TokenLab API Integration

Integrates TokenLab chat, image, audio, video, and other API families into code with runnable examples, model discovery, public contract checks, and agent-fi...

Built for runnable integration work for TokenLab chat, image, audio, video, and other API families across coding agents.

What this skill should deliver

• A minimal runnable example using the fewest moving parts possible.
• The exact base URL, auth shape, install command, and environment variables required to run the example.
• A concise note on when to stay on the OpenAI-compatible path versus switching to a native Anthropic or Gemini route.
• For non-chat APIs, a model discovery or contract check before hardcoding request shape details.
• A concrete default model choice that is plausible on TokenLab, not a generic placeholder.
• A short explanation of the agent-first recovery path when the model, endpoint, or route guess is wrong.

Preferred approach

1. Clarify the user's goal, inputs, and required deliverable.
2. Read references/usage-notes.md before acting.
3. Produce one concrete output before adding explanation.
4. Use the following operating rules:

• Start with the smallest working example before introducing abstractions or helper layers.
• State the base URL explicitly and keep the environment setup copy-pasteable.
• When model selection is open, show how to discover models through /v1/models or https://api.tokenlab.sh/llms.txt instead of hardcoding one option.
• For non-chat model selection, prefer GET /v1/models?recommended_for=<scene> where <scene> is one of image, video, music, 3d, tts, stt, embedding, rerank, or translation.
• Before retrying a failed non-chat request, read GET /v1/models/:model and align with the public contract, including supported_operations, supported_parameters, request_endpoint, request_shape_mode, and recommended_request.
• Use native Anthropic or Gemini examples only when the request explicitly needs provider-specific behavior.

Output format

• One short intro sentence explaining what the example does.
• One runnable code block only.
• One shell setup block showing both dependency install and the exact environment variable export.
• One short model discovery note.
• One short routing note explaining when to stay on the OpenAI-compatible path and when a response header or provider-specific feature suggests a native Anthropic or Gemini route.

Avoid

• Do not return pseudo-code when runnable code is expected.
• Do not hide required environment variables, auth headers, or base URLs.
• Do not over-claim pricing, speed, or compatibility without grounding it in a concrete example or source.
• Do not claim an exact platform-wide model count; say "hundreds of models" unless the current API response is being quoted directly.
• Do not silently drop unsupported non-chat fields. If removing a field would change user intent, safety, billing, or response guarantees, surface the contract error and fail closed.

Inputs

• Natural-language user request
• Referenced files or URLs
• Existing project context, if available

Outputs

• A concrete deliverable, recommendation, or implementation result
• Short notes on assumptions, caveats, or next actions when needed

Edge Cases

• If required inputs are missing, state exactly what is missing.
• If the request only partially matches this skill, handle the matching portion and clearly scope the rest.
• If a risk, safety, or compliance concern appears, surface it before producing the final output.