Skip to content
cccskills
FreePublished on GitHub

ima-sdk-client-side

Integrate video and audio ads into websites, apps, and TV platforms with Google IMA SDK. Follow platform-specific guidance for client-side ad requests, playback coordination, error handling, and cleanup after ads finish.

OriginalShow the original description

Supports Interactive Media Ads (IMA) SDK. Use this skill for client-side ad insertion when you are requesting video ads for websites, apps, TVs or other platforms using VAST or VMAP. Do not use for Dynamic Ad Insertion (DAI), SSAI, or SGAI.

See how to install

Good for

  • Adding video ads to a website
  • Integrating ads into mobile and TV apps
  • Coordinating ad playback with main content
  • Handling ad loading and playback errors
  • Cleaning up advertising SDK resources

Files included (7)

  • SKILL.md2.1 KB
  • references/ima-sdk-android-guide.md5.5 KB
  • references/ima-sdk-ios-guide.md8.6 KB
  • references/ima-sdk-tvos-guide.md5.9 KB
  • references/ima-sdk-web-guide.md8.3 KB
  • references/ima-sdk-web-iframe-mode.md2.8 KB
  • references/ima-sdk-web-mobile-safari.md514 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

IMA SDK client-side

The Google IMA SDK (Interactive Media Ads) lets you load in-stream video and audio ads into websites, apps, TVs and other digital platforms. Use an IMA SDK to request ads from any VAST-compliant ad server and manage ad playback.

Prerequisites

Before integrating the IMA SDK, you must read the platform-specific guide below to support all platforms that your app can support:


Quick start (general workflow)

  1. Import the SDK: Prerequisites, dependencies.
  2. Initialization: Early setup, Warmup, Settings Configuration, and Ad UI Setup.
  3. Ad Request: Creating and triggering the request, User gesture compliance.
  4. Ad Load Success/Failure: Handling the load event to get the AdsManager, or handling early fatal errors.
  5. Ad Playback Events: Listening to playback events via AdsManager to coordinate content play/pause, and handling non-fatal LOG events and fatal active playback errors.
  6. Cleanup: Properly destroying the AdsManager to release resources and prevent memory leaks.

For detailed, platform-specific implementation details, always refer to the guides in the Prerequisites section.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.

  • Configure alerts for agent latency and error rates
  • Monitor rapid increases in token usage
  • Monitor safety triggers and permission denials
google/skills21.4KUpdated Oct 17, 2026

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

  • Choose a deployable model and supported hardware
  • Estimate hourly deployment costs before approval
  • Check deployment progress and test predictions
google/skills21.4KUpdated Oct 17, 2026

Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.

  • Listing endpoints and checking their configuration
  • Creating an endpoint before model deployment
  • Updating endpoint display names or labels
google/skills21.4KUpdated Oct 17, 2026

Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.

  • Build evaluation datasets from conversation logs
  • Generate simulated user scenarios
  • Choose scoring criteria for responses and tool use
google/skills21.4KUpdated Oct 17, 2026

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

  • Send a test prompt to Gemini
  • Call Llama or DeepSeek with the OpenAI SDK
  • Write code to call an existing custom endpoint
google/skills21.4KUpdated Oct 17, 2026

Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.

  • Moving a Gemini application to Google Cloud
  • Using Google Cloud credits for Gemini API calls
  • Unifying agent authentication and billing
google/skills21.4KUpdated Oct 17, 2026

See all skills by google

Report a problem with this skill