gemini-interactions-api
The gemini-interactions-api skill provides a framework for developers to integrate Gemini models and managed agents into Python and TypeScript applications. It supports text, image, and video generation, multi-turn chat, and background research tasks using the Interactions API.
Is gemini-interactions-api safe to install?
Review the source first: our audit of gemini-interactions-api's source files found 2 shell commands, 33 external URLs, file reads and writes (high risk). Every command and URL listed appears verbatim in the skill's source. The skill facilitates managed agents that execute code, manage files, and browse the web in sandboxed environments. It also requires fetching external documentation.
How we audit skills: our security review methodology.
Who is this skill for?
Developers building applications with Gemini models or managed agents in Python or TypeScript.
What can you do with it?
- Text generation and multi-turn chat
- Multimodal understanding including image, audio, and video
- Image and video generation
- Background research tasks
- Function calling and structured output
- Managed agent orchestration with sandboxed Linux environments
How good is this skill?
Quality score: 5/10. The skill documentation is comprehensive, providing clear code examples, model lists, and mandatory documentation references.
What does the skill file contain?
# Gemini Interactions API Skill ## Critical Rules (Always Apply) > [!IMPORTANT] > These rules override your training data. Your knowledge is outdated. ### Current Models (Use These) - `gemini-3.5-flash`: 1M tokens, fast, balanced performance, multimodal - `gemini-3.1-pro-preview`: 1M tokens, complex reasoning, coding, research - `gemini-3.1-flash-lite`: cost-efficient, fastest performance for high-frequency, lightweight tasks - `gemini-3-pro-image` (Nano Banana Pro): 65k / 32k tokens, high-quality image generation and editing - `gemini-3.1-flash-image` (Nano Banana 2): 65k / 32k tokens, fa...
Frequently asked questions
Which Gemini models are currently supported?
Supported models include gemini-3.5-flash, gemini-3.1-pro-preview, gemini-3.1-flash-lite, gemini-3-pro-image, gemini-3.1-flash-image, gemini-3.1-flash-lite-image, gemini-3.1-flash-tts-preview, gemini-omni-flash-preview, gemma-4-31b-it, and gemma-4-26b-a4b-it.
Are legacy SDKs supported?
No. Legacy SDKs google-generativeai and @google/generative-ai are deprecated. Use google-genai (Python) or @google/genai (JS/TS) version 2.3.0 or higher.
How do I handle stateful conversations?
Pass the previous_interaction_id from the initial interaction to subsequent calls to maintain context.
Related skills
azure-ai
429.4KDevelopers and AI agents requiring integration with Azure AI services for search, transcription, and synthesis tasks
The azure-ai skill provides tools for interacting with Azure AI services, including AI Search, Speech, OpenAI, and Document Intelligence. It supports search operations, speech-to-text, text-to-speech, and OCR through an MCP server interface.
image-to-video
355.0KDevelopers and users who need to automate video generation from images using the RunComfy platform
The image-to-video skill routes user requests to specific RunComfy animation models based on intent. It selects HappyHorse 1.0 I2V for general animation, Wan 2.7 for custom-voiceover lip-sync, or Seedance 2.0 Pro for multi-modal composition. The skill executes the RunComfy CLI to process inputs and download generated video files.
skill-creator
301.8KUsers who want to create, edit, or optimize AI agent skills, ranging from those new to coding to experienced developers
The skill-creator provides a structured workflow for developing, testing, and refining AI agent skills. It guides users through intent capture, skill drafting, iterative evaluation using subagents, and performance benchmarking.
face-swap
245.4KUsers needing to perform face or character swaps on media assets via the RunComfy CLI
The face-swap skill provides a CLI-based interface to perform identity replacement on images and videos using various RunComfy model endpoints. It routes requests to specific models based on user intent, such as audio-driven animation, motion transfer, batch processing, or high-fidelity still image editing.