mistral-reference-architecture
Best-practice project layout and architecture patterns for Mistral AI apps.
Install
mkdir -p .claude/skills/mistral-reference-architecture && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4785" && unzip -o skill.zip -d .claude/skills/mistral-reference-architecture && rm skill.zipInstalls to .claude/skills/mistral-reference-architecture
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Implement Mistral AI reference architecture with best-practice projectKey capabilities
- →Implement layered project architecture
- →Configure Zod-validated environment variables
- →Manage singleton Mistral client instances
- →Define custom error classes with retry logic
- →Implement service-layer caching for deterministic requests
- →Create reusable prompt templates
How it works
It organizes code into distinct layers including API, Service, Mistral, and Infrastructure, using a singleton client and Zod for configuration validation.
Inputs & outputs
When to use mistral-reference-architecture
- →Structuring new AI projects
- →Standardizing error handling
- →Configuring service layers
- →Designing production-grade integrations
About this skill
Mistral AI Reference Architecture
Overview
Production-ready architecture patterns for Mistral AI integrations: layered project structure, singleton client, Zod-validated config, custom error classes, service layer with caching, health checks, prompt templates, and model routing.
Prerequisites
- TypeScript/Node.js project (ESM)
@mistralai/mistralaiSDKzodfor config validation- Testing framework (Vitest)
Layer Architecture
API Layer (Routes, Controllers, Middleware)
|
Service Layer (Business Logic, Orchestration)
|
Mistral Layer (Client, Config, Errors, Prompts)
|
Infrastructure Layer (Cache, Queue, Monitoring)
Instructions
Step 1: Directory Structure
src/
├── mistral/
│ ├── client.ts # Singleton client factory
│ ├── config.ts # Zod-validated config
│ ├── errors.ts # Custom error classes
│ ├── types.ts # Shared types
│ └── prompts.ts # Prompt templates
├── services/
│ ├── chat.service.ts # Chat with caching + retry
│ ├── embed.service.ts # Embeddings + search
│ └── rag.service.ts # RAG pipeline
├── api/
│ ├── chat.route.ts # HTTP endpoints
│ └── health.route.ts # Health check
└── config/
├── base.ts # Shared config
├── development.ts # Dev overrides
└── production.ts # Prod overrides
Step 2: Config with Zod Validation
// src/mistral/config.ts
import { z } from 'zod';
const MistralConfigSchema = z.object({
apiKey: z.string().min(10, 'MISTRAL_API_KEY required'),
defaultModel: z.string().default('mistral-small-latest'),
timeoutMs: z.number().default(30_000),
maxRetries: z.number().default(3),
cache: z.object({
enabled: z.boolean().default(true),
ttlMs: z.number().default(3_600_000),
maxSize: z.number().default(5000),
}).default({}),
});
export type MistralConfig = z.infer<typeof MistralConfigSchema>;
export function loadConfig(): MistralConfig {
return MistralConfigSchema.parse({
apiKey: process.env.MISTRAL_API_KEY,
defaultModel: process.env.MISTRAL_MODEL,
timeoutMs: process.env.MISTRAL_TIMEOUT ? Number(process.env.MISTRAL_TIMEOUT) : undefined,
});
}
Step 3: Singleton Client
// src/mistral/client.ts
import { Mistral } from '@mistralai/mistralai';
import { loadConfig, type MistralConfig } from './config.js';
let _client: Mistral | null = null;
let _config: MistralConfig | null = null;
export function getMistralClient(): Mistral {
if (!_client) {
_config = loadConfig();
_client = new Mistral({
apiKey: _config.apiKey,
timeoutMs: _config.timeoutMs,
maxRetries: _config.maxRetries,
});
}
return _client;
}
export function getConfig(): MistralConfig {
if (!_config) loadConfig();
return _config!;
}
export function resetClient(): void {
_client = null;
_config = null;
}
Step 4: Custom Error Classes
// src/mistral/errors.ts
export type MistralErrorCode =
| 'AUTH_ERROR'
| 'RATE_LIMIT'
| 'BAD_REQUEST'
| 'SERVICE_ERROR'
| 'TIMEOUT'
| 'CONTEXT_OVERFLOW';
export class MistralServiceError extends Error {
constructor(
message: string,
public readonly code: MistralErrorCode,
public readonly status: number,
public readonly retryable: boolean,
) {
super(message);
this.name = 'MistralServiceError';
}
static fromApiError(error: any): MistralServiceError {
const status = error.status ?? error.statusCode ?? 500;
if (status === 401) return new MistralServiceError('Authentication failed', 'AUTH_ERROR', 401, false);
if (status === 429) return new MistralServiceError('Rate limit exceeded', 'RATE_LIMIT', 429, true);
if (status === 400) return new MistralServiceError(error.message, 'BAD_REQUEST', 400, false);
if (status >= 500) return new MistralServiceError('Service error', 'SERVICE_ERROR', status, true);
