Data Manager Api Audience Ingestion logo

Data Manager Api Audience Ingestion

OrganizationPopular
google
data-manager-api-audience-ingestion

Guides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an entire audience for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).

Overview

Publishergoogle
Repositoryskills
Skill namedata-manager-api-audience-ingestion
Stars
20.1K
Forks
1.6K
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Data Manager Api Audience Ingestion 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.
  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/google/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/ads/data-manager-api-audience-ingestion .claude/skills/data-manager-api-audience-ingestion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Manager Api Audience Ingestion 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 Data Manager Api Audience Ingestion 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 Data Manager Api Audience Ingestion 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.

Data Manager API Audience Ingestion

Implementation Workflow

Prerequisites

  • Authentication & Library Installation: If you need to set up access to the Data Manager API or install the client and utility libraries, refer to the data-manager-api-setup skill.
  • Audience Creation (if needed): If the user does not have an existing audience or needs to create a new one, use the Create an Audience reference. This step provides the product_destination_id needed for the ingestion or removal requests.

Step 1: Identify Use Case & Read Documentation

  • Determine Destination Account Type: [CRITICAL] If it's not clear where the data is being sent (e.g., Google Ads, Display & Video 360, etc.), STOP and CLARIFY with the user BEFORE generating any code. Do not assume Google Ads by default. This maps to the account_type field of the operating_account in the Destination.
  • Read the implementation guide: Read the relevant guide for your destination and use case. Do this before answering questions or writing code because each destination has unique payload structures, consent rules, and required fields.
DestinationAudience TypeAccepted Data TypesUpload GuideRemove All/Replace All Guide
Google AdsCustomer Matchcomposite_data.user_data (contact info), mobile_data (device IDs), user_id_data (user IDs)Upload DataRemove All/Replace All
Display & Video 360 (DV360)Customer Matchcomposite_data.user_data (contact info), mobile_data (device IDs)Upload DataRemove All/Replace All

Step 2: Retrieve Code Sample

[!IMPORTANT] If writing or updating an ingestion script, ALWAYS retrieve the relevant code sample to use as a reference:

Step 3: Retrieve Migration Guides

[!IMPORTANT] If refactoring code to upgrade from another Google API, ALWAYS extract the full contents of the relevant field mapping guide.

Google Ads
Display & Video 360

Step 4: Implementation

Implement the ingestion logic using the following checkpoints:

  • Initialize Client: Instantiate the Data Manager client (IngestionServiceClient).
  • Define Destinations: Build the Destination object using the product_destination_id and the appropriate account configurations: operating_account (target account receiving data), login_account (if authenticating using a manager account or a data partner account), and linked_account (if you're a data partner accessing the account via a partner link to a manager account). STRONGLY RECOMMENDED: Refer to the Configure destinations and headers guide for more details on configuring destinations.
  • Format User Data: If sending an IngestAudienceMembersRequest or RemoveAudienceMembersRequest, refer to Formatting User Data to properly normalize and hash user identifiers using the utility library.
  • Construct Payload: Build the appropriate request payload based on the operation:
    • Add: IngestAudienceMembersRequest
    • Remove: RemoveAudienceMembersRequest
    • Remove All: RemoveAllAudienceMembersRequest
  • Support Validation: Support sending the validate_only boolean option on the payload to allow developers to validate schemas without actually applying changes.
  • Send Request: Execute the appropriate method and record the returned request_id for later diagnostics:
    • Add: ingest_audience_members
    • Remove: remove_audience_members
    • Remove All: remove_all_audience_members
  • Check for Ingestion Warnings: If any non-required field had a validation failure, the response from ingest_audience_members will also include field_warnings, a list of FieldWarning objects detailing the issues.
  • Retrieve Request Status: Check the status of the ingestion request using diagnostics. Since request processing is asynchronous, a successful response (HTTP 200 OK returning a request_id) only indicates the payload was received. To check if the records actually succeeded, partially succeeded, or failed to process, query client.retrieve_request_status using the request_id. Skipping this step is a common user mistake.

Critical Gotchas

  • If sending hashed user identifiers in user_data for ingest_audience_members or remove_audience_members, you must set the encoding field on the IngestAudienceMembersRequest to HEX or BASE64.
  • If uploading to a Customer Match audience, the terms_of_service field is required on the IngestAudienceMembersRequest to indicate the user has accepted the policies.
  • Only set the address field on UserIdentifier if all required fields (postal_code, family_name, given_name, region_code) are present; incomplete address fields will cause the API request to fail.
  • product_destination_id must be a numeric string. It is NOT a resource name.
  • The enum values for ConsentStatus are CONSENT_GRANTED and CONSENT_DENIED. Do not use the values GRANTED and DENIED.
  • Field names on UserIdentifier are email_address and phone_number. Do not use the Google Ads API field names hashed_email and hashed_phone_number.
  • Do not call the diagnostics endpoint (retrieve_request_status) if validate_only is set to true.

Error Handling & Troubleshooting

Inspecting Error Payloads & Ingestion Warnings

[!IMPORTANT] Refer to Understand API Errors for a detailed guide on how to understand the structure of errors and warnings returned by the API.

Retrieving Request Status (Diagnostics)

Periodically poll for status using exponential backoff, starting at least 30 minutes after sending the request.

  1. Call client.retrieve_request_status using RetrieveRequestStatusRequest(request_id=...).
  2. Loop through request_status_per_destination in the response to inspect each target's request_status.
  3. If processing is complete and request_status is SUCCESS, PARTIAL_SUCCESS, or FAILED, inspect diagnostic values:
    • Audience Status: Check the status specific to your request:
      • Ingest: Check the data-type-specific status nested under audience_members_ingestion_status (e.g., composite_data_ingestion_status).
      • Remove Individual Members: Check the data-type-specific status nested under audience_members_removal_status (e.g., composite_data_removal_status).
      • Remove All Members: There are no nested status fields or record counts available to check for this request type.
      • Record Count: If applicable (ingest or remove individual members), check record_count (nested inside the data-type-specific status object) which includes both success and failure.
      • Identifier Counts: If applicable (ingest or remove individual members), check the data-type-specific count field nested inside the status object (e.g., data_type_counts if uploading or removing composite data, or mobile_id_count if uploading or removing mobile IDs). Refer to the Diagnostics Guide for other count fields.
      • Match Rate Range: For uploads of user_data and composite_data, check upload_match_rate_range nested inside the status object.
    • Error Details: If status is FAILED or PARTIAL_SUCCESS, inspect each error's reason and record_count under error_info.error_counts.
    • Warning Details: Inspect each warning's reason and record_count under warning_info.warning_counts (even if the destination status is SUCCESS).

API Reference

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 Data Manager Api Audience Ingestion AI skill do?

Guides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an entire audience for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).

Why use Data Manager Api Audience Ingestion on TypingMind?

Because you install it once and use it with any model. Data Manager Api Audience Ingestion 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 Data Manager Api Audience Ingestion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/skills/tree/main/skills/ads/data-manager-api-audience-ingestion. 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 Data Manager Api Audience Ingestion?

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 Data Manager Api Audience Ingestion?

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

Is the Data Manager Api Audience Ingestion AI skill free?

Yes. It is published on GitHub by google 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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