Zdx Compare Location Experience

作者 zscaler809f68d6c921无许可证收录于 2026年10月8日更新于 2026年10月8日

Compare digital experience across locations, departments, and geolocations using ZDX data. Identifies which offices or regions have the best and worst experience for specific applications, detects location-specific issues, and provides optimization recommendations. Aligned with ZDX Copilot analytics and optimization use cases. Use when an administrator asks: 'Which office has the worst experience?', 'Compare application performance between locations', 'Is the Dallas office having network issues?', or 'Show me ZDX scores by department.'

AI 生成的概览

比较各办公地点、部门和地区的 ZDX 数字体验评分,并生成 Word 与 HTML 报告。

功能
该技能引导智能体调用 ZDX 工具,列出地点和部门、比较应用评分、深入查看评分趋势以及页面获取时间、DNS、可用性等指标,并检查受影响用户、告警和设备健康状况。随后它组装一个 JSON 数据负载,通过随附的 HTML 模板渲染,并另外写出一个 Word 文档。两份输出均命名为 location_comparison_report_<时间戳>,包含排名、根因分析和按优先级排列的修复措施。
适用场景
适用于管理员询问哪个办公地点或地区体验最差、希望比较不同地点或部门的应用性能,或怀疑存在特定站点的网络问题。也适合按部门或地理区域细分 ZDX 评分的请求。
运行要求
需要访问 ZDX MCP 工具(地点、部门、应用、指标、告警和设备接口),并具备写入磁盘文件的能力。技能附带 HTML 模板和示例报告,但不含脚本;智能体需自行读取模板并替换 JSON 数据负载。

ZDX: Compare Location Experience

Keywords

compare locations, location experience, office comparison, department comparison, regional performance, site health, location score, office performance, worst office, best practices, optimization, geographic analysis, location ranking

Overview

Compare digital experience across different locations, departments, and geolocations to identify which sites perform best and worst. This skill uses ZDX filtering capabilities to break down application scores, metrics, alerts, and device health by organizational dimensions, enabling proactive optimization and targeted remediation.

Use this skill when: An administrator wants to compare application experience across offices, identify the worst-performing locations, investigate whether an issue is location-specific, or optimize network configuration for underperforming sites.

ZDX Copilot alignment: This skill covers the Analytics and Optimization categories -- comparing performance across organizational dimensions and recommending improvements.


Data Presentation Requirements

All tables are rendered by the HTML template, not by hand. Do not author <table>, <thead>, <tbody>, <tr>, <th>, or <td> markup in your reply or in the report. You produce a JSON payload (see Data Payload Contract) and the template at ./templates/report.html.template turns it into the styled, sortable, exportable tables shown in ./example/report.example.html.

After each table, provide:

  1. Detailed analysis explaining the performance differences between locations and what they indicate
  2. Root cause identification for underperforming locations (DNS, ISP, WiFi, device fleet age, etc.)
  3. Next steps / resolution with site-specific remediation actions prioritized by impact and feasibility

Use color-coded rows based on location ranking:

  • Green: Top-performing locations (score 66-100)
  • Yellow: Borderline locations (score 34-65)
  • Red: Worst-performing locations (score 0-33)

⚠ HTML OUTPUT — READ THIS BEFORE PRODUCING ANY HTML

There is exactly one acceptable way to produce the HTML output:

  1. Read the template from disk — do NOT inline a copy in your response. The template lives next to this SKILL.md inside the skill's package, at:

    text
    ./templates/report.html.template

    The ./ prefix is intentional: this path is relative to the skill folder (the directory containing this SKILL.md), never an absolute path. Most agents that load skills from an uploaded .zip extract the package into a working directory and expose its contents via that relative path — read the file by joining the skill's own root directory with ./templates/report.html.template. Do not rewrite this to an absolute path that points at the author's machine.

  2. Build a single JSON object (__ZDX_DATA__ payload) shaped exactly as documented in the Data Payload Contract section below. Aggregate the responses from the ZDX MCP tool calls (Steps 1–7 of the Workflow) into that object.

  3. Replace the literal token __ZDX_DATA__ (which appears once, inside <script type="application/json" id="zdx-data">__ZDX_DATA__</script>) with the JSON object. Do not edit any other part of the template.

  4. Write the result to disk as location_comparison_report_<YYYYMMDD-HHMMSS>.html next to the .docx, and give the user a computer:// link to it.

This template already provides: Zscaler header with logo · sticky top bar · scope summary bar · KPI cards with severity-coded top borders · per-table search + filter chips · sortable color-coded tables · per-table CSV export · light/dark theme toggle · top-right language dropdown (EN / ES / PT / FR / JA) · printable PDF view · localStorage prefs · Analysis / Root Cause / Remediation block.

If you find yourself writing <html>, <style>, or <table> in a code-block destined for the user, stop. Read the template instead.

A populated reference rendering ships with this skill at ./example/report.example.html (relative to the skill folder). Open it in a browser to preview the exact layout and depth expected.

