LLM Mart Basic

Use when creating a feature, designing folder structure, adding repositories/services/view models, wiring dependency injection, or deciding which layer owns logic.

Use when creating a Cubit or Bloc, modeling state with sealed classes or status enums, wiring BlocBuilder/BlocListener/BlocProvider, writing bloc tests, or choosing between Cubit and Bloc.

Use when asked to review a PR, MR, branch, or diff, audit changed files, or check code quality.

Use when writing switch statements, refactoring if-else chains, creating data classes, choosing records vs classes, destructuring values, or modernizing pre-Dart-3 code.

Use when building AI agents in Dart, implementing Genkit flows or tools, integrating LLMs into Dart or Flutter applications, or using Genkit Dart plugins.

Use when writing Dart code, reviewing for style, refactoring naming, adding doc comments, structuring imports, or enforcing type annotations.

Use when setting up firebase_ai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI errors.

Use when logging analytics events, setting user properties, configuring default event parameters, building funnels, or adding screen-view tracking.

Use when implementing app attestation, configuring App Check providers, setting up debug tokens, enabling backend enforcement, or managing token refresh.

Use when setting up auth, managing auth state, implementing email/password or social sign-in, handling auth errors, or managing users.

Use when setting up Firestore, designing schemas, doing CRUD, creating listeners, paginating queries, configuring indexes, enabling offline persistence, or writing security rules.

Use when calling callable functions (httpsCallable), passing data to server-side logic, handling function errors/timeouts, configuring regions, or testing with the Emulator Suite.

Use when implementing crash reporting, capturing fatal/non-fatal errors, recording isolate/async exceptions, customizing reports, or uploading obfuscated symbols.

Use when setting up Data Connect, writing GraphQL queries/mutations, configuring generated SDKs, handling offline, or applying security rules.

Use when syncing real-time data, structuring JSON trees, reading/writing, creating listeners, enabling offline persistence, managing presence, sharding, or writing security rules.

Use when running in-app message campaigns, triggering/suppressing messages, configuring opt-in data collection, testing on specific devices, or handling message callbacks.

Use when setting up Firebase Cloud Messaging, managing permissions and tokens, handling background/foreground notification taps, or dispatching messages server-side (HTTP v1).

Use when implementing feature flags, running A/B tests, setting parameter defaults, fetching/activating config, or enabling real-time config updates.

Use when setting up Storage, uploading/downloading files, managing metadata, handling errors, or applying security rules.

Use when scaffolding a project, refactoring into layers, creating view models/repositories, configuring dependency injection, or implementing unidirectional data flow (MVVM).

A green PR, a controller reporting success, and not one line of the new code running

Create a multi-agent group chat with AG2 using configurable speaker selection patterns

Scaffold an A2A-compliant AG2 agent with server wiring, card settings, and skill definitions

Scaffold a new AG2 ConversableAgent with tool functions, system prompt, and LLM config

Create a tool function for an AG2 agent with type annotations, docstrings, and JSON return contracts

Create a sequential multi-agent pipeline where each agent processes and passes results to the next

Design a complete multi-agent workflow from a natural language description, selecting the right orchestration pattern

Follow RED-GREEN-REFACTOR cycle approach for test-driven development

Implement secure user authentication system

Generate and maintain project changelog

Setup mutation testing for code quality

Add and configure new project dependencies

Setup application performance monitoring

Implement property-based testing framework

Add a new entry to the project's CHANGELOG.md file following Keep a Changelog format

Preflight a repo before AI agents change files

Display all available development tools

Review and improve system architecture

Explore architectural decisions through systematic scenario analysis with trade-off evaluation and future-proofing assessment.

Enable bidirectional GitHub-Linear synchronization

Interview to flesh out a plan/spec

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.