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Cxas Configurable Dashboards

Organization
GoogleCloudPlatform
cxas-configurable-dashboards

Author, validate, and manage Contact Center AI (CCAI) Insights Configurable Dashboards. Use when users want to define multi-tab analytics dashboards, configure Vega-Lite charts and SQL queries, maintain declarative dashboards.yaml configurations, or synchronize dashboards to GCP projects.

Overview

PublisherGoogleCloudPlatform
Repositorycxas-scrapi
Skill namecxas-configurable-dashboards
Stars
113
Forks
106
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GoogleCloudPlatform on GitHub. Read the source before you install it.

Installation

Install the Cxas Configurable Dashboards AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/GoogleCloudPlatform/cxas-scrapi/tree/main/.agents/skills/cxas-configurable-dashboards
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GoogleCloudPlatform/cxas-scrapi.git /tmp/cxas-scrapi
mkdir -p .claude/skills
cp -r /tmp/cxas-scrapi/.agents/skills/cxas-configurable-dashboards .claude/skills/cxas-configurable-dashboards
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cxas Configurable Dashboards in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cxas Configurable Dashboards on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cxas Configurable Dashboards is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CCAI Insights Configurable Dashboards Skill

This skill guides you in authoring, refining, validating, and synchronizing Contact Center AI (CCAI) Insights Configurable Dashboards.

CCAI Insights Configurable Dashboards allow users to build customizable, multi-tab reporting views with rich visualization widgets (Score Cards, Bar/Line charts, Pie charts, Tables, Sankey diagrams) powered by Vega-Lite specifications and SQL queries against conversation metrics.


1. Overview & Declarative YAML Schema

Dashboards are defined declaratively in dashboards.yaml:

yaml
version: "1.0"
project_id: "your-gcp-project-id"
location: "us-central1"

dashboards:
  - dashboard_id: "executive_kpis"
    display_name: "Executive Contact Center KPIs"
    description: "High-level summary of inbound call volumes, virtual agent containment, and quality."
    date_range:
      relative:
        quantity: 7
        unit: "DAY"

    root_container:
      display_name: "Root"
      widgets:
        - container:
            display_name: "Overview Tab"
            description: "Operational summary metrics"
            widgets:
              # Tile 1: Total Volume Scorecard
              - chart:
                  display_name: "Total Conversations"
                  chart_visualization_type: "SCORE_CARD"
                  width: 4
                  height: 3
                  data_source:
                    generative_insights:
                      sql_query: "SELECT COUNT(DISTINCT conversation_id) AS total_calls FROM conversations"
                      chart_spec:
                        mark: "text"
                        encoding:
                          text: {field: "total_calls", type: "quantitative"}

              # Tile 2: Top Contact Drivers Bar Chart
              - chart:
                  display_name: "Top Contact Drivers"
                  chart_visualization_type: "BAR"
                  width: 8
                  height: 6
                  data_source:
                    generative_insights:
                      sql_query: >-
                        SELECT issue_category, COUNT(1) AS volume
                        FROM conversations
                        WHERE issue_category IS NOT NULL
                        GROUP BY 1
                        ORDER BY volume DESC
                        LIMIT 10
                      chart_spec:
                        mark: "bar"
                        encoding:
                          x: {field: "volume", type: "quantitative", title: "Calls"}
                          y: {field: "issue_category", type: "nominal", sort: "-x", title: "Category"}

Core Structural Requirements

  1. Root Container Constraint (ValidateDashboardStructure):
    • Every dashboard must have a root_container.
    • Direct widgets in root_container must all be Container widgets representing tabs/sections.
  2. Widgets within Tabs:
    • Each tab container contains child widgets (container for sub-grouping, chart for visualizations, or chart_reference for linked charts).
  3. Chart Visualizations:
    • chart_visualization_type: SCORE_CARD, BAR, LINE, AREA, PIE, SCATTER, TABLE, SANKEY.
    • data_source: Contains generative_insights with sql_query and Vega-Lite chart_spec.

