[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MMZE2Z743HY72SWDFX6C15":3,"skill-same:01M6MMZE2Z743HY72SWDFX6C15":117},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":64,"install":65,"resources":78},"01M6MMZE2Z743HY72SWDFX6C15","gemini-api",null,"Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Covers SDK usage (Python, JS\u002FTS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.","arkhe-genesis\u002FArkhe-OS","arkhe-genesis","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F272629719?v=4","01M6KJDTSTJD2AD5RFPVZNGYCP","Rafael Oliveira","01M6KJDTXSTA997KDK184EA55A",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"gemini","gemini api","api","Tools","usage","guides","live","platform","caching","active","environment","credentials","google","Enterprise","ai sdk","batch","capabilities","google cloud","python","go","2026-11-10 10:12:30",false,"---\nname: gemini-api\ndescription: Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Covers SDK usage (Python, JS\u002FTS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.\ncompatibility: Requires active Google Cloud credentials and Agent Platform API enabled.\n---\n\nIMPORTANT: Agent Platform (full name Gemini Enterprise Agent Platform) was previously named \"Vertex AI\" and many web resources use the legacy branding.\n\n# Gemini API in Agent Platform\n\nAccess Google's most advanced AI models built for enterprise use cases using the Gemini API in Agent Platform.\n\nProvide these key capabilities:\n\n- **Text generation** - Chat, completion, summarization\n- **Multimodal understanding** - Process images, audio, video, and documents\n- **Function calling** - Let the model invoke your functions\n- **Structured output** - Generate valid JSON matching your schema\n- **Context caching** - Cache large contexts for efficiency\n- **Embeddings** - Generate text embeddings for semantic search\n- **Live Realtime API** - Bidirectional streaming for low latency Voice and Video interactions\n- **Batch Prediction** - Handle massive async dataset prediction workloads\n\n## Core Directives\n\n- **Unified SDK**: ALWAYS use the Gen AI SDK (`google-genai` for Python, `@google\u002Fgenai` for JS\u002FTS, `google.golang.org\u002Fgenai` for Go, `com.google.genai:google-genai` for Java, `Google.GenAI` for C#).\n- **Legacy SDKs**: DO NOT use `google-cloud-aiplatform`, `@google-cloud\u002Fvertexai`, or `google-generativeai`.\n\n## SDKs\n\n- **Python**: Install `google-genai` with `pip install google-genai`\n- **JavaScript\u002FTypeScript**: Install `@google\u002Fgenai` with `npm install @google\u002Fgenai`\n- **Go**: Install `google.golang.org\u002Fgenai` with `go get google.golang.org\u002Fgenai`\n- **C#\u002F.NET**: Install `Google.GenAI` with `dotnet add package Google.GenAI`\n- **Java**:\n  - groupId: `com.google.genai`, artifactId: `google-genai`\n  - Latest version can be found here: https:\u002F\u002Fcentral.sonatype.com\u002Fartifact\u002Fcom.google.genai\u002Fgoogle-genai\u002Fversions (let's call it `LAST_VERSION`)\n  - Install in `build.gradle`:\n\n    ```\n    implementation(\"com.google.genai:google-genai:${LAST_VERSION}\")\n    ```\n\n  - Install Maven dependency in `pom.xml`:\n\n    ```xml\n    \u003Cdependency>\n\t    \u003CgroupId>com.google.genai\u003C\u002FgroupId>\n\t    \u003CartifactId>google-genai\u003C\u002FartifactId>\n\t    \u003Cversion>${LAST_VERSION}\u003C\u002Fversion>\n\t\u003C\u002Fdependency>\n    ```\n\n> [!WARNING]\n> Legacy SDKs like `google-cloud-aiplatform`, `@google-cloud\u002Fvertexai`, and `google-generativeai` are deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.\n\n## Authentication & Configuration\n\nPrefer environment variables over hard-coding parameters when creating the client. Initialize the client without parameters to automatically pick up these values.\n\n### Application Default Credentials (ADC)\nSet these variables for standard [Google Cloud authentication](https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fstart\u002Fgcp-auth):\n```bash\nexport GOOGLE_CLOUD_PROJECT='your-project-id'\nexport GOOGLE_CLOUD_LOCATION='global'\nexport GOOGLE_GENAI_USE_VERTEXAI=true\n```\n- By default, use `location=\"global\"` to access the global endpoint, which provides automatic routing to regions with available capacity.\n- If a user explicitly asks to use a specific region (e.g., `us-central1`, `europe-west4`), specify that region in the `GOOGLE_CLOUD_LOCATION` parameter instead. Reference the [supported regions documentation](https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Flearn\u002Flocations) if needed.