brainstorming
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
anth-hello-worldCreate a minimal working Anthropic Claude Messages API example. Use when starting a new Claude integration, testing your setup, or learning basic Messages API patterns for text, vision, and streaming. Trigger with phrases like "anthropic hello world", "claude api example", "anthropic quick start", "simple claude code", "first messages api call".
Independent GitHub snapshot · Automated classification · Original documentation governs
name: anth-hello-world description: 'Create a minimal working Anthropic Claude Messages API example.
Use when starting a new Claude integration, testing your setup,
or learning basic Messages API patterns for text, vision, and streaming.
Trigger with phrases like "anthropic hello world", "claude api example",
"anthropic quick start", "simple claude code", "first messages api call".
' allowed-tools: Read, Write, Edit version: 1.7.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:
Three minimal examples covering the Claude Messages API core surfaces: basic text completion, vision (image analysis), and streaming responses.
anth-install-auth setupANTHROPIC_API_KEY in environmentanthropic package or Node.js 18+ with @anthropic-ai/sdkimport anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[
{"role": "user", "content": "Explain quantum computing in 3 sentences."}
]
)
# Response structure
print(message.content[0].text) # The actual text response
print(f"ID: {message.id}") # msg_01XFDUDYJgAACzvnptvVoYEL
print(f"Model: {message.model}") # claude-sonnet-4-20250514
print(f"Stop: {message.stop_reason}")# end_turn
print(f"Usage: {message.usage.input_tokens}in / {message.usage.output_tokens}out")
import Anthropic from '@anthropic-ai/sdk';
import * as fs from 'fs';
const client = new Anthropic();
// From file (base64)
const imageData = fs.readFileSync('chart.png').toString('base64');
const message = await client.messages.create({
model: 'claude-sonnet-4-20250514',
max_tokens: 1024,
messages: [{
role: 'user',
content: [
{
type: 'image',
source: {
type: 'base64',
media_type: 'image/png',
data: imageData,
},
},
{ type: 'text', text: 'Describe what this chart shows.' },
],
}],
});
console.log(message.content[0].type === 'text' ? message.content[0].text : '');
import anthropic
client = anthropic.Anthropic()
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": "Write a haiku about APIs."}]
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)
# Get final message with full metadata
final = stream.get_final_message()
print(f"\nTokens used: {final.usage.input_tokens}+{final.usage.output_tokens}")
Use the text example first when verifying credentials: send a fixed, short
prompt and confirm that message.content[0].text is present before integrating
the client into application code. Use the vision example only after that check
passes and replace chart.png with a non-sensitive local fixture. For an
interactive command-line feature, use the streaming example so text is emitted
incrementally, then read the final message to capture token usage for logs or
cost controls.
| Error | HTTP Code | Cause | Solution |
|---|---|---|---|
authentication_error | 401 | Invalid API key | Check ANTHROPIC_API_KEY |
invalid_request_error | 400 | Bad params (e.g., empty messages) | Validate request body |
rate_limit_error | 429 | Too many requests | Implement backoff (see anth-rate-limits) |
overloaded_error | 529 | API temporarily overloaded | Retry after 30-60s |
api_error | 500 | Server error | Retry with exponential backoff |
| Parameter | Required | Description |
|---|---|---|
model | Yes | Model ID: claude-sonnet-4-20250514, claude-haiku-4-20250514, claude-opus-4-20250514 |
max_tokens | Yes | Maximum output tokens (model-dependent max) |
messages | Yes | Array of {role, content} objects |
system | No | System prompt (string or content blocks) |
temperature | No | 0.0-1.0, default 1.0 |
top_p | No | Nucleus sampling (use temperature OR top_p) |
stop_sequences | No | Array of strings that stop generation |
stream | No | Enable SSE streaming |
Proceed to anth-local-dev-loop for development workflow setup.