Skillshub clade-model-inference

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/jeremylongshore/claude-code-plugins-plus-skills/clade-model-inference" ~/.claude/skills/comeonoliver-skillshub-clade-model-inference && rm -rf "$T"

manifest: skills/jeremylongshore/claude-code-plugins-plus-skills/clade-model-inference/SKILL.md

Anthropic Messages API — Streaming & Advanced Patterns

Overview

The Messages API is the only inference endpoint. Every Claude interaction goes through

client.messages.create()

. This skill covers streaming, system prompts, vision, and structured output.

Prerequisites

Completed
```
clade-install-auth
```
Familiarity with
```
clade-hello-world
```

Instructions

Step 1: Streaming Responses

import Anthropic from '@claude-ai/sdk';

const client = new Anthropic();

const stream = client.messages.stream({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  messages: [{ role: 'user', content: 'Write a haiku about TypeScript.' }],
});

for await (const event of stream) {
  if (event.type === 'content_block_delta' && event.delta.type === 'text_delta') {
    process.stdout.write(event.delta.text);
  }
}

const finalMessage = await stream.finalMessage();
console.log('\n\nTokens:', finalMessage.usage);

Step 2: Vision — Sending Images

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: fs.readFileSync('screenshot.png').toString('base64'),
        },
      },
      { type: 'text', text: 'Describe what you see in this image.' },
    ],
  }],
});

Step 3: JSON / Structured Output

const message = await client.messages.create({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  system: `Respond with valid JSON only. Schema: { "summary": string, "sentiment": "positive"|"negative"|"neutral", "confidence": number }`,
  messages: [{ role: 'user', content: 'Analyze: "This product exceeded my expectations!"' }],
});

const result = JSON.parse(message.content[0].text);
// { summary: "Very positive review", sentiment: "positive", confidence: 0.95 }

Python Streaming

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 Python."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

print(f"\nTokens: {stream.get_final_message().usage}")

Output

Non-streaming: Full
```
Message
```
object with
```
content
```
,
```
usage
```
,
```
stop_reason
```
Streaming events:
- ```
message_start
```
  — message metadata
- ```
content_block_start
```
  — new content block beginning
- ```
content_block_delta
```
  — incremental text (
```
text_delta
```
  ) or tool input (
```
input_json_delta
```
  )
- ```
message_delta
```
  — final
```
stop_reason
```
  and usage
- ```
message_stop
```
  — stream complete

Error Handling

Error Cause Solution

overloaded_error

(529)

Anthropic API temporarily overloaded

Retry with exponential backoff; use

client.messages.create

with built-in retries

rate_limit_error

(429)

Exceeded RPM or TPM

Check

retry-after

header. See

clade-rate-limits

invalid_request_error

Image too large or bad format

Max 20 images per request. Supported: PNG, JPEG, GIF, WebP. Max 5MB each

Key Parameters

Parameter	Type	Description
`model`	string	Required. Model ID (e.g. `claude-sonnet-4-20250514` )
`max_tokens`	int	Required. Maximum output tokens (1–8192 typical)
`messages`	array	Required. Alternating user/assistant messages
`system`	string	Optional. System prompt for behavior/persona
`temperature`	float	Optional. 0.0–1.0, default 1.0
`top_p`	float	Optional. Nucleus sampling threshold
`stop_sequences`	string[]	Optional. Custom stop strings
`stream`	boolean	Optional. Enable SSE streaming

Examples

See Step 1 (streaming), Step 2 (vision with base64 images), and Step 3 (structured JSON output) above. Python streaming example included.

Resources

Next Steps

See

clade-embeddings-search

for tool use and function calling patterns.