TokenLab Model Picker

Pick models from TokenLab catalog data

Use this skill when a user asks which TokenLab model to use, how to compare model options, or how to route a workload across model families.

What this skill should deliver

• A short model shortlist with exact TokenLab model IDs.
• The workload assumptions used to pick the models.
• A public catalog lookup path that the user or agent can rerun.
• A fallback model when the first choice is unavailable or too expensive.
• A caveat when a recommendation depends on volatile pricing, availability, or benchmark data.

Preferred approach

1. Identify the workload: chat, coding, agent loop, image, video, audio, embedding, rerank, translation, decisions, or multimodal.
2. Use the public model catalog before recommending hardcoded IDs:
   • General catalog: GET https://api.tokenlab.sh/v1/models
   • Task shortlist: GET https://api.tokenlab.sh/v1/models?recommended_for=<scene>
   • Model contract: GET https://api.tokenlab.sh/v1/models/:model
   • Pricing detail: GET https://api.tokenlab.sh/v1/models/:model/pricing
3. Prefer exact public model IDs over family names.
4. Separate recommendation dimensions:
   • quality or frontier capability
   • cost sensitivity
   • latency or fast iteration
   • native endpoint needs
   • multimodal input or output
5. Return a compact table, then one runnable API example if useful.

Default shortlist patterns

• Coding and agent work: choose a strong reasoning/coding model, a cheaper fallback, and a fast iteration model.

• General chat: choose one balanced model and one lower-cost fallback.

• Image or video: use recommended_for=image or recommended_for=video instead of guessing request shapes.

• Embeddings, rerank, translation, TTS, STT, music, or 3D: use the task-specific shortlist and inspect the model contract before showing parameters.

• Bounded semantic decisions: discover category=decision, inspect the System One contract, and compare task error and escalation coverage on local examples. Use deterministic code for exact calculations and a generative model when the output must be free text. Do not select a decision model as the agent’s chat engine.

Output format

• One sentence naming the workload assumptions.
• A table with Use, Model ID, Why, and Fallback.
• One catalog command the user can rerun.
• One warning line if availability, pricing, or provider-native behavior must be verified.

Avoid

• Do not claim a single universal best model.
• Do not recommend provider-prefixed or physical route names as public model IDs.
• Do not invent prices or model counts.
• Do not silently translate a native-only need into a generic chat completion.
• Do not recommend a model that is absent from the current public catalog.

Edge Cases

• If the user asks for the cheapest option, still include capability limits.
• If the user asks for a benchmark winner, require a cited benchmark and observed date.
• If the catalog is unavailable, say so and fall back to the last known examples only as examples, not truth.