# Build Reliable Gemini API Applications

Gemini API projects can fail when model names, SDKs, and documentation paths change. This skill provides focused guidance for SDK setup, model selection, examples, and official references.

## Install

```bash
npx skillstore add sickn33/gemini-api-dev
```

## Metadata

- Status: approved
- Slug: sickn33-gemini-api-dev
- Skillstore revision: r2
- Version status: missing
- Tree hash: 7e0b83c528cb418ad00c6d8da5af77f9c800b72c2da241e4b65db833376099cb
- Author: sickn33
- GitHub username: sickn33
- License: Apache-2.0
- Repository: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/gemini-api-dev
- Ref: 88a8e9a07f4c54ab105c1c41b6267c287146b07b
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: confirmation\_required
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: scripts, external\_commands, network
- Quality score: 38
- Quality tier: warning
- Public page: https://skillstore.pages.dev/skills/sickn33-gemini-api-dev
- Manifest: https://skillstore.pages.dev/api/skills/sickn33-gemini-api-dev/manifest

## Capabilities

- Lists Gemini and Gemma model identifiers with intended workload profiles.
- Provides basic text-generation examples for Python, TypeScript, Go, and Java.
- Identifies current SDK package names and warns against deprecated packages.
- Guides documentation lookup through Google MCP search or official text pages.
- Links official references for function calling, structured outputs, images, embeddings, and SDK migration.

## Use Cases

- Prototype a Text Feature: Create a first Gemini text-generation flow using a supported SDK and a suitable model.
- Standardize SDK Setup: Compare package names and basic text-generation patterns across Python, TypeScript, Go, and Java.
- Plan an Advanced Integration: Locate official references for function calling, structured outputs, images, embeddings, and SDK migration.

## Prompt Templates

### Create a First Request

```
Create a minimal Gemini API text-generation example in [language]. Use the current SDK named by this skill and explain the required setup.
```

### Choose a Model

```
Recommend a Gemini or Gemma model for [workload]. Consider latency, reasoning, multimodal needs, context size, cost, and required verification.
```

### Design Tool Integration

```
Design a Gemini API feature for [application] using function calling and structured outputs. Define validation, error handling, and tests without exposing credentials.
```

### Review Production Architecture

```
Review this Gemini API architecture: [architecture]. Check model availability, multimodal limits, tool safety, quotas, retries, observability, and documentation freshness.
```

## Limitations

- Does not provide complete implementations for function calling, structured outputs, multimodal processing, or embeddings.
- Does not configure API keys, cloud projects, billing, quotas, or deployment environments.
- Model availability, pricing, limits, and package versions can change after publication.
- Does not cover the Gemini Live API workflow.

## Best Practices

- Verify model identifiers, package versions, quotas, and pricing against current official documentation.
- Keep API credentials in an environment variable or secret manager, never in prompts or source files.
- Add validation, retries, observability, cost controls, and environment-specific tests before deployment.

## Anti Patterns

- Do not copy model names into production without checking current availability.
- Do not start new projects with SDK packages marked as deprecated.
- Do not treat quick-start examples as complete security, validation, retry, or deployment implementations.

## Security Audit

- Audited at: 2026-07-23T23:24:53.626\+00:00
- Summary: Static analysis produced 46 alerts, but all reflect inert Markdown, language examples, package instructions, or official documentation links rather than executable threats. The skill contains a high-confidence prompt injection phrase that claims its rules override model training data. Publication should wait until that authority-overriding language is removed.

## Stats

- Views: 100
- Downloads: 12
- Favorites: 0
- Popularity score: 0