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dart-genkit

Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.

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Price unconfirmed★ 29 GitHub starsRegistry updated · Sep 11, 2026agent-skill

Overview

Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.

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AI Engineering with Genkit Dart

Build structured, type-safe, and observable AI-powered workflows and agents using the Genkit Dart SDK.

Project Setup
Defining Prompts & Models
Creating Custom Tools
Orchestrating Flows
Best Practices

Project Setup

Add the Genkit package to your Dart project's pubspec.yaml:

dependencies:
  genkit: ^0.1.0-preview  # Replace with the latest version
  google_generative_ai: ^0.4.0

Set up your API credentials (e.g., Gemini API key) in your environment:
```
export GEMINI_API_KEY="your_api_key_here"
```

Defining Prompts & Models

Genkit separates model invocation and prompt structure from core application logic using structured prompts.

import 'package:genkit/genkit.dart';
import 'package:google_generative_ai/google_generative_ai.dart';

void main() async {
  // Initialize Genkit
  final ai = Genkit(
    model: 'gemini-1.5-flash',
    apiKey: String.fromEnvironment('GEMINI_API_KEY'),
  );

  // Invoke the model with a simple prompt
  final response = await ai.generate(
    prompt: 'Explain the concept of monads in Dart.',
  );
  
  print(response.text);
}

Creating Custom Tools

Genkit agents utilize Tools to execute tasks (e.g., database queries, web scraping, mathematical calculations).

import 'package:genkit/genkit.dart';

// 1. Define schemas for input and output
final additionInputSchema = Schema.object({
  'a': Schema.number(description: 'First number'),
  'b': Schema.number(description: 'Second number'),
});

// 2. Define the tool
final addTool = ai.defineTool(
  name: 'addNumbers',
  description: 'Adds two numbers together.',
  inputSchema: additionInputSchema,
  action: (input) async {
    final a = input['a'] as num;
    final b = input['b'] as num;
    return {'result': a + b};
  },
);

Orchestrating Flows

Flows are executable pipelines that support structured input and output schemas, built-in telemetry, and error handling.

import 'package:genkit/genkit.dart';

// Define the input and output schemas
final jokeRequestSchema = Schema.object({
  'topic': Schema.string(description: 'The topic for the joke'),
});

final jokeResponseSchema = Schema.object({
  'setup': Schema.string(),
  'punchline': Schema.string(),
});

// Define the Flow
final jokeFlow = ai.defineFlow(
  name: 'jokeFlow',
  inputSchema: jokeRequestSchema,
  outputSchema: jokeResponseSchema,
  action: (input) async {
    final topic = input['topic'] as String;
    
    final response = await ai.generate(
      prompt: 'Tell me a structured joke about $topic.',
      responseSchema: jokeResponseSchema,
    );
    
    return response.structuredOutput!;
  },
);

void main() async {
  // Run the flow
  final result = await jokeFlow.run({'topic': 'coding'});
  print('Setup: ${result['setup']}');
  print('Punchline: ${result['punchline']}');
}

Best Practices

Exhaustive Schema Definitions: Always define explicit inputSchema and outputSchema for tools and flows to ensure the LLM generates correctly structured arguments.
Environment Isolation: Do not hardcode API keys. Use environment variables or secure credential storage.
Trace Observability: Enable Genkit's trace observability in development to inspect agent decision trees and tool invocations.
Error Handling: Wrap tool execution in standard try-catch blocks and return error details gracefully so the agent can self-correct.

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File metadata

name: dart-genkit
description: Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.
metadata:
    platforms: "dart"
    languages: "dart"
    category: "ai"

View original text

---
name: dart-genkit
description: Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.
metadata:
    platforms: "dart"
    languages: "dart"
    category: "ai"
---

# AI Engineering with Genkit Dart

Build structured, type-safe, and observable AI-powered workflows and agents using the **Genkit Dart SDK**.

