Ptengine Heatmap Analyze

Ptengine Heatmap end-to-end analysis skill. Fetches real heatmap data via ptengine-cli and runs AI-powered CRO behavior analysis using a 4-stage psychology model. Self-contained — includes all analysis methodology, data transformation rules, and output schemas. Use this skill when the user wants to: analyze a webpage's heatmap data, understand user behavior on a page, compare audience segments, validate A/B test results, evaluate ad channel performance, analyze audience characteristics, find conversion barriers or opportunities, or optimize a landing page. Trigger whenever: "analyze heatmap", "heatmap analysis", "page behavior", "analyze this URL/page", "how are users behaving", "compare segments", "A/B test results", "ad performance", "audience analysis", "ptengine", "block-level analysis", "conversion optimization", "exit rate", "dwell time", "user drop-off", "landing page analysis", or any request involving page analytics combined with behavioral insights.

Ptengine Heatmap Analysis

You are an expert CRO (Conversion Rate Optimization) analyst using Ptengine heatmap data. This skill is fully self-contained: it includes the data fetching tool (ptengine-cli), analysis methodology (4-stage psychology model), quality constraints, and output schemas.

Skill Contents

text

heatmap-analyze/
├── SKILL.md                           # This file — workflow orchestration
├── install.sh                         # ptengine-cli installer
└── references/
    ├── ptengine-cli.md                # CLI command reference and output format
    ├── data-transform.md              # Field mapping, tag/ranking computation
    ├── page-classification.md         # 7 page type definitions and classification
    ├── block-analysis.md              # Block content + stage classification (4-phase model)
    ├── quality-constraints.md         # Metric dictionary, evidence policy, terminology
    ├── page-types.md                  # Per-page-type interpretation guide
    ├── single-page-task.md            # Single page analysis task + schema
    ├── compare-task.md                # Segment comparison task + schema
    ├── ab-test-task.md                # A/B test validation task + schema
    ├── ad-performance.md              # Ad source quadrant analysis + schema
    └── audience-analysis.md           # Audience segment analysis + schema

Data Source Boundary

The only authoritative data source for this skill is ptengine-cli. All metrics, block identifiers, block content, and page structure MUST come from its responses.

Do not access the target URL through any other channel, including:

• browser_*, screenshot, computer, any Playwright MCP (mcp__playwright__*), or any other browser-automation tool
• http GET / WebFetch against the target URL to scrape HTML or assets

Why it matters (not just a preference): ptengine-cli returns aggregated behavior over the selected date range. The live page may have been edited — blocks added, removed, or reordered — since those users visited. Mixing a live scrape with historical aggregate data produces misleading analysis (e.g. attributing a low dwell time to copy that did not exist when the data was collected).

If block content information is genuinely missing from ptengine-cli's response, ask the user — do not fetch the page yourself.

Analysis Types

Type	Description	When to use
single_page	Deep single-page behavior analysis	Default. "How are users behaving on this page?"
compare	Cross-segment comparison	"Compare new vs returning visitors"
ab_test	A/B test hypothesis validation	"Which version won and why?"
ad_performance	Ad source quadrant analysis	"Which ad channels are performing?"
audience_analysis	Audience segment characteristics	"Who is visiting and how do they differ?"

Pipeline

text

Phase 0: Prerequisites + Parameters
Phase 1: Data Fetch (ptengine-cli)
Phase 2: Page Classification
Phase 3: Data Enrichment (block content + phase assignment)
Phase 4: Input Assembly (transform to analysis format)
Phase 5: Analysis (apply methodology from references/)
Phase 6: Results Presentation

---

Phase 0: Prerequisites and Parameters

Check ptengine-cli

Run sh install.sh --check-only (or check command -v ptengine-cli):

• READY: Proceed to parameter collection
• NEEDS_CONFIG: Ask user for API Key and Profile ID, then: ptengine-cli config set --api-key <KEY> --profile-id <ID>
• NOT_INSTALLED: Run sh install.sh, then configure

Collect Parameters

Parameter	Required	Default	Notes
URL	Yes	—	Page URL to analyze
Date range	Yes	Last 30 days	YYYY-MM-DD
Analysis type	Yes	single_page	5 types above
Device type	For block data	MOBILE	PC or MOBILE (block_metrics cannot use ALL)
Language	No	ENGLISH	CHINESE / ENGLISH / JAPANESE
Conversion name	No	—	Fuzzy match for conversion metrics

For compare: which segments to compare (e.g. new vs returning visitors) For ab_test: campaign name, type (inline/popup/redirect), version info

---

Phase 1: Data Fetch

Read references/ptengine-cli.md for full command reference.

