Dfcx Framework Ingestor logo

Dfcx Framework Ingestor

Organization
GoogleCloudPlatform
dfcx-framework-ingestor

Parsing and Ingestion directives for the Dialogflow CX (DFCX) agent structure.

Overview

PublisherGoogleCloudPlatform
Repositorycxas-scrapi
Skill namedfcx-framework-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 Dfcx Framework 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/frameworks/dfcx
  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/frameworks/dfcx .claude/skills/dfcx-framework-ingestor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dfcx Framework 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 Dfcx Framework 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 Dfcx Framework 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.

Dialogflow CX Ingestor Skill

This skill standardizes the ingestion, parsing, and behavior extraction of conversational agents exported in the official, public Dialogflow CX (DFCX) format.


1. DFCX Export Package Layout

An official Dialogflow CX agent export is packaged as a .zip archive. When extracted, it conforms to the following public directory layout:

<agent_zip_root>/
├── agent.json                       # Global agent metadata (default language, timezone, display name)
├── intents/                         # Declared intents and training phrases
│   └── <intent_name>/
│       └── <intent_name>.json       # Intent configuration
├── entityTypes/                     # Custom entity declarations
│   └── <entity_name>/
│       └── <entity_name>.json       # Entity configuration and synonyms
├── transitionRouteGroups/           # Shared flow-level transition groups
├── webhooks/                        # Declared webhooks and backend REST integrations
│   └── <webhook_name>.json          # Webhook configuration (defining target URI and credentials)
└── flows/                           # Subfolders representing individual Flows
    └── <flow_name>/                 # Flow directory (e.g., Default Start Flow, Billing)
        ├── <flow_name>.json         # Flow configuration listing flow-level transition routes and entry points
        ├── transitionRouteGroups/   # Shared transition route groups specific to this flow
        └── pages/                   # Directory containing individual page configurations
            └── <page_name>.json     # Page definition containing entry prompts, form parameters, and transitions

2. Ingestion & Flow Tracing Directives

The Ingestor Agent MUST parse the extracted file structures using standard directory traversal and JSON parsing, mapping them to the primary coverage checklist:

A. CUJ Mapping

  • CUJ Discovery: Treat each Flow folder under flows/ (e.g., flows/Default Start Flow/ or flows/Billing Flow/) as a high-level Parent CUJ. The folder's directory name represents the CUJ title.

B. Sub-intent & Trigger Extraction

For each Flow, traverse <flow_name>.json and all .json files inside its pages/ subdirectory:

  1. Flow-Level Transition Routes:
    • Parse the "transitionRoutes" array inside <flow_name>.json.
    • Identify each transition triggered by an intent (where "intent" is declared, e.g., projects/.../intents/<intent_id>).
    • Map each unique triggered transition as a Sub-intent / Scenario (using the target page/flow name to synthesize its name).
  2. Page-Level Transition Routes:
    • For each page .json inside pages/, parse the "transitionRoutes" array to map conversational branches and trigger conditions.
  3. Form Parameter nodes:
    • Parse the "form.parameters" array inside a page JSON.
    • Each parameter collected (e.g., email_address, postal_code) represents a sub-intent step in the conversational walkthrough.
  4. Fulfillment Webhooks:
    • Identify "triggerFulfillment.webhook" inside transition routes or page configs.
    • Resolve the webhook's target endpoint destination URI by looking up its matching configuration file inside the global webhooks/ directory (e.g. webhooks/<webhook_name>.json's "genericWebService.uri").
    • Note: Webhooks represent standard REST integrations defined as toolsets (indexed via OpenAPI specifications open_api_schema.yaml under the toolsets/ directory).

C. Alignment with Canonical cxas-agent-migration Standards

When parsing Dialogflow CX layouts, the Ingestor Agent SHOULD align the extracted behaviors with the open-source cxas-agent-migration topology (N->M agents consolidation):

  1. Playbook/Flow to Agent Persona: Each extracted Flow directory represents a distinct sub-agent boundary. Treat the flow's transitions and event handlers as the sub-agent's private prompt limits and routing callbacks.
  2. Deduplicate & Group Variables: Consolidate intent parameters and page forms variables using cxas-agent-migration Stage 1 rules (variable deduplication) to avoid parameter bloat in transcripts.
  3. Topological Rewiring: Track the source dependency graph (parent flows -> children flows) using cxas-agent-migration Stage 3 rules, ensuring that transitions from steering accurately reflect the legacy DFCX intent routing.

3. Dialogue Simulation & Transcript Guidelines

When simulating conversation transcripts based on parsed DFCX folder packages, enforce the following rules:

A. Verbatim Prompts Extraction

To guarantee the simulated agent speaks the exact developer-defined prompts, parse the fulfillment blocks:

  1. Page Entry Prompts:
    • Read "entryFulfillment.messages" inside pages/<page_name>.json.
    • Extract speech variants under "text": {"text": [...]}. The simulated Agent turn MUST output this string verbatim.
  2. Form Slot-Filling Prompts:
    • Read "initialPromptFulfillment.messages" inside "form.parameters[].fillBehavior" in the page JSON.
    • The simulated Agent turn asking for the parameter MUST output these prompt strings verbatim.
  3. Transition Prompts:
    • Read "triggerFulfillment.messages" inside transition routes.

B. Programmatic Flow Traversal & Intent Discovery

To discover conversational paths and map execution sequences:

  1. Flow Start Point: Access flows/<flow_name>/<flow_name>.json and locate the "transitionRoutes" inside the root flow config. These define the entry intents (e.g. "intent": "support_request").
  2. State Traversal:
    • For each route, trace the target node: if the target is a sub-flow (e.g. "targetFlow": "flows/Billing/"), pivot to that sub-flow config.
    • If the target is a page (e.g. "targetPage": "Collect Email"), load and parse the page configuration file pages/Collect Email.json.
  3. Fulfillment Sequence Tracking:
    • Trace visited pages in chronological order along the path to extract the sequential webhook invocations (triggerFulfillment.webhook) and parameter mappings, mapping each valid end-to-end path as a scenario sequence.

Frequently asked questions

What does the Dfcx Framework Ingestor AI skill do?

Parsing and Ingestion directives for the Dialogflow CX (DFCX) agent structure.

Why use Dfcx Framework Ingestor on TypingMind?

Because you install it once and use it with any model. Dfcx Framework 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 Dfcx Framework 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/frameworks/dfcx. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dfcx Framework 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 Dfcx Framework Ingestor?

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

Is the Dfcx Framework 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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