## Contents
- [Project Setup](#project-setup)
- [Defining Prompts & Models](#defining-prompts--models)
- [Creating Custom Tools](#creating-custom-tools)
- [Orchestrating Flows](#orchestrating-flows)
- [Best Practices](#best-practices)

---

## Project Setup

1. Add the Genkit package to your Dart project's `pubspec.yaml`:
   ```yaml
   dependencies:
     genkit: ^0.1.0-preview  # Replace with the latest version
     google_generative_ai: ^0.4.0
   ```
2. Set up your API credentials (e.g., Gemini API key) in your environment:
   ```bash
   export GEMINI_API_KEY="your_api_key_here"
   ```

---

## Defining Prompts & Models

Genkit separates model invocation and prompt structure from core application logic using structured prompts.

```dart
import 'package:genkit/genkit.dart';
import 'package:google_generative_ai/google_generative_ai.dart';

void main() async {
  // Initialize Genkit
  final ai = Genkit(
    model: 'gemini-1.5-flash',
    apiKey: String.fromEnvironment('GEMINI_API_KEY'),
  );

  // Invoke the model with a simple prompt
  final response = await ai.generate(
    prompt: 'Explain the concept of monads in Dart.',
  );
  
  print(response.text);
}
```

---

## Creating Custom Tools

Genkit agents utilize **Tools** to execute tasks (e.g., database queries, web scraping, mathematical calculations).

```dart
import 'package:genkit/genkit.dart';

// 1. Define schemas for input and output
final additionInputSchema = Schema.object({
  'a': Schema.number(description: 'First number'),
  'b': Schema.number(description: 'Second number'),
});

// 2. Define the tool
final addTool = ai.defineTool(
  name: 'addNumbers',
  description: 'Adds two numbers together.',
  inputSchema: additionInputSchema,
  action: (input) async {
    final a = input['a'] as num;
    final b = input['b'] as num;
    return {'result': a + b};
  },
);
```

---

## Orchestrating Flows

Flows are executable pipelines that support structured input and output schemas, built-in telemetry, and error handling.

```dart
import 'package:genkit/genkit.dart';

// Define the input and output schemas
final jokeRequestSchema = Schema.object({
  'topic': Schema.string(description: 'The topic for the joke'),
});

final jokeResponseSchema = Schema.object({
  'setup': Schema.string(),
  'punchline': Schema.string(),
});

// Define the Flow
final jokeFlow = ai.defineFlow(
  name: 'jokeFlow',
  inputSchema: jokeRequestSchema,
  outputSchema: jokeResponseSchema,
  action: (input) async {
    final topic = input['topic'] as String;
    
    final response = await ai.generate(
      prompt: 'Tell me a structured joke about $topic.',
      responseSchema: jokeResponseSchema,
    );
    
    return response.structuredOutput!;
  },
);

void main() async {
  // Run the flow
  final result = await jokeFlow.run({'topic': 'coding'});
  print('Setup: ${result['setup']}');
  print('Punchline: ${result['punchline']}');
}
```

---

## Best Practices

- **Exhaustive Schema Definitions**: Always define explicit `inputSchema` and `outputSchema` for tools and flows to ensure the LLM generates correctly structured arguments.
- **Environment Isolation**: Do not hardcode API keys. Use environment variables or secure credential storage.
- **Trace Observability**: Enable Genkit's trace observability in development to inspect agent decision trees and tool invocations.
- **Error Handling**: Wrap tool execution in standard `try-catch` blocks and return error details gracefully so the agent can self-correct.

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Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: dhruvanbhalara/skills
License: MIT
Version: Unknown
Last GitHub push: Aug 30, 2026
Registry updated: Sep 11, 2026
Instruction path: skills/dart/dart-genkit/SKILL.md @ a74a6fbe04a0

Version reported in registry metadata; check source releases before relying on it.

Quality

53/100

Needs review

Trust

57/100

Do not auto-install

Audit

68/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

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More details

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Creator: dhruvanbhalara
Source: dhruvanbhalara/skills
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dhruvanbhalara

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dart-genkit

Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.

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Price unconfirmed★ 29 GitHub starsRegistry updated · Sep 11, 2026agent-skill

Overview

Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.

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AI Engineering with Genkit Dart

Build structured, type-safe, and observable AI-powered workflows and agents using the Genkit Dart SDK.

