Unified AI SDK for Dart enabling code generation, structured outputs, tools, flows, and agents.
Works with
Provides core APIs for generation, tool definition, flow orchestration, embeddings, and streaming with a single interface
Includes 8+ plugins for LLM providers (Google Gemini, Anthropic Claude, OpenAI GPT), Firebase AI, Model Context Protocol, Chrome browser integration, and HTTP server hosting via Shelf
Built-in CLI with local development UI for flow execution, tracing, model experimentat
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondeveloping-genkit-dartExecute the skills CLI command in your project's root directory to begin installation:
Fetches developing-genkit-dart from firebase/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate developing-genkit-dart. Access via /developing-genkit-dart in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Genkit Dart is an AI SDK for Dart that provides a unified interface for code generation, structured outputs, tools, flows, and AI agents.
If you need help with initializing Genkit (Genkit()), Generation (ai.generate), Tooling (ai.defineTool), Flows (ai.defineFlow), Embeddings (ai.embedMany), streaming, or calling remote flow endpoints, please load the core framework reference:
references/genkit.md
The Genkit CLI provides a local development UI for running Flow, tracing executions, playing with models, and evaluating outputs.
check if the user has it installed: genkit --version
Installation:
curl -sL cli.genkit.dev | bash # Native CLI
# OR
npm install -g genkit-cli # Via npm
Usage:
Wrap your run command with genkit start to attach the Genkit developer UI and tracing:
genkit start -- dart run main.dart
Genkit relies on a large suite of plugins to perform generative AI actions, interface with external LLMs, or host web servers.
When asked to use any given plugin, always verify usage by referring to its corresponding reference below. You should load the reference when you need to know the specific initialization arguments, tools, models, and usage patterns for the plugin:
| Plugin Name | Reference Link | Description |
|---|---|---|
genkit_google_genai |
references/genkit_google_genai.md | Load for Google Gemini plugin interface usage. |
genkit_anthropic |
references/genkit_anthropic.md | Load for Anthropic plugin interface for Claude models. |
genkit_openai |
references/genkit_openai.md | Load for OpenAI plugin interface for GPT models, Groq, and custom compatible endpoints. |
genkit_middleware |
references/genkit_middleware.md | Load for Tooling for specific agentic behavior: filesystem, skills, and toolApproval interrupts. |
genkit_mcp |
references/genkit_mcp.md | Load for Model Context Protocol integration (Server, Host, and Client capabilities). |
genkit_chrome |
references/genkit_chrome.md | Load for Running Gemini Nano locally inside the Chrome browser using the Prompt API. |
genkit_shelf |
references/genkit_shelf.md | Load for Integrating Genkit Flow actions over HTTP using Dart Shelf. |
genkit_firebase_ai |
references/genkit_firebase_ai.md | Load for Firebase AI plugin interface (Gemini API via Vertex AI). |
Whenever you define schemas mapping inside of Tools, Flows, and Prompts, you must use the schemantic library.
To learn how to use schemantic, ensure you read references/schemantic.md for how to implement type safe generated Dart code. This is particularly relevant when you encounter symbols like @Schema(), SchemanticType, or classes with the $ prefix. Genkit Dart uses schemantic for all of its data models so it's a CRITICAL skill to understand for using Genkit Dart.
dart analyze before generating the final response.Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
developing-genkit-dart fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: developing-genkit-dart is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in developing-genkit-dart — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for developing-genkit-dart matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: developing-genkit-dart is focused, and the summary matches what you get after install.
We added developing-genkit-dart from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for developing-genkit-dart matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in developing-genkit-dart — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added developing-genkit-dart from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
developing-genkit-dart is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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