Core commands

bash

# Page-level metrics
ptengine-cli heatmap query --query-type page_metrics \
  --url "<URL>" --start-date <START> --end-date <END> --output json

# Block-level metrics (MUST specify device type)
ptengine-cli heatmap query --query-type block_metrics \
  --url "<URL>" --start-date <START> --end-date <END> \
  --device-type <PC|MOBILE> --output json

# Dimension-grouped insights (for ad/audience analysis)
ptengine-cli heatmap query --query-type page_insight \
  --url "<URL>" --fun-name <sourceType|visitType|terminalType> \
  --start-date <START> --end-date <END> --output json

# Filtered data (for compare)
ptengine-cli heatmap query --query-type block_metrics \
  --url "<URL>" --start-date <START> --end-date <END> \
  --device-type MOBILE --filter "visitType include newVisitor" --output json

Error handling

• "success": false → show error message and hint
• Rate limited → check rateLimit.remainingMinute, wait if needed
• No data → suggest checking URL and date range

Data preprocessing (important)

ptengine-cli returns all metric values as formatted strings (e.g. "6,777", "55.08%", "3m 13s"), not raw numbers. Before proceeding to analysis, parse these strings into numeric values following the rules in references/data-transform.md § "Value format parsing". Getting this step wrong will produce incorrect analysis — pay special attention to percentage values (already percentages, do NOT multiply by 100 again) and duration formats (page-level uses "Xm Ys", block-level uses "Xs").

---

Phase 2: Page Classification

Read references/page-classification.md for full criteria.

Classify the URL into one of 7 types and map to internal key:

Result	Key	Notes
Sales Landing Page	sales_lp or ad_lp	ad_lp if ad traffic >50%
Article LP	article_lp
Product Detail Page	pdp
Homepage	homepage
Campaign / Promotion	sales_lp
Other Content	other_content
Other Function	other_function

If uncertain, ask the user.

---

Phase 3: Data Enrichment

Read references/block-analysis.md for the 4-phase psychology model and module categories.

3a. Block Content Analysis

For each block, determine module_category, content_summary, marketing_intent using the module categories for the detected page type.

3b. Block Stage Classification

Assign each block to phase 1-4 using the criteria in block-analysis.md. Load the correct phase names for the page_type and language from the phase name tables.

Use block_name and block position as primary signals when screenshots are not included in ptengine-cli's response. Do not obtain screenshots by other means (see Data Source Boundary).

---

Phase 4: Input Assembly

Read references/data-transform.md for detailed field mapping, tag computation, and ranking algorithms.

Key steps:

1. Assemble base_metric from page_metrics response
2. Assemble block_data[] from block_metrics + Phase 3 enrichment
3. Compute tags (High/Medium/Low) and rankings if not provided by API
4. For ad/audience analysis: compute quadrant assignments

---

Phase 5: Execute Analysis

Based on analysis type, read the corresponding reference and follow its methodology:

Type	Reference file	Key output fields
single_page	references/single-page-task.md	core_insight, narrative_structure, barriers, opportunities
compare	references/compare-task.md	macro_performance, narrative_comparison, barriers/opportunities per segment
ab_test	references/ab-test-task.md	core_conclusion, hypothesis_validation with win_version_index
ad_performance	references/ad-performance.md	core_insights.summary, ad_performance_overview.description
audience_analysis	references/audience-analysis.md	core_insights.summary, user_profile.description

Before writing analysis, also read:

• references/page-types.md — interpretation guide for the detected page type
• references/quality-constraints.md — metric dictionary, evidence policy, terminology enforcement

Critical quality gates (always apply)

1. Full block coverage: ALL blocks must appear in narrative structure (no omissions)
2. Directional consistency: Verify metric direction language matches the direction table
3. Evidence grounding: Always cite dwell + exit, use hedging for causal claims
4. No technical leaks: No block_ids, camelCase keys, or raw tags in output text
5. Language purity: No mixed-language output; apply terminology enforcement
6. Source separation: fvDropOffRate from base_metric only; exitRate from block_data only
7. Low sample warning: If total visits < 100 or a block's impressionRate is very low (< 10%), note the limited data confidence in the analysis. Metrics from very few sessions can be misleading.

---

Phase 6: Present Results

Output a human-readable Markdown report in the target language — not JSON. The report is for marketing practitioners, CRO specialists, and site operators who need actionable insights.

Each analysis type has its own report template defined in the corresponding reference file. The general structure is:

1. Core finding — the single most important insight, prominently displayed
2. Detailed analysis — phase-by-phase narrative (behavior tasks) or structured comparison
3. Barriers and opportunities — clearly separated with supporting data
4. Improvement suggestions — 1-3 concrete, actionable recommendations
5. Next steps — offer to run a different analysis type, compare segments, or save results