Agent skill
mistral-hello-world
Create a minimal working Mistral AI chat completion example. Use when starting a new Mistral integration, testing your setup, or learning basic Mistral API patterns. Trigger with phrases like "mistral hello world", "mistral example", "mistral quick start", "simple mistral code", "mistral chat".
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/mistral-hello-world
SKILL.md
Mistral AI Hello World
Overview
Minimal working example demonstrating core Mistral AI chat completion functionality.
Prerequisites
- Completed
mistral-install-authsetup - Valid API credentials configured
- Development environment ready
Instructions
Step 1: Create Entry File
TypeScript (hello-mistral.ts)
import Mistral from '@mistralai/mistralai';
const client = new Mistral({
apiKey: process.env.MISTRAL_API_KEY,
});
async function main() {
const response = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'Say "Hello, World!" in a creative way.' }
],
});
console.log(response.choices?.[0]?.message?.content);
}
main().catch(console.error);
Python (hello_mistral.py)
import os
from mistralai import Mistral
client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))
def main():
response = client.chat.complete(
model="mistral-small-latest",
messages=[
{"role": "user", "content": "Say 'Hello, World!' in a creative way."}
],
)
print(response.choices[0].message.content)
if __name__ == "__main__":
main()
Step 2: Run the Example
# TypeScript
npx tsx hello-mistral.ts
# Python
python hello_mistral.py
Step 3: Streaming Response (Advanced)
TypeScript Streaming
import Mistral from '@mistralai/mistralai';
const client = new Mistral({
apiKey: process.env.MISTRAL_API_KEY,
});
async function streamChat() {
const stream = await client.chat.stream({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'Tell me a short story about AI.' }
],
});
for await (const event of stream) {
const content = event.data?.choices?.[0]?.delta?.content;
if (content) {
process.stdout.write(content);
}
}
console.log(); // newline
}
streamChat().catch(console.error);
Python Streaming
import os
from mistralai import Mistral
client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))
def stream_chat():
stream = client.chat.stream(
model="mistral-small-latest",
messages=[
{"role": "user", "content": "Tell me a short story about AI."}
],
)
for event in stream:
content = event.data.choices[0].delta.content
if content:
print(content, end="", flush=True)
print() # newline
if __name__ == "__main__":
stream_chat()
Output
- Working code file with Mistral client initialization
- Successful API response with generated text
- Console output showing:
Hello, World!
(But spoken by a million synchronized starlings,
spelling it across the twilight sky...)
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Import Error | SDK not installed | Verify with npm list @mistralai/mistralai |
| Auth Error | Invalid credentials | Check MISTRAL_API_KEY is set |
| Timeout | Network issues | Increase timeout or check connectivity |
| Rate Limit | Too many requests | Wait and retry with exponential backoff |
Examples
Multi-turn Conversation
const messages = [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: 'What is the capital of France?' },
];
const response1 = await client.chat.complete({
model: 'mistral-small-latest',
messages,
});
// Add assistant response to conversation
messages.push({
role: 'assistant',
content: response1.choices?.[0]?.message?.content || '',
});
// Continue conversation
messages.push({ role: 'user', content: 'What about Germany?' });
const response2 = await client.chat.complete({
model: 'mistral-small-latest',
messages,
});
console.log(response2.choices?.[0]?.message?.content);
With Temperature Control
const response = await client.chat.complete({
model: 'mistral-small-latest',
messages: [{ role: 'user', content: 'Write a haiku about coding.' }],
temperature: 0.7, // 0-1, higher = more creative
maxTokens: 100,
});
Resources
Next Steps
Proceed to mistral-local-dev-loop for development workflow setup.
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