PaperclipOrg
Claude Agent Skill · by Firebase

Developing Genkit Go

If you're building AI apps in Go, this skill knows Genkit's patterns inside and out. It handles the core workflow of wrapping AI logic in flows, passing the Gen

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Terminal · npx
$npx skills add https://github.com/obra/superpowers --skill brainstorming
Works with Paperclip

How Developing Genkit Go fits into a Paperclip company.

Developing Genkit Go drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

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SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
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SKILL.md97 lines
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---name: developing-genkit-godescription: 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.metadata:  genkit-managed: true--- # 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 ```gopackage main import (	"context"	"fmt"	"log"	"net/http" 	"github.com/genkit-ai/genkit/go/ai"	"github.com/genkit-ai/genkit/go/genkit"	"github.com/genkit-ai/genkit/go/plugins/googlegenai"	"github.com/genkit-ai/genkit/go/plugins/server") func main() {	ctx := context.Background()	g := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.GoogleAI{})) 	genkit.DefineFlow(g, "jokeFlow", func(ctx context.Context, topic string) (string, error) {		return genkit.GenerateText(ctx, g,			ai.WithModelName("googleai/gemini-flash-latest"),			ai.WithPrompt("Tell me a joke about %s", topic),		)	}) 	mux := http.NewServeMux()	for _, f := range genkit.ListFlows(g) {		mux.HandleFunc("POST /"+f.Name(), genkit.Handler(f))	}	log.Fatal(server.Start(ctx, "127.0.0.1:8080", mux))}``` ## Core Features Load the appropriate reference based on what you need: | Feature | Reference | When to load || --- | --- | --- || Initialization | [references/getting-started.md](references/getting-started.md) | Setting up `genkit.Init`, plugins, the `*Genkit` instance pattern || Generation | [references/generation.md](references/generation.md) | `Generate`, `GenerateText`, `GenerateData`, streaming, output formats || Prompts | [references/prompts.md](references/prompts.md) | `DefinePrompt`, `DefineDataPrompt`, `.prompt` files, schemas || Tools | [references/tools.md](references/tools.md) | `DefineTool`, tool interrupts, `RestartWith`/`RespondWith` || Flows & HTTP | [references/flows-and-http.md](references/flows-and-http.md) | `DefineFlow`, `DefineStreamingFlow`, `genkit.Handler`, HTTP serving || Model Providers | [references/providers.md](references/providers.md) | Google AI, Vertex AI, Anthropic, OpenAI-compatible, Ollama setup | ## Genkit CLI Check if installed: `genkit --version` **Installation:**```bashcurl -sL cli.genkit.dev | bash``` **Key commands:** ```bash# Start app with Developer UI (tracing, flow testing) at http://localhost:4000genkit start -- go run .genkit start -o -- go run .   # also opens browser # Run a flow directly from the CLIgenkit flow:run myFlow '{"data": "input"}'genkit flow:run myFlow '{"data": "input"}' --stream   # with streaminggenkit flow:run myFlow '{"data": "input"}' --wait      # wait for completion # Look up Genkit documentationgenkit docs:search "streaming" gogenkit docs:list gogenkit docs:read go/flows.md``` See [references/getting-started.md](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. Code-defined prompts are better for simple, single-line cases.- **Look up the latest model IDs.** Model names change frequently. Check provider documentation for current model IDs rather than relying on hardcoded names. See [references/providers.md](references/providers.md).