Agent skill · software engineering · aiskillstore
developing-genkit-go
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.
Why this skill is useful
Provides specific Genkit Go SDK patterns and commands that the AI wouldn't reliably generate on its own.
What it needs
Requires genkit installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Genkit Go Genkit Go is an AI SDK for Go that provides generation, structured output, streaming, tool calling, prompts, and flows with a unified interface across model providers. Hello World Core Features Load the appropriate reference based on what you need: Feature Reference When to load --- --- --- Initialization references/getting-started.md Setting up genkit.Init, plugins, the Genkit instance pattern Generation references/generation.md Generate, GenerateText, GenerateData, streaming, output formats Prompts references/prompts.md DefinePrompt, DefineDataPrompt, .prompt files, schemas Tools references/tools.md DefineTool, tool interrupts, RestartWith/RespondWith Flows & HTTP references/flows-and-http.md DefineFlow, DefineStreamingFlow, genkit.Handler, HTTP serving Model Providers references/providers.md Google AI, Vertex AI, Anthropic, OpenAI-compatible, Ollama setup Genkit CLI Check if installed: genkit --version Installation: Key commands: See references/getting-started.md for full CLI and Developer UI details. Key Guidance Pass g explicitly. The Genkit instance returned by genkit.Init is the central registry. Pass it to all Genkit functions rather than storing it as a global. This is a core pattern throughout the SDK. Wrap AI logic in flows. Flows give you tracing, observability, HTTP deployment via genkit.Handler, and the ability to test from the Developer UI and CLI. Any generation call worth keeping should live in a flow. Use jsonschema:"description=..." struct tags on output types. The model uses these descriptions to understand what each field should contain. Without them, structured output quality drops significantly. Write good tool descriptions. The model decides which tools to call based on their description string. Vague descriptions lead to missed or incorrect tool calls. Use .prompt files for complex prompts. They separate prompt content from Go code, support Handlebars templating, and can be iterated on without recompilation. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/developing-genkit-go