation_date: "2025-01-31"
  sessions_analyzed: 1247

  validation_results:
    overall_match: 72%

    by_marker:
      - marker: "views_multiple_products"
        expected: ">3 products"
        actual_match: 89%
        status: "VALIDATED"

      - marker: "compares_prices"
# ...

Handoff to Field

## RESEARCHER_HANDOFF (from Trace)

### Persona Validation: Cautious Comparison Shopper

**Analysis Period:** 2025-01-01 to 2025-01-31
**Sessions Analyzed:** 1,247

### Validation Summary

| Marker | Expected | Match Rate | Status |
|--------|----------|------------|--------|
| Multiple product views | >3 products | 89% | ✅ Validated |
| Compares prices | Competitor visits | 34% | ⚠️ Needs review |
| Reads reviews | Scrolls to reviews | 67% | 🔶 Partial |

...

Pattern C: Trace → Echo (Problem Handoff)

Hand off discovered problems to Echo for simulation verification.

Problem Discovery Format

PROBLEM_DISCOVERY:
  id: "PROB-2025-0131-001"
  discovery_date: "2025-01-31"

  location:
    page: "/checkout/payment"
    element: "#submit-payment-btn"

  evidence:
    sessions_analyzed: 3421
    frustration_score: 23.7 (High)

    signals:
      rage_clicks:
        rate: 18%
# ...

Handoff to Echo

## ECHO_HANDOFF (from Trace)

### Problem: Payment Submit Button Frustration

**Frustration Score:** 23.7 (High)
**Sessions Analyzed:** 3,421

### Evidence

| Signal | Rate | Detail |
|--------|------|--------|
| Rage clicks | 18% | Average 4.2 clicks before success |
| Back loops | 34% | Return to cart, re-add items |
| Abandonment | 28% | Exit after 2+ submit attempts |

...

Pattern D: Echo → Trace (Prediction Validation)

Validate Echo's simulation predictions with real session data.

Prediction Input

ECHO_PREDICTION:
  prediction_id: "ECHO-PRED-001"
  prediction_date: "2025-01-25"

  persona: "Senior User"
  flow: "Account settings"

  predicted_friction:
    - location: "Password change form"
      issue: "Font size too small"
      confidence: 0.85
      expected_signals:
        - zoom_gestures
        - long_form_completion_time

# ...

Validation Process

TRACE_VALIDATION:
  prediction_id: "ECHO-PRED-001"
  validation_date: "2025-01-31"

  segment_criteria:
    age_group: "60+"
    flow: "Account settings"

  sessions_analyzed: 234

  validation_results:
    - prediction: "Font size too small"
      status: "CONFIRMED"
      confidence_delta: +0.10  # Higher than predicted
      evidence:
# ...

Validation Report

## Echo Prediction Validation Report

**Prediction ID:** ECHO-PRED-001
**Persona:** Senior User
**Flow:** Account settings
**Sessions Analyzed:** 234

### Results

| Prediction | Confidence | Status | Evidence |
|------------|------------|--------|----------|
| Font size too small | 0.85 → 0.95 | ✅ CONFIRMED | 78% zoom, 3.2x time |
| Low color contrast | 0.72 → 0.57 | 🔶 PARTIAL | 8% dead clicks |

### Confirmed: Font Size Issue
...

Persona Segment Mapping

Default Segment Mappings

Field Persona Trace Technical Filters Behavioral Markers
Mobile-first Millennial device=mobile, age=25-35 fast_navigation, gesture_heavy
Cautious Shopper session_duration>10min multiple_product_views, review_reader
Senior User age=60+ slow_pace, zoom_gestures
Power User visits>10/month keyboard_shortcuts, direct_navigation
First-time Visitor visit_count=1 help_seeking, exploration_pattern

Custom Segment Definition

CUSTOM_SEGMENT:
  name: "[Persona Name]"

  technical_filters:
    # Demographic
    age_range: "[min]-[max]"
    location: "[region/country]"

    # Device
    device_type: "[mobile/desktop/tablet/any]"
    browser: "[specific or any]"

    # Behavioral (quantitative)
    session_duration: "[operator] [value]"
    pages_per_session: "[operator] [value]"
# ...

Collaboration Handoff Table (SKILL.md excerpt)

Direction Handoff Purpose
Field → Trace RESEARCHER_TO_TRACE Persona definitions for session filtering
Echo → Trace ECHO_TO_TRACE Verify predictions with real sessions
Pulse → Trace PULSE_TO_TRACE Quantitative anomaly triggers qualitative analysis
Trace → Field TRACE_TO_RESEARCHER Real data validates/updates personas
Trace → Echo TRACE_TO_ECHO Discovered issues for simulation verification
Trace → Canvas TRACE_TO_CANVAS Behavior data to journey diagrams
Trace → Palette TRACE_TO_PALETTE UX fix recommendations based on behavior analysis
Voice → Trace VOICE_TO_TRACE Qualitative feedback mapped to behavioral session evidence
Trace → Experiment TRACE_TO_EXPERIMENT Behavioral insights inform A/B test hypothesis design (Hypothesis Readiness Score ≥7 required)
Trace → Cast TRACE_TO_CAST_DRIFT Trigger persona update on ≥15% behavioral divergence
Trace → Voice TRACE_TO_VOICE Frustration detection → targeted-survey design
Trace → Saga TRACE_TO_SAGA Narrativization of high-impact session analysis
Trace → Pulse TRACE_TO_PULSE Feed PLG activation evidence into metric design

Source: SKILL.md on GitHub

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    The 'Trace' skill is a comprehensive UX analysis tool designed to interpret user session replays, frustration signals, and journey narratives. It focuses on identifying behavioral patterns like rage clicks and dead clicks while maintaining strict privacy standards through PII masking and GDPR/CCPA compliance. No malicious patterns, unauthorized data access, or code execution risks were detected.

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Signed by skilld at 35ffd55. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

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