Project Setup
Defining Prompts & Models
Creating Custom Tools
Orchestrating Flows
Best Practices

Project Setup

Add the Genkit package to your Dart project's pubspec.yaml:

dependencies:
  genkit: ^0.1.0-preview  # Replace with the latest version
  google_generative_ai: ^0.4.0

Set up your API credentials (e.g., Gemini API key) in your environment:
```
export GEMINI_API_KEY="your_api_key_here"
```

Defining Prompts & Models

Genkit separates model invocation and prompt structure from core application logic using structured prompts.

import 'package:genkit/genkit.dart';
import 'package:google_generative_ai/google_generative_ai.dart';

void main() async {
  // Initialize Genkit
  final ai = Genkit(
    model: 'gemini-1.5-flash',
    apiKey: String.fromEnvironment('GEMINI_API_KEY'),
  );

  // Invoke the model with a simple prompt
  final response = await ai.generate(
    prompt: 'Explain the concept of monads in Dart.',
  );
  
  print(response.text);
}

Creating Custom Tools

Genkit agents utilize Tools to execute tasks (e.g., database queries, web scraping, mathematical calculations).

import 'package:genkit/genkit.dart';

// 1. Define schemas for input and output
final additionInputSchema = Schema.object({
  'a': Schema.number(description: 'First number'),
  'b': Schema.number(description: 'Second number'),
});

// 2. Define the tool
final addTool = ai.defineTool(
  name: 'addNumbers',
  description: 'Adds two numbers together.',
  inputSchema: additionInputSchema,
  action: (input) async {
    final a = input['a'] as num;
    final b = input['b'] as num;
    return {'result': a + b};
  },
);

Orchestrating Flows

Flows are executable pipelines that support structured input and output schemas, built-in telemetry, and error handling.

import 'package:genkit/genkit.dart';

// Define the input and output schemas
final jokeRequestSchema = Schema.object({
  'topic': Schema.string(description: 'The topic for the joke'),
});

final jokeResponseSchema = Schema.object({
  'setup': Schema.string(),
  'punchline': Schema.string(),
});

// Define the Flow
final jokeFlow = ai.defineFlow(
  name: 'jokeFlow',
  inputSchema: jokeRequestSchema,
  outputSchema: jokeResponseSchema,
  action: (input) async {
    final topic = input['topic'] as String;
    
    final response = await ai.generate(
      prompt: 'Tell me a structured joke about $topic.',
      responseSchema: jokeResponseSchema,
    );
    
    return response.structuredOutput!;
  },
);

void main() async {
  // Run the flow
  final result = await jokeFlow.run({'topic': 'coding'});
  print('Setup: ${result['setup']}');
  print('Punchline: ${result['punchline']}');
}

Best Practices

Exhaustive Schema Definitions: Always define explicit inputSchema and outputSchema for tools and flows to ensure the LLM generates correctly structured arguments.
Environment Isolation: Do not hardcode API keys. Use environment variables or secure credential storage.
Trace Observability: Enable Genkit's trace observability in development to inspect agent decision trees and tool invocations.
Error Handling: Wrap tool execution in standard try-catch blocks and return error details gracefully so the agent can self-correct.

View source ↗

File metadata

name: dart-genkit
description: Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.
metadata:
    platforms: "dart"
    languages: "dart"
    category: "ai"

View original text

---
name: dart-genkit
description: Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.
metadata:
    platforms: "dart"
    languages: "dart"
    category: "ai"
---

# AI Engineering with Genkit Dart

Build structured, type-safe, and observable AI-powered workflows and agents using the **Genkit Dart SDK**.

## Contents
- [Project Setup](#project-setup)
- [Defining Prompts & Models](#defining-prompts--models)
- [Creating Custom Tools](#creating-custom-tools)
- [Orchestrating Flows](#orchestrating-flows)
- [Best Practices](#best-practices)

---

## Project Setup

1. Add the Genkit package to your Dart project's `pubspec.yaml`:
   ```yaml
   dependencies:
     genkit: ^0.1.0-preview  # Replace with the latest version
     google_generative_ai: ^0.4.0
   ```
2. Set up your API credentials (e.g., Gemini API key) in your environment:
   ```bash
   export GEMINI_API_KEY="your_api_key_here"
   ```

