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model-routerlisted

Plan AI model work across Microsoft Foundry, Hugging Face, and ElevenLabs using live provider evidence. Use when choosing a model or provider, comparing cross-provider options, decomposing multimodal work, estimating constraints, or preparing an executable plan before any paid service call.
fabioc-aloha/Alex_Skill_Mall · ★ 4 · AI & Automation · score 77
Install: claude install-skill fabioc-aloha/Alex_Skill_Mall
# Model Router Turn a task into an explainable, executable provider plan. This skill advises; it does not authorize or invoke paid provider work. ## Procedure 1. State the task goal and concrete deliverables. 2. Decompose compound requests into model-sized steps. Keep steps separate when they have different modalities, privacy boundaries, or validation methods. 3. Capture hard constraints: modality, quality, latency, privacy, region, license, budget, local-versus-hosted preference, and required output format. 4. Query all relevant live provider tools. Use the exact provider contract in [provider-contract.md](references/provider-contract.md). 5. Eliminate candidates that violate a hard constraint. Do not rank an infeasible candidate above a feasible one because its quality looks better. 6. Compare the feasible set. Separate observed evidence from provider claims and unknowns. 7. Select a primary model and ordered fallbacks. A recommendation may remain a Pareto set when quality, cost, privacy, and latency cannot be reduced to one honest score. 8. If the selected provider/model requires a credential, add a plain-language `Secret setup` section naming the provider-native login or exact host environment variable. Do not ask for the value before model selection, and do not write or echo it. 9. Emit a JSON plan conforming to [model-task-plan.schema.json](references/model-task-plan.schema.json). 10. Set `consent.status` to `pending` whenever any st