Ai Chat Studio

# AI Chat Studio

Part of [Agent Skills™](https://github.com/itallstartedwithaidea/agent-skills) by [googleadsagent.ai™](https://googleadsagent.ai)

## Description

AI Chat Studio provides a multi-LLM chat orchestration framework with 300+ assistant presets, intelligent model routing, and conversation management. The agent configures and manages interactions across multiple language model providers—OpenAI, Anthropic, Google, open-source models—selecting the optimal model for each task based on capability, cost, and latency requirements.

Not every task needs the most powerful model. A code review benefits from a reasoning-heavy model; a translation task runs well on a mid-tier model; a simple reformatting task wastes money on anything beyond a fast, cheap model. This skill implements intelligent routing that matches task characteristics to model capabilities, reducing cost by 40-60% while maintaining quality where it matters.

The 300+ assistant presets encode domain-specific system prompts, temperature settings, and output format constraints for common tasks: code generation, technical writing, data analysis, creative ideation, customer support, legal review, and more. Each preset is tested against a quality benchmark and tagged with the models it performs best on.

## Use When

- Configuring multi-provider LLM access in an application
- Routing tasks to the optimal model by cost-quality trade-off
- Managing conversation history and context windows
- Deploying domain-specific AI assistants with curated presets
- Building chat interfaces with streaming responses
- Comparing model outputs for the same prompt across providers

## How It Works

```mermaid
graph TD
    A[User Message] --> B[Task Classifier]
    B --> C{Task Type}
    C -->|Complex Reasoning| D[Claude 4 / GPT-4o]
    C -->|Code Generation| E[Claude 4 / Codestral]
    C -->|Translation| F[GPT-4o-mini / Gemini Flash]
    C -->|Simple Format| G[Haiku / Flash]
    D --> H[Apply Preset: System Prompt + Params]
    E --> H
    F --> H
    G --> H
    H --> I[Manage Context Window]
    I --> J[Stream Response]
    J --> K[Log Usage + Cost]
```

The task classifier analyzes the incoming message to determine complexity and domain, then routes to the most cost-effective model capable of handling it. Presets provide domain-specific system prompts and parameter tuning.

## Implementation

```typescript
interface ModelConfig {
  provider: "openai" | "anthropic" | "google" | "ollama";
  model: string;
  maxTokens: number;
  costPer1kInput: number;
  costPer1kOutput: number;
  capabilities: string[];
}

const MODEL_REGISTRY: ModelConfig[] = [
  { provider: "anthropic", model: "claude-sonnet-4-20250514", maxTokens: 8192,
    costPer1kInput: 0.003, costPer1kOutput: 0.015, capabilities: ["reasoning", "code", "analysis"] },
  { provider: "openai", model: "gpt-4o-mini", maxTokens: 4096,
    costPer1kInput: 0.00015, costPer1kOutput: 0.0006, capabilities: ["general", "translation", "format"] },
  { provider: "google", model: "gemini-2.0-flash", maxTokens: 8192,
    costPer1kInput: 0.0001, costPer1kOutput: 0.0004, capabilities: ["general", "fast", "multimodal"] },
];

interface AssistantPreset {
  id: string;
  name: string;
  systemPrompt: string;
  temperature: number;
  preferredModels: string[];
  tags: string[];
}

class ChatRouter {
  constructor(private models: ModelConfig[], private presets: Map<string, AssistantPreset>) {}

  route(message: string, presetId?: string): { model: ModelConfig; preset?: AssistantPreset } {
    const preset = presetId ? this.presets.get(presetId) : undefined;
    const taskType = this.classifyTask(message);

    const candidates = this.models.filter(m =>
      m.capabilities.some(c => taskType.requiredCapabilities.includes(c))
    );

    const selected = c