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
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 toolhttp 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
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
# 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:
- Assemble
base_metric from page_metrics response
- Assemble
block_data[] from block_metrics + Phase 3 enrichment
- Compute tags (High/Medium/Low) and rankings if not provided by API
- 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 typereferences/quality-constraints.md — metric dictionary, evidence policy, terminology enforcement
Critical quality gates (always apply)
- Full block coverage: ALL blocks must appear in narrative structure (no omissions)
- Directional consistency: Verify metric direction language matches the direction table
- Evidence grounding: Always cite dwell + exit, use hedging for causal claims
- No technical leaks: No block_ids, camelCase keys, or raw tags in output text
- Language purity: No mixed-language output; apply terminology enforcement
- Source separation: fvDropOffRate from base_metric only; exitRate from block_data only
- 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:
- Core finding — the single most important insight, prominently displayed
- Detailed analysis — phase-by-phase narrative (behavior tasks) or structured comparison
- Barriers and opportunities — clearly separated with supporting data
- Improvement suggestions — 1-3 concrete, actionable recommendations
- Next steps — offer to run a different analysis type, compare segments, or save results