\n\n### Agent Platform in Express Mode\nSet these variables when using [Express Mode](https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fstart\u002Fapi-keys?usertype=expressmode) with an API key:\n```bash\nexport GOOGLE_API_KEY='your-api-key'\nexport GOOGLE_GENAI_USE_VERTEXAI=true\n```\n\n### Initialization\nInitialize the client without arguments to pick up environment variables:\n```python\nfrom google import genai\nclient = genai.Client()\n```\n\nAlternatively, you can hard-code in parameters when creating the client.\n\n```python\nfrom google import genai\nclient = genai.Client(vertexai=True, project=\"your-project-id\", location=\"global\")\n```\n\n## Models\n\n- Use `gemini-3.1-pro-preview` for complex reasoning, coding, research (1M tokens)\n  - IMPORTANT: Do not use `gemini-3-pro-preview`\n- Use `gemini-3-flash-preview` for fast, balanced performance, multimodal (1M tokens)\n- Use `gemini-3-pro-image-preview` for Nano Banana Pro image generation and editing\n- Use `gemini-3.1-flash-image-preview` for Nano Banana 2 image generation and editing\n- Use `gemini-live-2.5-flash-native-audio` for Live Realtime API including native audio\n\nUse the following models only if explicitly requested:\n\n- `gemini-2.5-flash-image`\n- `gemini-2.5-flash`\n- `gemini-2.5-flash-lite`\n- `gemini-2.5-pro`\n\n> [!IMPORTANT]\n> Models like `gemini-2.0-*`, `gemini-1.5-*`, `gemini-1.0-*`, `gemini-pro` are legacy and deprecated. Use the new models above. Your knowledge is outdated.\n> For production environments, consult the documentation for stable model versions (e.g. `gemini-3-flash`).\n\n## Quick Start\n\n### Python\n```python\nfrom google import genai\nclient = genai.Client()\nresponse = client.models.generate_content(\n    model=\"gemini-3-flash-preview\",\n    contents=\"Explain quantum computing\"\n)\nprint(response.text)\n```\n\n### TypeScript\u002FJavaScript\n```typescript\nimport { GoogleGenAI } from \"@google\u002Fgenai\";\nconst ai = new GoogleGenAI({ vertexai: { project: \"your-project-id\", location: \"global\" } });\nconst response = await ai.models.generateContent({\n    model: \"gemini-3-flash-preview\",\n    contents: \"Explain quantum computing\"\n});\nconsole.log(response.text);\n```\n\n### Go\n```go\npackage main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"log\"\n\t\"google.golang.org\u002Fgenai\"\n)\n\nfunc main() {\n\tctx := context.Background()\n\tclient, err := genai.NewClient(ctx, &genai.ClientConfig{\n\t\tBackend:  genai.BackendVertexAI,\n\t\tProject:  \"your-project-id\",\n\t\tLocation: \"global\",\n\t})\n\tif err != nil {\n\t\tlog.Fatal(err)\n\t}\n\n\tresp, err := client.Models.GenerateContent(ctx, \"gemini-3-flash-preview\", genai.Text(\"Explain quantum computing\"), nil)\n\tif err != nil {\n\t\tlog.Fatal(err)\n\t}\n\n\tfmt.Println(resp.Text)\n}\n```\n\n### Java\n```java\nimport com.google.genai.Client;\nimport com.google.genai.types.GenerateContentResponse;\n\npublic class GenerateTextFromTextInput {\n  public static void main(String[] args) {\n    Client client = Client.builder().vertexAi(true).project(\"your-project-id\").location(\"global\").build();\n    GenerateContentResponse response =\n        client.models.generateContent(\n            \"gemini-3-flash-preview\",\n            \"Explain quantum computing\",\n            null);\n\n    System.out.println(response.text());\n  }\n}\n```\n\n### C#\u002F.NET\n```csharp\nusing Google.GenAI;\n\nvar client = new Client(\n    project: \"your-project-id\",\n    location: \"global\",\n    vertexAI: true\n);\n\nvar response = await client.Models.GenerateContent(\n    \"gemini-3-flash-preview\",\n    \"Explain quantum computing\"\n);\n\nConsole.WriteLine(response.Text);\n```\n\n## API spec & Documentation (source of truth)\n\nWhen implementing or debugging API integration for Agent Platform, refer to the official Agent Platform documentation:\n- **Agent Platform Documentation**: https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Foverview\n- **REST API Reference**: https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Freference\u002Frest\n\nThe Gen AI SDK on Agent Platform uses the `v1beta1` or `v1` REST API endpoints (e.g., `https:\u002F\u002F{LOCATION}-aiplatform.googleapis.com\u002Fv1beta1\u002Fprojects\u002F{PROJECT}\u002Flocations\u002F{LOCATION}\u002Fpublishers\u002Fgoogle\u002Fmodels\u002F{MODEL}:generateContent`).