---

## Defining Prompts & Models

Genkit separates model invocation and prompt structure from core application logic using structured prompts.

```dart
import 'package:genkit/genkit.dart';
import 'package:google_generative_ai/google_generative_ai.dart';

void main() async {
  // Initialize Genkit
  final ai = Genkit(
    model: 'gemini-1.5-flash',
    apiKey: String.fromEnvironment('GEMINI_API_KEY'),
  );

  // Invoke the model with a simple prompt
  final response = await ai.generate(
    prompt: 'Explain the concept of monads in Dart.',
  );
  
  print(response.text);
}
```

---

## Creating Custom Tools

Genkit agents utilize **Tools** to execute tasks (e.g., database queries, web scraping, mathematical calculations).

```dart
import 'package:genkit/genkit.dart';

// 1. Define schemas for input and output
final additionInputSchema = Schema.object({
  'a': Schema.number(description: 'First number'),
  'b': Schema.number(description: 'Second number'),
});

// 2. Define the tool
final addTool = ai.defineTool(
  name: 'addNumbers',
  description: 'Adds two numbers together.',
  inputSchema: additionInputSchema,
  action: (input) async {
    final a = input['a'] as num;
    final b = input['b'] as num;
    return {'result': a + b};
  },
);
```

---

## Orchestrating Flows

Flows are executable pipelines that support structured input and output schemas, built-in telemetry, and error handling.

```dart
import 'package:genkit/genkit.dart';

// Define the input and output schemas
final jokeRequestSchema = Schema.object({
  'topic': Schema.string(description: 'The topic for the joke'),
});

final jokeResponseSchema = Schema.object({
  'setup': Schema.string(),
  'punchline': Schema.string(),
});

// Define the Flow
final jokeFlow = ai.defineFlow(
  name: 'jokeFlow',
  inputSchema: jokeRequestSchema,
  outputSchema: jokeResponseSchema,
  action: (input) async {
    final topic = input['topic'] as String;
    
    final response = await ai.generate(
      prompt: 'Tell me a structured joke about $topic.',
      responseSchema: jokeResponseSchema,
    );
    
    return response.structuredOutput!;
  },
);

void main() async {
  // Run the flow
  final result = await jokeFlow.run({'topic': 'coding'});
  print('Setup: ${result['setup']}');
  print('Punchline: ${result['punchline']}');
}
```

---

## Best Practices

- **Exhaustive Schema Definitions**: Always define explicit `inputSchema` and `outputSchema` for tools and flows to ensure the LLM generates correctly structured arguments.
- **Environment Isolation**: Do not hardcode API keys. Use environment variables or secure credential storage.
- **Trace Observability**: Enable Genkit's trace observability in development to inspect agent decision trees and tool invocations.
- **Error Handling**: Wrap tool execution in standard `try-catch` blocks and return error details gracefully so the agent can self-correct.

Review the source

Price & running costs

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Run it: Requirements have not been confirmed. Check the source for agent, API and service charges.
License: MIT
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

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Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

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Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: dhruvanbhalara/skills
License: MIT
Version: Unknown
Last GitHub push: Aug 30, 2026
Registry updated: Sep 11, 2026
Instruction path: skills/dart/dart-genkit/SKILL.md @ a74a6fbe04a0

Version reported in registry metadata; check source releases before relying on it.

Quality

53/100

Needs review

Trust

57/100

Do not auto-install

Audit

68/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

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More details

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      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/dhruvanbhalara-dart-genkit",
    "api": "https://www.openagentskill.com/api/agent/skills/dhruvanbhalara-dart-genkit",
    "audit": "https://www.openagentskill.com/skills/dhruvanbhalara-dart-genkit/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=dhruvanbhalara-dart-genkit&task=Use%20dart-genkit%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dart-genkit%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dart-genkit%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/dhruvanbhalara-dart-genkit/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/dhruvanbhalara-dart-genkit"
  }
}

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Creator: dhruvanbhalara
Source: dhruvanbhalara/skills
Indexed by: OpenAgentSkill community index

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Overview

AI Engineering with Genkit Dart

Contents

Project Setup

Defining Prompts & Models

Creating Custom Tools

Orchestrating Flows

Best Practices

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Start with one small task

Source & usage notes

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Overview

AI Engineering with Genkit Dart

Contents

Project Setup

Defining Prompts & Models

Creating Custom Tools

Orchestrating Flows

Best Practices

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

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