Together AI Error Diagnosis

Overview

This skill classifies failures by status and workload state, preserves evidence, and avoids turning permanent errors into costly retry storms.

Prerequisites

• Redacted status, response body, headers, SDK version, and request shape
• The current model ID, endpoint type, and Together project alias
• Batch, fine-tune, endpoint, file, or deployment IDs when applicable
• The caller's timeout and retry policy

Tool Discipline

Use Read, Glob, and Grep to inspect the adapter, logs, retry code, and manifests. Use WebFetch for current error, model, and lifecycle documentation. Use Write or Edit only for the approved fix or regression test after the failure class is proven.

Current Contract

• 400 means request/schema trouble; 401 authentication; 402 spend limit; 404 endpoint/model; 429 throttling; 500/503 server or overload.
• Context overflow can surface as 403 in Together's error table; inspect the body rather than classifying by status alone.
• Model availability and redirects change. Resolve 404 against the current catalog and deprecation page.
• A completed asynchronous job can still contain line-level failures; inspect its error artifact.

Authentication

Together APIs use a project-scoped Bearer key from TOGETHER_API_KEY. Verify presence and project routing without echoing the key. Treat a leaked header as a credential incident, not merely a request bug.

Instructions

1. Reproduce once with a sanitized minimal request and capture status, body, useful headers, latency, and SDK version.
2. Classify the error as credential, billing, request, model lifecycle, dynamic limit, transient provider, or asynchronous-job failure.
3. Compare the exact request and model to current primary documentation.
4. Apply the narrowest correction and add a deterministic regression case.
5. Retry only 429, 500, 503, or 504 with bounded jitter and an overall deadline.
6. Verify recovery, redact evidence, and report any provider-side incident separately.

Approval Boundaries

Do not broaden credentials, raise spend limits, switch models, or resubmit paid jobs automatically. Each changes authority, behavior, or cost and requires the relevant owner.

Output

Return the failure class, sanitized evidence, root cause, correction, retry disposition, regression coverage, and any required owner action.

Error Handling

Examples

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

status=404; class=model-lifecycle; catalog=checked; substitution=approval-required; retry=no

Resources

• Skill-specific official documentation
• Error codes
• Deprecations
• Dynamic rate limits