\n\n> [!TIP]\n> **Use the Developer Knowledge MCP Server**: If the `search_documents` or `get_document` tools are available, use them to find and retrieve official documentation for Google Cloud and Agent Platform directly within the context. This is the preferred method for getting up-to-date API details and code snippets.\n\n## Workflows and Code Samples\n\nReference the [Python Docs Samples repository](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fpython-docs-samples\u002Ftree\u002Fmain\u002Fgenai) for additional code samples and specific usage scenarios.\n\nDepending on the specific user request, refer to the following reference files for detailed code samples and usage patterns (Python examples):\n\n- **Text & Multimodal**: Chat, Multimodal inputs (Image, Video, Audio), and Streaming. See [references\u002Ftext_and_multimodal.md](references\u002Ftext_and_multimodal.md)\n- **Embeddings**: Generate text embeddings for semantic search. See [references\u002Fembeddings.md](references\u002Fembeddings.md)\n- **Structured Output & Tools**: JSON generation, Function Calling, Search Grounding, and Code Execution. See [references\u002Fstructured_and_tools.md](references\u002Fstructured_and_tools.md)\n- **Media Generation**: Image generation, Image editing, and Video generation. See [references\u002Fmedia_generation.md](references\u002Fmedia_generation.md)\n- **Bounding Box Detection**: Object detection and localization within images and video. See [references\u002Fbounding_box.md](references\u002Fbounding_box.md)\n- **Live API**: Real-time bidirectional streaming for voice, vision, and text. See [references\u002Flive_api.md](references\u002Flive_api.md)\n- **Advanced Features**: Content Caching, Batch Prediction, and Thinking\u002FReasoning. See [references\u002Fadvanced_features.md](references\u002Fadvanced_features.md)\n- **Safety**: Adjusting Responsible AI filters and thresholds. See [references\u002Fsafety.md](references\u002Fsafety.md)\n- **Model Tuning**: Supervised Fine-Tuning and Preference Tuning. See [references\u002Fmodel_tuning.md](references\u002Fmodel_tuning.md)","IMPORTANT: Agent Platform (full name Gemini Enterprise Agent Platform) was previously named \"Vertex AI\" and many web resources use the legacy branding.\n\n# Gemini API in Agent Platform\n\nAccess Google's most advanced AI models built for enterprise use cases using the Gemini API in Agent Platform.\n\nProvide these key capabilities:\n\n- **Text generation** - Chat, completion, summarization\n- **Multimodal understanding** - Process images, audio, video, and documents\n- **Function calling** - Let the model invoke your functions\n- **Structured output** - Generate valid JSON matching your schema\n- **Context caching** - Cache large contexts for efficiency\n- **Embeddings** - Generate text embeddings for semantic search\n- **Live Realtime API** - Bidirectional streaming for low latency Voice and Video interactions\n- **Batch Prediction** - Handle massive async dataset prediction workloads\n\n## Core Directives\n\n- **Unified SDK**: ALWAYS use the Gen AI SDK (`google-genai` for Python, `@google\u002Fgenai` for JS\u002FTS, `google.golang.org\u002Fgenai` for Go, `com.google.genai:google-genai` for Java, `Google.GenAI` for C#).\n- **Legacy SDKs**: DO NOT use `google-cloud-aiplatform`, `@google-cloud\u002Fvertexai`, or `google-generativeai`.\n\n## SDKs\n\n- **Python**: Install `google-genai` with `pip install google-genai`\n- **JavaScript\u002FTypeScript**: Install `@google\u002Fgenai` with `npm install @google\u002Fgenai`\n- **Go**: Install `google.golang.org\u002Fgenai` with `go get google.golang.org\u002Fgenai`\n- **C#\u002F.NET**: Install `Google.GenAI` with `dotnet add package Google.GenAI`\n- **Java**:\n  - groupId: `com.google.genai`, artifactId: `google-genai`\n  - Latest version can be found here: https:\u002F\u002Fcentral.sonatype.com\u002Fartifact\u002Fcom.google.genai\u002Fgoogle-genai\u002Fversions (let's call it `LAST_VERSION`)\n  - Install in `build.gradle`:\n\n    ```\n    implementation(\"com.google.genai:google-genai:${LAST_VERSION}\")\n    ```\n\n  - Install Maven dependency in `pom.xml`:\n\n    ```xml\n    \u003Cdependency>\n\t    \u003CgroupId>com.google.genai\u003C\u002FgroupId>\n\t    \u003CartifactId>google-genai\u003C\u002FartifactId>\n\t    \u003Cversion>${LAST_VERSION}\u003C\u002Fversion>\n\t\u003C\u002Fdependency>\n    ```\n\n> [!WARNING]\n> Legacy SDKs like `google-cloud-aiplatform`, `@google-cloud\u002Fvertexai`, and `google-generativeai` are deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.