Use when building AI agents in Dart, implementing Genkit flows or tools, integrating LLMs into Dart or Flutter applications, or using Genkit Dart plugins.
npx skills add https://github.com/evanca/flutter-ai-rules --skill developing-genkit-dart
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.Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
Take evanca/developing-genkit-dart from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npm.
Without those the skill loads but fails at the first command.