Cxas Drawio Ingestor logo

Cxas Drawio Ingestor

Organization
GoogleCloudPlatform
cxas-drawio-ingestor

Extracts conversational transcripts from .drawio XML files.

Overview

PublisherGoogleCloudPlatform
Repositorycxas-scrapi
Skill namecxas-drawio-ingestor
Stars
113
Forks
106
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Cxas Drawio Ingestor 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-cuj-report-generator/ingestors/files/drawio
  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-cuj-report-generator/ingestors/files/drawio .claude/skills/cxas-drawio-ingestor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cxas Drawio Ingestor 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 Drawio Ingestor 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 Drawio Ingestor 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.

Draw.io Ingestor Skill

Use this skill when you need to extract dialogue turns and conversation flows from .drawio diagram files.

Protocol for Flowchart & Diagram Traversal

When processing .drawio flowchart designs, you must parse them as complete directed graphs to construct dialogue paths, rather than extracting flat text turns.

1. Parse Graph Structural Topology

  • Scan <mxCell> tags where vertex="1". Extract their id and value (text label).
  • Scan <mxCell> tags where edge="1". Match source and target vertex IDs to identify directed pathways.
  • Clean cell text by stripping internal HTML styling, tags, and formatting metadata (e.g., &lt;div&gt;, <font>, CSS styles). Unescape HTML entities (e.g., &quot; or &lt;) to obtain clean text.

2. Dialogue State & Path Traversal

  • Locate the start node (typically a top-level cell labeled "Start", "Welcome", or "Entry").
  • Perform a step-by-step traversal (DFS/BFS) along the directed edges. Each unique path from start to a terminal node constitutes a distinct conversation flow.
  • State Mapping:
    • Prompt Node: Maps to an Agent Turn (using the node's cleaned label).
    • Directed Edge Label: Maps to a User Input (representing speech utterances or DTMF keys that trigger that transition).
    • Computational Node: Maps to a System Action / Webhook (e.g., database lookups or validation calls).
    • Decision Diamond: Represents logical branching.

3. Brand and Domain Consistency Mapping

Ensure that you map all domain concepts consistently to the target brand or theme requested by the user or specified in the requirements. Do not mix multiple industrial domains in a single report.

  • If the target brand has specific concepts, map requirements to fit that brand's services.
  • If no brand is specified, use clean, generic customer service phrasing suitable for the context.
  • Avoid using technical or backend-specific terminology in spoken Agent turns.

4. Format the Resulting CXAS Transcript

Compile the traversed paths into a structured CXAS YAML format complying with the root-level turn structures (turns containing speaker: Agent|User, text, and optional actions). Ensure every scenario starts with the standardized Agent welcome greeting, is voice-realistic, and ends with the standardized sign-off calling the end_session tool.

Example Target Schema

yaml
subintent_id: check_loyalty_status
subintent_name: "Check Loyalty VIP Status"
parent_cuj: "Loyalty Rewards"
turns:
  - speaker: Agent
    text: "Hello! Thanks for calling [Brand]. How can I help you today?"
  - speaker: User
    text: "I need to check my loyalty rewards point balance."
  - speaker: Agent
    text: "I'd be happy to help you check your loyalty rewards point balance. May I have your membership phone number?"
  - speaker: User
    text: "Yes, it is 555-0199."
  - speaker: Agent
    text: "Thank you. Let me verify your rewards balance."
    webhook_call:
      name: check_rewards_balance
      payload:
        phone_number: "5550199"
      response:
        vip_status: "ELITE"
        points_balance: 1500
  - speaker: Agent
    text: "I've found your account. You are an Elite member with a balance of 1,500 points. Is there anything else I can help you with today?"
  - speaker: User
    text: "No, that's all. Thank you."
  - speaker: Agent
    text: "Thank you for calling [Brand]! Goodbye."
    tool_call:
      name: end_session
      payload:
        session_escalated: false
        reason: "Conversation completed successfully"

Linguistic & Voice Naturalness Standards

All generated spoken dialogue turns (Agent voice turns) MUST strictly adhere to high-fidelity spoken voice standards. Subagents must ensure:

  1. Numeric Voice Normalization: Spoken Agent turns MUST NOT contain raw digits, formatted currencies, or punctuation symbols representing numbers (e.g., do NOT write "450", "$909", "555-0199"). Instead, numbers must be explicitly spelled out phonetically:
    • Correct: "four hundred fifty points", "nine hundred nine dollars".
    • IDs and Phone Numbers: Must be written digit-by-digit separated by spaces or commas: "five five five, zero, one, nine, nine".
  2. Spoken Breath Span Limit: Agent turns must remain concise, natural, and conversational. Individual spoken text blocks MUST NOT exceed 300 characters inside a single turn.
  3. Vocabulary Smoothness: Avoid robotic repetitions of the same long words (do not repeat the same word of length 5+ more than 4 times in a single turn).
  4. Conversational Politeness: Agent turns must maintain standard polite voice markers (please, thank you, thanks, certainly, happy to help, welcome, goodbye, great day).

Frequently asked questions

What does the Cxas Drawio Ingestor AI skill do?

Extracts conversational transcripts from .drawio XML files.

Why use Cxas Drawio Ingestor on TypingMind?

Because you install it once and use it with any model. Cxas Drawio Ingestor 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 Drawio Ingestor in TypingMind?

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

Which AI models can use Cxas Drawio Ingestor?

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 Drawio Ingestor?

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

Is the Cxas Drawio Ingestor 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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