\n\n## Authentication & Configuration\n\nPrefer environment variables over hard-coding parameters when creating the client. Initialize the client without parameters to automatically pick up these values.\n\n### Application Default Credentials (ADC)\nSet these variables for standard [Google Cloud authentication](https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fstart\u002Fgcp-auth):\n```bash\nexport GOOGLE_CLOUD_PROJECT='your-project-id'\nexport GOOGLE_CLOUD_LOCATION='global'\nexport GOOGLE_GENAI_USE_VERTEXAI=true\n```\n- By default, use `location=\"global\"` to access the global endpoint, which provides automatic routing to regions with available capacity.\n- If a user explicitly asks to use a specific region (e.g., `us-central1`, `europe-west4`), specify that region in the `GOOGLE_CLOUD_LOCATION` parameter instead. Reference the [supported regions documentation](https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Flearn\u002Flocations) if needed.\n\n### Agent Platform in Express Mode\nSet these variables when using [Express Mode](https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fstart\u002Fapi-keys?usertype=expressmode) with an API key:\n```bash\nexport GOOGLE_API_KEY='your-api-key'\nexport GOOGLE_GENAI_USE_VERTEXAI=true\n```\n\n### Initialization\nInitialize the client without arguments to pick up environment variables:\n```python\nfrom google import genai\nclient = genai.Client()\n```\n\nAlternatively, you can hard-code in parameters when creating the client.\n\n```python\nfrom google import genai\nclient = genai.Client(vertexai=True, project=\"your-project-id\", location=\"global\")\n```\n\n## Models\n\n- Use `gemini-3.1-pro-preview` for complex reasoning, coding, research (1M tokens)\n  - IMPORTANT: Do not use `gemini-3-pro-preview`\n- Use `gemini-3-flash-preview` for fast, balanced performance, multimodal (1M tokens)\n- Use `gemini-3-pro-image-preview` for Nano Banana Pro image generation and editing\n- Use `gemini-3.1-flash-image-preview` for Nano Banana 2 image generation and editing\n- Use `gemini-live-2.5-flash-native-audio` for Live Realtime API including native audio\n\nUse the following models only if explicitly requested:\n\n- `gemini-2.5-flash-image`\n- `gemini-2.5-flash`\n- `gemini-2.5-flash-lite`\n- `gemini-2.5-pro`\n\n> [!IMPORTANT]\n> Models like `gemini-2.0-*`, `gemini-1.5-*`, `gemini-1.0-*`, `gemini-pro` are legacy and deprecated. Use the new models above. Your knowledge is outdated.\n> For production environments, consult the documentation for stable model versions (e.g. `gemini-3-flash`).\n\n## Quick Start\n\n### Python\n```python\nfrom google import genai\nclient = genai.Client()\nresponse = client.models.generate_content(\n    model=\"gemini-3-flash-preview\",\n    contents=\"Explain quantum computing\"\n)\nprint(response.text)\n```\n\n### TypeScript\u002FJavaScript\n```typescript\nimport { GoogleGenAI } from \"@google\u002Fgenai\";\nconst ai = new GoogleGenAI({ vertexai: { project: \"your-project-id\", location: \"global\" } });\nconst response = await ai.models.generateContent({\n    model: \"gemini-3-flash-preview\",\n    contents: \"Explain quantum computing\"\n});\nconsole.log(response.text);\n```\n\n### Go\n```go\npackage main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"log\"\n\t\"google.golang.org\u002Fgenai\"\n)\n\nfunc main() {\n\tctx := context.Background()\n\tclient, err := genai.NewClient(ctx, &genai.ClientConfig{\n\t\tBackend:  genai.BackendVertexAI,\n\t\tProject:  \"your-project-id\",\n\t\tLocation: \"global\",\n\t})\n\tif err != nil {\n\t\tlog.Fatal(err)\n\t}\n\n\tresp, err := client.Models.GenerateContent(ctx, \"gemini-3-flash-preview\", genai.Text(\"Explain quantum computing\"), nil)\n\tif err != nil {\n\t\tlog.Fatal(err)\n\t}\n\n\tfmt.Println(resp.Text)\n}\n```\n\n### Java\n```java\nimport com.google.genai.Client;\nimport com.google.genai.types.GenerateContentResponse;\n\npublic class GenerateTextFromTextInput {\n  public static void main(String[] args) {\n    Client client = Client.builder().vertexAi(true).project(\"your-project-id\").location(\"global\").build();\n    GenerateContentResponse response =\n        client.models.generateContent(\n            \"gemini-3-flash-preview\",\n            \"Explain quantum computing\",\n            null);\n\n    