Vertex AI API Dev
JetBrains/skills
Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK.
A skill your agent uses when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform.
$ npx skills add google/skills --skill gemini-api -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills gemini-api --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/gemini-api .claude/skills/gemini-api && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "gemini-api" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gemini-api into .claude/skills/gemini-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-api", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/gemini-apiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill gemini-api -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills gemini-api --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/gemini-api .agents/skills/gemini-api && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gemini-api" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gemini-api into .agents/skills/gemini-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-api", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill gemini-api -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills gemini-api --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/gemini-api .cursor/skills/gemini-api && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gemini-api" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gemini-api into .cursor/skills/gemini-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-api", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/gemini-api--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill gemini-api -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills gemini-api --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/gemini-api .gemini/skills/gemini-api && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gemini-api" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gemini-api into .gemini/skills/gemini-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-api", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills gemini-apiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill gemini-api -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/gemini-api .github/skills/gemini-api && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gemini-api" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gemini-api into .github/skills/gemini-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-api", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill gemini-api -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills gemini-api --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/gemini-api .opencode/skills/gemini-api && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gemini-api" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gemini-api into .opencode/skills/gemini-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemini-api", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
gemini-apiA skill your agent uses when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform.
Gemini API is an agent skill from google/skills, published by the product's own GitHub organization. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `references/advanced_features.md`, `references/bounding_box.md` and `references/embeddings.md`). Compatibility notes: Requires active Google Cloud credentials and Agent Platform API enabled.
It sits in AI & LLM Engineering, covering LLM API integration and Caching. It works with Google Gemini, C#, Java and Python. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit 8a1ac05. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pipnpmgodotnetFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.cloud.google.comcentral.sonatype.comgithub.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
GOOGLE_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires active Google Cloud credentials and Agent Platform API enabled.
From compatibility in the SKILL.md frontmatter.
Gemini API loads about 2.6k tokens when it runs, and up to ~9.7k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 709 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from google/skills at commit 8a1ac05, republished under its Apache-2.0 licence (© google). 709 words, ~2,576 tokens.
.claude/skills/gemini-api/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.[!IMPORTANT] Agent Platform (full name Gemini Enterprise Agent Platform) was previously named "Vertex AI" and many web resources use the legacy branding.
Access Google's most advanced AI models built for enterprise use cases using the Gemini API in Agent Platform.
Provide these key capabilities:
google-genai for Python, @google/genai for JS/TS, google.golang.org/genai for Go, com.google.genai:google-genai for Java, Google.GenAI for C#).google-cloud-aiplatform, @google-cloud/vertexai, or google-generativeai.google-genai with pip install google-genai@google/genai with npm install @google/genaigoogle.golang.org/genai with go get google.golang.org/genaiGoogle.GenAI with dotnet add package Google.GenAIgroupId: com.google.genai, artifactId: google-genai
Latest version can be found here: https://central.sonatype.com/artifact/com.google.genai/google-genai/versions (let's call it LAST_VERSION)
Install in build.gradle:
implementation("com.google.genai:google-genai:${LAST_VERSION}")Install Maven dependency in pom.xml:
<dependency>
<groupId>com.google.genai</groupId>
<artifactId>google-genai</artifactId>
<version>${LAST_VERSION}</version>
</dependency>[!WARNING] Legacy SDKs like
google-cloud-aiplatform,@google-cloud/vertexai, andgoogle-generativeaiare deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.
Prefer environment variables over hard-coding parameters when creating the client. Initialize the client without parameters to automatically pick up these values.
Set these variables for standard Google Cloud authentication:
export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='global'
export GOOGLE_GENAI_USE_ENTERPRISE=truelocation="global" to access the global endpoint, which provides automatic routing to regions with available capacity.us-central1, europe-west4), specify that region in the GOOGLE_CLOUD_LOCATION parameter instead. Reference the supported regions documentation if needed.Set these variables when using Express Mode with an API key:
export GOOGLE_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_ENTERPRISE=trueInitialize the client without arguments to pick up environment variables:
from google import genai
client = genai.Client()Alternatively, you can hard-code in parameters when creating the client.
from google import genai
client = genai.Client(
enterprise=True,
project="your-project-id",
location="global",
)gemini-3.8-flash for fast, balanced performance, multimodal (1M tokens)gemini-3.1-pro-preview (which replaces gemini-3-pro-preview) for complex reasoning, coding, research (1M tokens)gemini-3.5-flash-lite for high-frequency, lightweight tasks (1M tokens)gemini-3-pro-image (aka Nano Banana Pro) for high-quality image generation and editinggemini-3.1-flash-image (aka Nano Banana 2) for medium-quality image generation and editinggemini-3.1-flash-lite-image (aka Nano Banana 2 Lite) for fast image generation and editinggemini-live-2.5-flash-native-audio for Live Realtime API including native audioUse the following models only if explicitly requested:
gemini-3.7-flashgemini-3.6-flashgemini-3.5-flashgemini-3.1-flash-litegemini-2.5-flash-imagegemini-2.5-flashgemini-2.5-flash-litegemini-2.5-pro[!IMPORTANT] Models like
gemini-2.0-*,gemini-1.5-*,gemini-1.0-*,gemini-proare legacy and deprecated. Use the new models above. Your knowledge is outdated. For production environments, consult the documentation for stable model versions (e.g.gemini-3.8-flash).
