gemini-batch
This skill should be used when the user asks to "use Gemini Batch API", "process documents at scale", "submit a batch job", "upload files to Gemini", or needs large-scale LLM processing. Includes production gotchas and best practices.
npx skills add majiayu000/claude-skill-registry --skill gemini-batch --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Gemini Batch API Skill Large-scale asynchronous document processing using Google's Gemini models. ## When to Use - Process thousands of documents with the same prompt - Cost-effective bulk extraction (50% cheaper than synchronous API) - Jobs that can tolerate 24-hour completion windows ## IRON LAW: Use Examples First, Never Guess API **READ EXAMPLES BEFORE WRITING ANY CODE. NO EXCEPTIONS.** ### The Rule ``` User asks for batch API work ↓ MANDATORY: Read examples/batch_processor.py or examples/icon_batch_vision.py ↓ Copy the pattern exactly ↓ DO NOT guess parameter names DO NOT try wrapper types DO NOT improvise API calls ``` ### Why This Matters The Batch API has non-obvious requirements that will fail silently: 1. **Metadata must be flat primitives** - Nested objects cause cryptic errors 2. **Parameter is `dest=` not `destination=`** - Wrong name → TypeError 3. **Config is plain dict** - Not a wrapper type 4. **Examples are authoritative** - Working code beats assumptions **Rationale:** Previous agents wasted hours debugging API errors that the examples would have prevented. The patterns in `examples/` are battle-tested production code. ### Rationalization Table - STOP If You Ca
- When to Use
- IRON LAW: Use Examples First, Never Guess API
- The Rule
- Why This Matters
- Rationalization Table - STOP If You Catch Yourself Thinking:
- Red Flags - STOP If You Catch Yourself Thinking:
- MANDATORY Checklist Before ANY Batch API Code
- Prerequisites
- Install gcloud SDK
- Authentication Setup
- Create GCS Bucket
- Quick Start
- Standard Gemini API (API Key)
- Vertex AI (Recommended)
brew install google-cloud-sdk curl https://sdk.cloud.google.com | bash Authenticate with Google Cloud Platform gcloud auth login Set up Application Default Credentials for Python libraries gcloud auth application-default login Enable Vertex AI API in your project gcloud services enable aiplatform.googleapis.com Create bucket in us-central1 (required region) gsutil mb -l us-central1 gs://your-batch-bucket
What does the gemini-batch skill do?
This skill should be used when the user asks to "use Gemini Batch API", "process documents at scale", "submit a batch job", "upload files to Gemini", or needs large-scale LLM processing. Includes production gotchas and best practices.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill gemini-batch --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.