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hedging8563/tokenlab-skills

Public distribution mirror for TokenLab agent skills

What is tokenlab-skills?

tokenlab-skills is a Claude Code agent skill that public distribution mirror for TokenLab agent skills.

Works with~Claude Code~Codex CLI~Cursor
npx skills add hedging8563/tokenlab-skills

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Documentation

TokenLab Cost Routing

Use this skill when a user asks how to reduce TokenLab cost, compare model prices, pick fallbacks, or route requests by quality, latency, and budget.

What this skill should deliver

  • A compact routing recommendation with exact public TokenLab model IDs.
  • A cost-aware fallback chain for the user's workload.
  • A catalog/pricing lookup path that can be rerun.
  • A note on which endpoint family each model should use.
  • Guardrails for when not to switch models because doing so would change output, safety, or request semantics.

Preferred approach

  1. Identify the workload and constraints:
    • chat, coding, agent loop, image, video, audio, embedding, rerank, translation, or multimodal
    • quality floor
    • latency target
    • budget or cost ceiling
    • native endpoint requirement
  2. Read live public catalog signals before recommending:
    • GET https://api.tokenlab.sh/v1/models
    • GET https://api.tokenlab.sh/v1/models?recommended_for=<scene>
    • GET https://api.tokenlab.sh/v1/models/:model
    • GET https://api.tokenlab.sh/v1/models/:model/pricing
  3. Build a chain with roles:
    • primary quality model
    • balanced default
    • fast fallback
    • budget fallback
  4. If the user asks for exact cost, compute from live pricing and their estimated token/media volume. State units and assumptions.
  5. For non-chat requests, inspect model details before changing parameters or endpoint family.

Output format

  • One sentence stating workload and assumptions.
  • A table with Route role, Model ID, Endpoint, Why, and When to fall back.
  • One catalog command and one pricing command.
  • A short implementation note for retries, rate limits, and user approval when quality would drop.

Avoid

  • Do not invent prices, discounts, or model counts.
  • Do not choose a cheaper model if that would silently remove required native behavior, tools, media support, safety constraints, or structured output guarantees.
  • Do not expose TokenLab internal channel, physical provider, or routing details.
  • Do not turn a user-provided model into a different model without saying why.
  • Do not hardcode a fallback list without saying when it was checked or how to refresh it.

Edge Cases

  • If catalog or pricing endpoints are unavailable, say that routing cannot be price-verified and provide only an example pattern.
  • If the user asks for "cheapest", include capability and reliability tradeoffs.
  • If billing risk is high, require explicit user approval before adding automatic fallback to paid media/video generation.

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