Data Payload Contract

The full __ZDX_DATA__ payload is one JSON object. Every field below is required unless marked optional.

json
{  "generated_at": "<ISO 8601 timestamp>",  "scope_en": "Free-form description in English",  "scope_es": "...in Spanish (optional, falls back to scope_en)",  "scope_pt": "...in Portuguese (optional)",  "scope_fr": "...in French (optional)",  "scope_ja": "...in Japanese (optional)",  "kpis": {    "totalLocations": "<int>",    "bestLocation": "<location name>",    "worstLocation": "<location name>",    "avgScore": "<int, 0-100>",    "alertCount": "<int>"  },  "tables": {    "locations": [      {        "severity": "critical | warning | good",        "rank": "<int>",        "name": "<location name>",        "score": "<0-100>",        "pft": "<page fetch time, e.g. '1.8s'>",        "dns": "<dns time, e.g. '18ms'>",        "availability": "<percentage, e.g. '100%'>",        "poorUsers": "<e.g. '0/120'>",        "alerts": "<int>"      }    ]  },  "analysis": {    "summary": "...",    "rootCause": "...",    "remediation": [      { "priority": "Immediate | Investigate | Monitor | Communicate", "action": "..." }    ]  }}

Map each row's severity from its tier: top (score ≥ 80) → good, borderline (50–79) → warning, poor (< 50) → critical.

Output Artifacts — MANDATORY

You MUST generate BOTH files below. Both are REQUIRED output for every location comparison.

1. Word Document (.docx) — REQUIRED

Write a Word document to disk named location_comparison_report_<YYYYMMDD-HHMMSS>.docx containing:

  • Executive summary with best/worst performing locations
  • Location ranking table (rank, location, score, PFT, DNS, availability, poor users, alerts)
  • Per-location analysis for underperforming sites with metric breakdowns
  • Root cause analysis for worst performers (DNS, ISP, WiFi, device fleet)
  • Cross-location metric comparison for each application
  • Site-specific remediation actions prioritized by impact

2. Interactive HTML Web Page (.html) — REQUIRED

Generated by the template-substitution flow described in the HTML OUTPUT section above. Filename: location_comparison_report_<YYYYMMDD-HHMMSS>.html. Do not hand-author HTML or CSS — the template ships everything the report needs.


Workflow

Step 1: List Available Locations and Departments

First, enumerate the organizational dimensions available for comparison.

List locations:

text
zdx_list_locations()```text
**List departments:**
```textzdx_list_departments()```text
Note the IDs returned -- these are used as filters in subsequent calls.
---
### Step 2: Compare Application Scores Across Locations
For each application of interest, retrieve scores filtered by different locations.
**Location A:**
```textzdx_list_applications(location_id=["<location_a_id>"], since=24)```text
**Location B:**
```textzdx_list_applications(location_id=["<location_b_id>"], since=24)```text
**Location C:**
```textzdx_list_applications(location_id=["<location_c_id>"], since=24)```text
Compile the scores side by side to identify which locations are underperforming.
---
### Step 3: Drill Into Score Trends for Underperforming Locations
For the worst-performing location, check the score trend.
```textzdx_get_application_score_trend(  app_id="<app_id>",  location_id=["<worst_location_id>"],  since=24)```text
Compare with a healthy location:
```textzdx_get_application_score_trend(  app_id="<app_id>",  location_id=["<healthy_location_id>"],  since=24)```text
**What to look for:**
- Does the underperforming location show a consistent low score or a sudden drop?- Does the score correlate with specific times of day (peak hours)?- Are multiple applications affected at this location, or just one?
---
### Step 4: Compare Metrics Across Locations
Drill into metrics for the specific application at each location.
**Page Fetch Time by location:**
```textzdx_get_application_metric(  app_id="<app_id>",  metric_name="pft",  location_id=["<location_a_id>"])```text
```textzdx_get_application_metric(  app_id="<app_id>",  metric_name="pft",  location_id=["<location_b_id>"])```text
**DNS by location:**
```textzdx_get_application_metric(  app_id="<app_id>",  metric_name="dns",  location_id=["<location_a_id>"])```text
**Availability by location:**
```textzdx_get_application_metric(  app_id="<app_id>",  metric_name="availability",  location_id=["<location_a_id>"])```text
---
### Step 5: Compare Impacted Users by Location
Understand user impact at each location.
```textzdx_list_application_users(  app_id="<app_id>",  score_bucket="poor",  location_id=["<worst_location_id>"])```text
```textzdx_list_application_users(  app_id="<app_id>",  score_bucket="poor",  location_id=["<healthy_location_id>"])```text
**Calculate impact ratios:** If Location A has 5 poor users out of 50 total (10%) vs Location B with 25 poor users out of 100 total (25%), Location B has a more severe issue despite Location A having fewer total users.
---
### Step 6: Check Location-Specific Alerts
```textzdx_list_alerts(location_id=["<worst_location_id>"], since=48)```text
```textzdx_list_historical_alerts(location_id=["<worst_location_id>"], since=168)```text
Recurring alerts at a specific location indicate a persistent infrastructure issue at that site.
---
### Step 7: Assess Device Health at Underperforming Locations
Check if the issue is device-related rather than network-related.
```textzdx_list_devices(location_id=["<worst_location_id>"])```text
For specific devices showing issues:
```textzdx_get_device(device_id="<device_id>")```text
Check for:
- High CPU/memory usage across many devices (hardware refresh needed)- Outdated ZCC agent versions- WiFi issues (common in specific offices)
---
### Step 8: Present Comparison Report
Assemble the `__ZDX_DATA__` payload defined in the *Data Payload Contract* and render it through `./templates/report.html.template` (see the **HTML OUTPUT** section). The template already produces the location ranking table, KPI cards, color coding by tier, search/sort, and CSV export — do **not** hand-author any HTML or markdown table here.
What you DO write is the `analysis` block inside the payload. **Do not skip it.** This is what makes the comparison useful:
- **`analysis.summary`** (3–5 sentences): rank the locations and call out the gap between best and worst. Quote concrete numbers from the metric calls in Steps 2–4 (e.g., *"Dallas is the clear outlier with a score of 42, significantly below the org average of 75. DNS resolution at 180ms is 4× the average of other locations (28ms), which is cascading into elevated Page Fetch Times."*).- **`analysis.rootCause`** (2–4 sentences for each worst performer): identify the dominant bottleneck (DNS, ISP, WiFi, device fleet) using the historical alert pattern (Step 6) and device health (Step 7) as evidence.- **`analysis.remediation`** (4–6 items): label each with a priority bucket and a concrete action.
| Priority | Apply to | Action ||---|---|---|| `Immediate` | Worst performer(s) (score < 50) | Investigate the bottleneck metric. If DNS, check local resolver health and consider a redundant DNS provider. If ISP, open a ticket with the carrier with timestamps. || `Investigate` | Borderline locations (score 50–79) | Compare against healthy peers; track active alerts. Escalate if score drops further within 24h. || `Monitor` | Healthy locations on a downward trend | Add to weekly watchlist; verify no regional events are pending. || `Communicate` | All affected locations | Notify site IT contacts with the metric evidence and the expected remediation timeline. |
---
## Department Comparison Variant
The same workflow applies when comparing departments instead of locations.
```textzdx_list_applications(department_id=["<dept_a_id>"], since=24)zdx_list_applications(department_id=["<dept_b_id>"], since=24)```text
**Department comparison is useful for:**
- Understanding if a specific team is disproportionately affected- Compliance reporting (e.g., "How is the Finance team's experience?")- Device fleet differences between departments (older hardware, different OS)
---
## Multi-Application Comparison
Compare how all applications perform at a single location.
```textzdx_list_applications(location_id=["<location_id>"], since=24)```text
If all applications are degraded at one location:
- The issue is likely network infrastructure (ISP, WiFi, LAN)- Not application-specific
If only one application is degraded at a location:
- Likely application-specific routing or server issue for that region- Check application CDN or server closest to that location
---
## Edge Cases
### Location with No Active Devices
```textNo active devices found at location "<location_name>" in therequested time window. The office may be closed, or devicesmay not be reporting.
Verify:- Is the office operational?- Are ZDX agents deployed at this location?- Check the 'since' parameter (try a wider window)```text
### All Locations Equally Degraded
If all locations show similar poor scores:
- The issue is not location-specific- Likely an application server or Zscaler service-wide event- Switch to the **Analyze Application Health** skill
### New Location Without Baseline
For recently added locations with no historical data:
- Cannot reliably compare with established locations- Collect at least 1 week of data before drawing conclusions- Use current metrics as the initial baseline
---
## Quick Reference
**Primary workflow:** List Locations/Depts → Compare App Scores → Drill Trends → Compare Metrics → Check Users → Review Alerts → Report
**Tools used:**
- `zdx_list_locations()` -- enumerate locations- `zdx_list_departments()` -- enumerate departments- `zdx_list_applications(location_id/department_id)` -- scores by dimension- `zdx_get_application_score_trend(app_id, location_id)` -- trend by location- `zdx_get_application_metric(app_id, metric_name, location_id)` -- metrics by location- `zdx_list_application_users(app_id, score_bucket, location_id)` -- impacted users by location- `zdx_list_alerts(location_id)` -- location-specific alerts- `zdx_list_historical_alerts(location_id)` -- recurring patterns- `zdx_list_devices(location_id)` -- devices at a location- `zdx_get_device(device_id)` -- device health details
**Comparison dimensions:**
- `location_id` -- by office / ZIA location- `department_id` -- by business unit- `geo_id` -- by geographic region
**Related skills:**
- [Troubleshoot User Experience](../troubleshoot-user-experience/) -- individual user drill-down- [Analyze Application Health](../analyze-application-health/) -- org-wide app health- [Investigate Alerts](../investigate-alerts/) -- alert-focused investigation

来源与署名

来源:zscaler/zscaler-mcp-server位于skills/zdx/compare-location-experience提交809f68d

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