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.8-flash",
contents="Explain quantum computing",
)
print(response.text)import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ enterprise: { project: "your-project-id", location: "global" } });
const response = await ai.models.generateContent({
model: "gemini-3.8-flash",
contents: "Explain quantum computing"
});
console.log(response.text);package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
Backend: genai.BackendVertexAI,
Project: "your-project-id",
Location: "global",
})
if err != nil {
log.Fatal(err)
}
resp, err := client.Models.GenerateContent(ctx, "gemini-3.8-flash", genai.Text("Explain quantum computing"), nil)
if err != nil {
log.Fatal(err)
}
fmt.Println(resp.Text)
}import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;
public class GenerateTextFromTextInput {
public static void main(String[] args) {
Client client = Client.builder().enterprise(true).project("your-project-id").location("global").build();
GenerateContentResponse response =
client.models.generateContent(
"gemini-3.8-flash",
"Explain quantum computing",
null);
System.out.println(response.text());
}
}using Google.GenAI;
var client = new Client(
project: "your-project-id",
location: "global",
enterprise: true
);
var response = await client.Models.GenerateContent(
"gemini-3.8-flash",
"Explain quantum computing"
);
Console.WriteLine(response.Text);When implementing or debugging API integration for Agent Platform, refer to the official Agent Platform documentation:
The Gen AI SDK on Agent Platform uses the v1beta1 or v1 REST API endpoints (e.g., https://{LOCATION}-aiplatform.googleapis.com/v1beta1/projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}:generateContent).
[!TIP] Use the Developer Knowledge MCP Server: If the
search_documentsorget_documenttools 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.
Reference the Python Docs Samples repository for additional code samples and specific usage scenarios.
Depending on the specific user request, refer to the following reference files for detailed code samples and usage patterns (Python examples):
© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 9 other files (references) in skills/cloud/gemini-api of google/skills.
Open the folder on GitHubat commit 8a1ac05
We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in google/skills, which our catalogue first saw on October 7, 2026.
Gemini API next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gemini API this skillgoogle/skills | 21k | 3 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Vertex AI API DevJetBrains/skills | 363 | 1 repos | ~2.4k | Automated safety check: Pass | None | |
| Gemini API DevAyuilos/Miffan | 182 | 1 repos | ~1.4k | Automated safety check: Pass | AGPL-3.0 | |
| Claude API Developmentwarpdotdev/warp | 65k | 3 repos | ~8.2k | Automated safety check: Pass | Apache-2.0 | |
| Google Genai SDK Pythoncnemri/google-genai-skills | 127 | — | ~422 | Automated safety check: Pass | MIT | |
| Gemini API Devaiskillstore/marketplace | 430 | 3 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 |
JetBrains/skills
Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK.
Ayuilos/Miffan
A skill your agent uses when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function…
warpdotdev/warp
Guides building, debugging and tuning apps on the Claude API and Anthropic SDK, including prompt caching, and migrating code between Claude model versions.
cnemri/google-genai-skills
Expert guidance for writing Python code using the official Google GenAI SDK (google-genai) for Gemini API and Vertex AI.
aiskillstore/marketplace
A skill your agent uses when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function…
JetBrains/skills
A skill your agent uses when building applications with Gemini models, Gemini API, working with multimodal content (text, images, audio, video), implementing function calling, using structured…
google/skills
Manages Google Cloud Privileged Access Manager entitlements and grants: create and edit entitlements, request temporary access, and approve or deny pending grants.
google/skills
Writes Terraform alerting policies for AI agents that emit OpenTelemetry metrics, covering reliability, cost, safety, security and quality signals on Google Cloud.
google/skills
Deploys open models or custom weights from Model Garden to Agent Platform endpoints, checks deployment status and cleans up endpoints, confirming before any change.
google/skills
Searches, manages and scaffolds skills in the Gemini Enterprise Agent Platform Skill Registry using bundled Python scripts and Google Cloud credentials.
google/skills
Designs GCP infrastructure as local Terraform, validates and scans it against best practices, then imports it to Application Design Center for deployment and troubleshooting.
google/skills
Analyzes BigQuery slot use, query costs and execution bottlenecks from INFORMATION_SCHEMA to diagnose slow queries, slot contention and unpartitioned scans.
Categories
A skill your agent uses when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Gemini API is an agent skill from google/skills, published by the product's own GitHub organization. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform.
Gemini API fits situations like: the user asks about using Gemini in an enterprise environment; explicitly mentions Vertex AI.
Run `npx skills add google/skills --skill gemini-api -a claude-code`. Or copy the skill folder (skills/cloud/gemini-api in google/skills) into .claude/skills/gemini-api in your project. Claude Code loads it when a task matches its description.
Run `npx skills add google/skills --skill gemini-api -a codex`. Or copy the skill folder (skills/cloud/gemini-api in google/skills) into .agents/skills/gemini-api in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill gemini-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gemini-api, .gemini/skills/gemini-api, .github/skills/gemini-api and .opencode/skills/gemini-api in your project.
Going by SKILL.md and its folder, Gemini API needs the command-line tools its instructions call (pip, npm, go and dotnet) and credentials named GOOGLE_API_KEY. Our summary lists: Python 3; Node.js; A credential in GOOGLE_API_KEY. Compatibility (from SKILL.md): Requires active Google Cloud credentials and Agent Platform API enabled..
SKILL.md names 3 domains. As links in the text: docs.cloud.google.com, central.sonatype.com and github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Gemini API is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Gemini API: Vertex AI API Dev (JetBrains/skills, 363 stars), Gemini API Dev (Ayuilos/Miffan, 182 stars), Claude API Development (warpdotdev/warp, 65k stars) and Google Genai SDK Python (cnemri/google-genai-skills, 127 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 20,994 GitHub stars. The repository holds 145 skills in this directory. The repository was last updated on October 6, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.