System.out.println(response.text());\n  }\n}\n```\n\n### C#\u002F.NET\n```csharp\nusing Google.GenAI;\n\nvar client = new Client(\n    project: \"your-project-id\",\n    location: \"global\",\n    vertexAI: true\n);\n\nvar response = await client.Models.GenerateContent(\n    \"gemini-3-flash-preview\",\n    \"Explain quantum computing\"\n);\n\nConsole.WriteLine(response.Text);\n```\n\n## API spec & Documentation (source of truth)\n\nWhen implementing or debugging API integration for Agent Platform, refer to the official Agent Platform documentation:\n- **Agent Platform Documentation**: https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Foverview\n- **REST API Reference**: https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Freference\u002Frest\n\nThe Gen AI SDK on Agent Platform uses the `v1beta1` or `v1` REST API endpoints (e.g., `https:\u002F\u002F{LOCATION}-aiplatform.googleapis.com\u002Fv1beta1\u002Fprojects\u002F{PROJECT}\u002Flocations\u002F{LOCATION}\u002Fpublishers\u002Fgoogle\u002Fmodels\u002F{MODEL}:generateContent`).\n\n> [!TIP]\n> **Use the Developer Knowledge MCP Server**: If the `search_documents` or `get_document` tools are available, use them to find and retrieve official documentation for Google Cloud and Agent Platform directly within the context. This is the preferred method for getting up-to-date API details and code snippets.\n\n## Workflows and Code Samples\n\nReference the [Python Docs Samples repository](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fpython-docs-samples\u002Ftree\u002Fmain\u002Fgenai) for additional code samples and specific usage scenarios.\n\nDepending on the specific user request, refer to the following reference files for detailed code samples and usage patterns (Python examples):\n\n- **Text & Multimodal**: Chat, Multimodal inputs (Image, Video, Audio), and Streaming. See [references\u002Ftext_and_multimodal.md](references\u002Ftext_and_multimodal.md)\n- **Embeddings**: Generate text embeddings for semantic search. See [references\u002Fembeddings.md](references\u002Fembeddings.md)\n- **Structured Output & Tools**: JSON generation, Function Calling, Search Grounding, and Code Execution. See [references\u002Fstructured_and_tools.md](references\u002Fstructured_and_tools.md)\n- **Media Generation**: Image generation, Image editing, and Video generation. See [references\u002Fmedia_generation.md](references\u002Fmedia_generation.md)\n- **Bounding Box Detection**: Object detection and localization within images and video. See [references\u002Fbounding_box.md](references\u002Fbounding_box.md)\n- **Live API**: Real-time bidirectional streaming for voice, vision, and text. See [references\u002Flive_api.md](references\u002Flive_api.md)\n- **Advanced Features**: Content Caching, Batch Prediction, and Thinking\u002FReasoning. See [references\u002Fadvanced_features.md](references\u002Fadvanced_features.md)\n- **Safety**: Adjusting Responsible AI filters and thresholds. See [references\u002Fsafety.md](references\u002Fsafety.md)\n- **Model Tuning**: Supervised Fine-Tuning and Preference Tuning. See [references\u002Fmodel_tuning.md](references\u002Fmodel_tuning.md)",{"name":5,"description":7,"compatibility":57},"Requires active Google Cloud credentials and Agent Platform API enabled.","main","74cd3c4137064fdcde71ed9fe5c4bff58921d899","components\u002Fskills\u002Fcloud\u002Fgemini-api",0,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS","https:\u002F\u002Fgithub.com\u002Farkhe-genesis","2026-11-08 09:08:55",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add arkhe-genesis\u002FArkhe-OS --skill gemini-api",{"tool":71,"command":72},"bunx","bunx skills add arkhe-genesis\u002FArkhe-OS --skill gemini-api",{"tool":74,"command":75},"pnpm","pnpm dlx skills add arkhe-genesis\u002FArkhe-OS --skill