developing-genkit-go
Build AI Apps with Genkit Go SDK
Building AI applications in Go requires understanding Genkit SDK patterns for generation, prompts, flows, and model providers. This skill provides comprehensive reference documentation for creating AI-powered Go applications with structured output, streaming, and tool calling.
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Test it
Using "developing-genkit-go". Create a flow that generates a joke about the topic 'programming bugs'
Expected outcome:
A working Go file with genkit.Init, a DefineFlow for joke generation, HTTP handler setup with genkit.Handler, and instructions to test it with genkit start.
Using "developing-genkit-go". Show how to generate structured JSON with a Recipe type
Expected outcome:
Go code defining Recipe and Ingredient structs with jsonschema tags, then genkit.GenerateData call returning a *Recipe with title, ingredients, and steps fields.
Security Audit
SafeAll 283 static findings are false positives. This is a legitimate Firebase community skill providing Genkit Go SDK documentation. The skill contains Go code examples with printf-style string formatting (e.g., 'Tell me a joke about %s'), bash CLI installation commands, and references to standard AI API environment variable names. No malicious code patterns exist.
Medium Risk Issues (2)
Low Risk Issues (2)
Quality Score
What You Can Build
Build AI-powered APIs
Create HTTP APIs that use AI models for content generation, classification, or data extraction with built-in tracing.
Implement multi-step AI workflows
Build complex AI flows with tool calling, where the model can invoke custom Go functions to gather information or perform actions.
Generate structured data with AI
Use Genkit's schema support to generate typed Go structs directly from AI model outputs, eliminating manual JSON parsing.
Try These Prompts
Show me how to use genkit.GenerateText to generate a joke about a topic the user provides.
Create a flow that uses genkit.GenerateData with a Go struct schema to return structured JSON data.
Define a Genkit tool that allows the AI to search a database, then show how to use it in a flow.
Set up Genkit with both Google AI and Anthropic providers, showing how to switch between models.
Best Practices
- Pass the *Genkit instance explicitly through your call chain instead of storing it as a global variable
- Use jsonschema struct tags on output types so models understand what each field should contain
- Write clear tool descriptions - the model decides which tools to call based on these descriptions
Avoid
- Avoid hardcoding model names - check provider documentation for current model IDs as they change frequently
- Do not skip flows for production code - flows provide tracing and observability that are essential for debugging
- Avoid vague tool descriptions - they lead to missed or incorrect tool calls from the model