{
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    "slug": "model-router-premium",
    "name": "Model Router",
    "source": "tencent",
    "type": "skill",
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    "sections": [
      {
        "title": "model-router",
        "body": "This skill provides a compact, runnable router that inspects an OpenClaw-style configuration (or a simple models JSON) and selects an appropriate model for an incoming request based on:\n\ndeclared model capabilities and an optional cost score\ntask complexity (heuristic: short/simple vs long/complex)\nexplicit overrides (user or caller hints)\n\nDesign principles\n\nKeep decision logic small and deterministic.\nDefault to the cheapest model for general, not-complex tasks.\nEscalate to stronger models when the task appears complex or asks for high-fidelity results.\nMake model metadata explicit (capabilities, cost_score, tags) so the router is transparent and auditable.\n\nWhat this skill includes\n\nscripts/router.py — a small CLI and library to pick a model given a task description and a models configuration file.\nexamples/models.json — example model configurations (name, provider, cost_score, capabilities).\n\nWhen to use\n\nTrigger when you need to programmatically choose which LLM to call for a user request.\nUse for batching or middleware routing in server apps.\n\nUsage (quick)\n\nPrepare a models file (JSON) with model entries. See examples/models.json.\nCall: python3 scripts/router.py --models examples/models.json --task \"Summarize this email\" --mode auto\nThe script prints the chosen model and the reasoning.\n\nFiles\n\nscripts/router.py — router CLI/library\nexamples/models.json — sample model list"
      }
    ],
    "body": "model-router\n\nThis skill provides a compact, runnable router that inspects an OpenClaw-style configuration (or a simple models JSON) and selects an appropriate model for an incoming request based on:\n\ndeclared model capabilities and an optional cost score\ntask complexity (heuristic: short/simple vs long/complex)\nexplicit overrides (user or caller hints)\n\nDesign principles\n\nKeep decision logic small and deterministic.\nDefault to the cheapest model for general, not-complex tasks.\nEscalate to stronger models when the task appears complex or asks for high-fidelity results.\nMake model metadata explicit (capabilities, cost_score, tags) so the router is transparent and auditable.\n\nWhat this skill includes\n\nscripts/router.py — a small CLI and library to pick a model given a task description and a models configuration file.\nexamples/models.json — example model configurations (name, provider, cost_score, capabilities).\n\nWhen to use\n\nTrigger when you need to programmatically choose which LLM to call for a user request.\nUse for batching or middleware routing in server apps.\n\nUsage (quick)\n\nPrepare a models file (JSON) with model entries. See examples/models.json.\nCall: python3 scripts/router.py --models examples/models.json --task \"Summarize this email\" --mode auto\nThe script prints the chosen model and the reasoning.\n\nFiles\n\nscripts/router.py — router CLI/library\nexamples/models.json — sample model list"
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    "provenanceUrl": "https://clawhub.ai/MrJootta/model-router-premium",
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    "owner": "MrJootta",
    "version": "0.1.1",
    "license": null,
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