gemini-api","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002FSKILL.md",true,[79,85,89,93,97,101,105,109,113],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fadvanced_features.md","REFERENCE","参考文档",4493,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Fadvanced_features.md",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fbounding_box.md",1866,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Fbounding_box.md",{"path":90,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fembeddings.md",590,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Fembeddings.md",{"path":94,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Flive_api.md",1064,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Flive_api.md",{"path":98,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fmedia_generation.md",3042,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Fmedia_generation.md",{"path":102,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fmodel_tuning.md",934,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Fmodel_tuning.md",{"path":106,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fsafety.md",1878,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Fsafety.md",{"path":110,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fstructured_and_tools.md",3786,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Fstructured_and_tools.md",{"path":114,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Ftext_and_multimodal.md",2312,"https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Fcomponents\u002Fskills\u002Fcloud\u002Fgemini-api\u002Freferences\u002Ftext_and_multimodal.md",[118,141,160,178,203,220],{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":52},"01M6MN97RPP1Q9BG489WHW70R8","career-ops","AI job search command center -- evaluate offers, generate CVs, scan portals, track applications","https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Ftools\u002F.ai-integrations\u002Fvscode\u002F.claude\u002Fskills\u002Fcareer-ops\u002FSKILL.md",[],[125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140],"ops","career","career ops","track","applications","evaluate","mode","true","command center","cvs","scan","command","offers","Job Search","search","generate",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":147,"publishedAt":52},"01M6MN93GFX8W9J1PCKDKZVD3N","zoom-out","Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.","https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Ftools\u002F.agents\u002Fskills\u002Fzoom-out\u002FSKILL.md",[],[148,149,150,151,152,153,154,155,132,156,157,158,159],"zoom out","zoom","use","how","context","code","it","perspective","re","you","with","understand",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":167,"publishedAt":52},"01M6MN8Y2ETBC91BGREF4D39JH","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.","https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Ftools\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[166],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[168,169,170,158,171,172,173,174,175,150,176,177],"write","write a skill","progressive disclosure","resources","new agent","structure","new skill","build","user","create",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":185,"publishedAt":52},"01M6MN8RWE2V2PPFVPD0G8FHKF","triage","Triage issues through a state machine driven by triage roles. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.","https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Ftools\u002F.agents\u002Fskills\u002Ftriage\u002FSKILL.md",[184],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[180,186,187,177,188,189,190,191,192,193,194,150,195,196,197,198,199,200,201,202],"bugs","state machine","requests","machine","feature","roles","issue workflow","manage","issue","issues","state","for","feature requests","prepare","triage issues","workflow","review",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":212,"publishedAt":52},"01M6MN8KVDBD28672WMER7HPXM","to-prd","Turn the current conversation context into a PRD and publish it to the project issue tracker. Use when user wants to create a PRD from the current context.","https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Ftools\u002F.agents\u002Fskills\u002Fto-prd\u002FSKILL.md",[209],{"categoryNo":210,"categoryName":211,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000031","产品经理",[213,214,150,176,152,215,177,194,216,154,217,218,219],"to prd","prd","publish","issue tracker","project","conversation","tracker",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":226,"publishedAt":52},"01M6MN8E9CGYVWVDDR0NJEFDBB","to-issues","Break a plan, spec, or PRD into independently-grabbable issues on the project issue tracker using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues.","https:\u002F\u002Fgithub.com\u002Farkhe-genesis\u002FArkhe-OS\u002Fblob\u002Fmain\u002Ftools\u002F.agents\u002Fskills\u002Fto-issues\u002FSKILL.md",[],[195,227,228,217,229,230,216,231,232,219,194,233,150,176,234,235,236,214,177],"to issues","spec","down","break","break down","tickets","implementation","work","convert","plan"]