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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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价格未确认★ 29 GitHub Stars目录更新于 · 2026年9月11日agent-skill

概览

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.

查看来源 ↗

文件元数据

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"

查看原始文本

---
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.

查看并核实来源

获取价格与运行成本

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运行 Skill: 尚未确认运行要求，请查看来源中的 Agent、API 和服务费用。
许可证: MIT
价格未确认: 我们尚未确认此 Skill 的价格，现有来源与安装入口仍可使用。

免费获取不代表免费运行，价格标签不代表安全评级。提交价格信息 →

已记录技能来源

已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
缺少 AI 审查批准
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

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录静态检查通过

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: dhruvanbhalara/skills
许可证: MIT
版本: Unknown
最近 GitHub 推送: 2026年8月30日
目录更新于: 2026年9月11日
技能指令路径: skills/dart/dart-genkit/SKILL.md @ a74a6fbe04a0

版本来自目录元数据，使用前请核实来源发布记录。

质量

53/100

需审查

信任

57/100

Do not auto-install

审计

68/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
缺少 AI 审查批准
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: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

Agent 接入

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

打开安装 API LLM 文本格式打开 Manifest 自动解析计划

更多详情

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  "trust": {
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      "installSafety": "standard package or runtime install path",
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      "last_outcome_at": null,
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    "AI review approval is missing"
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      "Audit: 68/100 Needs review",
      "Safety: 28/100 Avoid automatic install",
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    "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"
  }
}

创作者工具

收录来源

Registry 收录

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此列表来自公开来源，维护者认领获批前不会标记为官方。

创作者: dhruvanbhalara
来源: dhruvanbhalara/skills
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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所有者认领

认领此 Skill 页面

这条 Registry 收录列表归属于 dhruvanbhalara，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

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创作者外链工具包

将证据徽章加入你的 README

在开发者评估仓库的位置展示规范页面、当前信任与审计信号，以及真实的 Agent 验证证据。

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/dhruvanbhalara-dart-genkit?metric=listed&label=Listed)](https://www.openagentskill.com/skills/dhruvanbhalara-dart-genkit?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/dhruvanbhalara-dart-genkit?metric=trust&label=Trust)](https://www.openagentskill.com/skills/dhruvanbhalara-dart-genkit?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/dhruvanbhalara-dart-genkit?metric=audit&label=Audit)](https://www.openagentskill.com/skills/dhruvanbhalara-dart-genkit/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/dhruvanbhalara-dart-genkit?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/dhruvanbhalara-dart-genkit?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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