Together AI First Inference

Overview

This skill produces the smallest observable chat-completion probe without freezing a model catalog entry into application policy.

Prerequisites

• A configured TOGETHER_API_KEY secret reference
• Together Python SDK v2, current TypeScript SDK, or an HTTPS client
• A current chat model resolved from the Together catalog
• A small approved prompt containing no sensitive data

Tool Discipline

Use Read, Glob, and Grep to inspect the client wrapper and configuration. Use WebFetch for the current quickstart, model catalog, and response contract. Use Write or Edit only for the approved sample or test file; never place the key in code.

Current Contract

• Call client.chat.completions.create() with a Together model ID such as the current quickstart example.
• For streaming, iterate chunks and guard for an empty choices array before reading delta.content.
• Capture finish_reason and usage; bound max_tokens, timeout, and retry count.
• Re-resolve the model from the live catalog before production use because availability and redirects change.

Authentication

The SDK reads TOGETHER_API_KEY. Direct REST requests send the same project key as a Bearer token to https://api.together.ai/v1/chat/completions. Never return or log the header.

Instructions

1. Confirm the requested modality is chat and select a current chat-capable model.
2. Create one system message and one sanitized user message with an explicit output bound.
3. Choose non-streaming for contract inspection or streaming for latency and chunk handling.
4. Validate status, non-empty content, finish reason, model identifier, and usage fields.
5. Record latency and request outcome without storing the prompt or response when either is sensitive.
6. Remove disposable output and hand off the model-selection rationale.

Approval Boundaries

Do not send customer data, regulated content, or proprietary prompts until data handling and retention are approved. Do not silently switch to a more expensive or materially different model.

Output

Return the client/runtime, resolved model, streaming mode, bounded request parameters, status, finish reason, token usage, latency, and redaction disposition.

Error Handling

Examples

The example below shows the minimum redacted evidence expected from a successful invocation of this operator workflow.

model=catalog-resolved; stream=true; max_tokens=128; status=200; finish=stop; secret=redacted

Resources

• Skill-specific official documentation
• Quickstart
• Chat completions
• Chat API