return new MistralServiceError(error.message, 'SERVICE_ERROR', status, false);
}
}
Step 5: Service Layer with Caching
// src/services/chat.service.ts
import { createHash } from 'crypto';
import { LRUCache } from 'lru-cache';
import { getMistralClient, getConfig } from '../mistral/client.js';
import { MistralServiceError } from '../mistral/errors.js';
const cache = new LRUCache<string, any>({ max: 5000, ttl: 3_600_000 });
export class ChatService {
async complete(messages: any[], options?: {
model?: string;
temperature?: number;
maxTokens?: number;
}) {
const config = getConfig();
const model = options?.model ?? config.defaultModel;
const temperature = options?.temperature ?? 0.7;
// Cache deterministic requests
if (temperature === 0 && config.cache.enabled) {
const key = createHash('sha256').update(JSON.stringify({ model, messages })).digest('hex');
const cached = cache.get(key);
if (cached) return cached;
const result = await this.executeChat(model, messages, { ...options, temperature: 0 });
cache.set(key, result);
return result;
}
return this.executeChat(model, messages, options);
}
async *stream(messages: any[], model?: string) {
const client = getMistralClient();
try {
const stream = await client.chat.stream({
model: model ?? getConfig().defaultModel,
messages,
});
for await (const event of stream) {
const text = event.data?.choices?.[0]?.delta?.content;
if (text) yield text;
}
} catch (error: any) {
throw MistralServiceError.fromApiError(error);
}
}
private async executeChat(model: string, messages: any[], options: any = {}) {
const client = getMistralClient();
try {
return await client.chat.complete({ model, messages, ...options });
} catch (error: any) {
throw MistralServiceError.fromApiError(error);
}
}
}
Step 6: Health Check
// src/api/health.route.ts
import { getMistralClient } from '../mistral/client.js';
export async function checkMistralHealth() {
const start = performance.now();
try {
const client = getMistralClient();
const models = await client.models.list();
const latencyMs = Math.round(performance.now() - start);
return {
status: latencyMs > 5000 ? 'degraded' : 'healthy',
latencyMs,
modelCount: models.data?.length ?? 0,
};
} catch (error: any) {
return {
status: 'unhealthy',
latencyMs: Math.round(performance.now() - start),
error: error.message,
};
}
}
Step 7: Prompt Templates
// src/mistral/prompts.ts
interface PromptTemplate {
name: string;
system: string;
userTemplate: (input: string) => string;
model: string;
maxTokens: number;
}
export const PROMPTS: Record<string, PromptTemplate> = {
summarize: {
name: 'summarize',
system: 'Summarize the text in 2-3 sentences. Be factual and concise.',
userTemplate: (text) => `Summarize:\n\n${text}`,
model: 'mistral-small-latest',
maxTokens: 200,
},
classify: {
name: 'classify',
system: 'Classify the input. Reply with one word only.',
userTemplate: (text) => text,
model: 'mistral-small-latest',
maxTokens: 10,
},
codeReview: {
name: 'codeReview',
system: 'Review code for bugs, security issues, and improvements. Be specific.',
userTemplate: (code) => `Review this code:\n\`\`\`\n${code}\n\`\`\``,
model: 'mistral-large-latest',
maxTokens: 1000,
},
};
Error Handling
| Issue | Cause | Resolution |
|---|---|---|
| Config validation error | Missing/invalid env vars | Check Zod error message |
| Rate limit (429) | RPM/TPM exceeded | MistralServiceError has retryable: true |
| Auth error (401) | Invalid API key | Not retryable, check credentials |
| Cache ineffective | High temperature | Only cache temperature=0 requests |
Resources
Output
- Layered directory structure with clear separation
- Zod-validated configuration from environment
- Singleton client with lazy initialization
- Custom error classes with retryability
- Service layer with caching and streaming
- Health check endpoint
- Reusable prompt templates
When not to use it
- →High-temperature requests that are non-deterministic
- →Applications requiring non-TypeScript environments
- →Projects not utilizing the Mistral AI SDK
Prerequisites
Limitations
- →Caching is only effective for temperature=0 requests
- →Requires manual configuration of environment variables
How it compares
It enforces a strict layered architecture and centralized error handling instead of ad-hoc integration scripts.
Compared to similar skills
mistral-reference-architecture side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| mistral-reference-architecture (this skill) | 1 | 27d | Review | Intermediate |
| mcp-builder | 136 | 3mo | Review | Advanced |
| architecture-patterns | 55 | 2mo | No flags | Advanced |
| nodejs-best-practices | 28 | 6mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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