2. Vega-Lite & SQL Recipes

Refer to:

Common Patterns

  • Scorecard (Single KPI):
    yaml
    chart_visualization_type: "SCORE_CARD"
    data_source:
      generative_insights:
        sql_query: "SELECT COUNT(1) AS total FROM conversations"
        chart_spec:
          mark: "text"
          encoding:
            text: {field: "total", type: "quantitative"}
  • Time Series Trend (Line Chart):
    yaml
    chart_visualization_type: "LINE"
    data_source:
      generative_insights:
        sql_query: "SELECT DATE(start_time) AS date, COUNT(1) AS calls FROM conversations GROUP BY 1"
        chart_spec:
          mark: "line"
          encoding:
            x: {field: "date", type: "temporal"}
            y: {field: "calls", type: "quantitative"}

3. Step-by-Step Workflow

Step 1: Ingest Requirements

  • Ask the user what operational metrics, KPIs, or tabs they need (e.g. Agent QA performance, Containment %, Top Contact Drivers, CSAT trends).
  • Identify the target GCP project ID and location.

Step 2: Draft or Edit Declarative YAML

  • Create or update dashboards.yaml in the user's workspace.
  • Structure tabs inside root_container.widgets.
  • Add scorecards, bar charts, and line charts with matching Vega-Lite specs and SQL queries.

Step 3: Compare with Active Remote Dashboards (diff)

Run diff to preview additions, modifications, and deletions:

bash
uv run cxas insights diff-dashboards --file dashboards.yaml

Step 4: Dry-Run Deploy

Verify planned operations against GCP without mutating resources:

bash
uv run cxas insights push-dashboards --file dashboards.yaml --dry-run

Step 5: Push to GCP

Deploy new and updated dashboards to Contact Center AI Insights:

bash
uv run cxas insights push-dashboards --file dashboards.yaml

If deleting obsolete remote dashboards:

bash
uv run cxas insights push-dashboards --file dashboards.yaml --force

4. CLI Command Reference

  • Pull Remote Dashboards:
    bash
    uv run cxas insights pull-dashboards --parent projects/PROJECT_ID/locations/LOCATION [--out dashboards.yaml]
  • Diff Dashboards:
    bash
    uv run cxas insights diff-dashboards --file dashboards.yaml
  • Push / Sync Dashboards:
    bash
    uv run cxas insights push-dashboards --file dashboards.yaml [--dry-run] [--force]
  • List Dashboards:
    bash
    uv run cxas insights list-dashboards --parent projects/PROJECT_ID/locations/LOCATION
  • Get Dashboard:
    bash
    uv run cxas insights get-dashboard --dashboard-name projects/PROJECT_ID/locations/LOCATION/dashboards/DASHBOARD_ID
  • Delete Dashboard:
    bash
    uv run cxas insights delete-dashboard --dashboard-name projects/PROJECT_ID/locations/LOCATION/dashboards/DASHBOARD_ID

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cxas Configurable Dashboards AI skill do?

Author, validate, and manage Contact Center AI (CCAI) Insights Configurable Dashboards. Use when users want to define multi-tab analytics dashboards, configure Vega-Lite charts and SQL queries, maintain declarative dashboards.yaml configurations, or synchronize dashboards to GCP projects.

Why use Cxas Configurable Dashboards on TypingMind?

Because you install it once and use it with any model. Cxas Configurable Dashboards is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cxas Configurable Dashboards in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GoogleCloudPlatform/cxas-scrapi/tree/main/.agents/skills/cxas-configurable-dashboards. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cxas Configurable Dashboards?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cxas Configurable Dashboards?

As many as you like. As long as a model supports skills, you can use Cxas Configurable Dashboards with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cxas Configurable Dashboards AI skill free?

Yes. It is published on GitHub by GoogleCloudPlatform under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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