Tokenlab Model Picker

TokenLab Model Picker

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, 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=
• Model contract: GET https://api.tokenlab.sh/v1/models/:model
• Pricing detail: GET https://api.tokenlab.sh/v1/models/:model/pricing

1. Prefer exact public model IDs over family names.
2. Separate recommendation dimensions:

• quality or frontier capability
• cost sensitivity
• latency or fast iteration
• native endpoint needs
• multimodal input or output

1. 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.

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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: hedging8563
• Source: hedging8563/tokenlab-skills
• License: MIT
• Homepage: https://tokenlab.sh

Install and usage instructions live in the source repository linked above.