🤖 Skill: gemini-sdk-expert (v1.3.0)

Executive Summary

gemini-sdk-expert is a high-tier skill focused on mastering the Google Gemini ecosystem. In 2026, building with AI isn't just about prompts; it's about Structural Integrity, Context Optimization, and Multimodal Orchestration. This skill provides the blueprint for building ultra-reliable, cost-effective, and powerful AI applications using the latest @google/genai standards.

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📋 Table of Contents

1. Core Capabilities
2. The "Do Not" List (Anti-Patterns)
3. Quick Start: JSON Enforcement
4. Standard Production Patterns
5. Advanced Agentic Patterns
6. Context Caching Strategy
7. Multimodal Integration
8. Safety & Responsible AI
9. Reference Library

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🚀 Core Capabilities

• Strict Structured Output: Leveraging responseSchema for 100% reliable JSON generation.
• Agentic Function Calling: enabling models to interact with private APIs and tools.
• Long-Form Context Management: Using Context Caching for massive datasets (2M+ tokens).
• Native Multimodal Reasoning: Processing video, audio, and documents as first-class inputs.
• Latency Optimization: Strategic model selection (Flash vs. Pro) and streaming responses.

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🚫 The "Do Not" List (Anti-Patterns)

Anti-Pattern	Why it fails in 2026	Modern Alternative
Regex Parsing	Fragile and prone to hallucination.	Use responseSchema (Controlled Output).
Old SDK (@google/generative-ai)	Outdated, lacks 2026 features.	Use @google/genai exclusively.
Uncached Large Contexts	Extremely expensive and slow.	Use Context Caching for repetitive queries.
Hardcoded API Keys	Security risk.	Use Secure Environment Variables and GOOGLE_GENAI_API_VERSION.
Single-Model Bias	Pro is overkill for simple extraction.	Use Gemini 3 Flash for speed/cost tasks.

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⚡ Quick Start: JSON Enforcement

The #1 rule in 2026: Structure at the Source.

import { GoogleGenerativeAI, Type } from "@google/genai";

// Optional: Set API Version via env
// process.env.GOOGLE_GENAI_API_VERSION = "v1beta1";

const schema = {
  type: Type.OBJECT,
  properties: {
    status: { type: Type.STRING, enum: ["COMPLETE", "PENDING", "ERROR"] },
    summary: { type: Type.STRING },
    priority: { type: Type.NUMBER }
  },
  required: ["status", "summary"]
};

// Always set MIME type to application/json
const result = await model.generateContent({
  contents: [{ role: 'user', parts: [{ text: "Evaluate task X..." }] }],
  generationConfig: {
    responseMimeType: "application/json",
    responseSchema: schema
  }
});

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🛠 Standard Production Patterns

Pattern A: The Data Extractor (Flash)

Best for processing thousands of documents quickly and cheaply.

• Model: gemini-3-flash
• Config: High topP, low temperature for deterministic extraction.

Pattern B: The Complex Reasoner (Pro)

Best for architectural decisions, coding assistance, and deep media analysis.

• Model: gemini-3-pro
• Config: Enable Strict Mode in schemas for 100% adherence.

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🧩 Advanced Agentic Patterns

Parallel Function Calling

Reduce round-trips by allowing the model to call multiple tools at once. See References: Function Calling for implementation.

Semantic Caching

Store and retrieve embeddings of common queries to bypass the LLM for identical requests.

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💾 Context Caching Strategy

In 2026, we don't re-upload. We cache.

• Warm-up Phase: Initial context upload.
• Persistence Phase: Referencing the cache via cachedContent.
• Cleanup Phase: Managing TTLs to optimize storage costs.

See References: Context Caching for more.

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📸 Multimodal Integration

Gemini 3 understands the world visually and audibly.

• Video: Scene detection and temporal reasoning.
• Audio: Sentiment, tone, and environment detection.
• Document: Visual layout and OCR.

See References: Multimodal Mastery for details.

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📖 Reference Library

Detailed deep-dives into Gemini SDK excellence:

• Structured Output: Nested schemas and validation.
• Function Calling: Tools, execution loops, and security.
• Context Caching: Reducing cost and latency.
• Multimodal 2026: Video, audio, and PDF mastery.

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Updated: January 31, 2026 - 10:45