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Use for Qwen/Ollama/...\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-04-30T08:05:20.172Z | user\n\nInitial release 2026-04-30\n\nArchive index:\n\nArchive v1.0.0: 4 files, 3614 bytes\n\nFiles: scripts/local_model_quantization_router.py (3260b), skill-card.md (1715b), SKILL.md (1508b), _meta.json (150b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: local-model-quantization-router\ndescription: Recommend local LLM model routes and quantization levels using hardware, privacy, task complexity, context size, and budget constraints. Use for Qwen/Ollama/local endpoint cost-performance decisions.\n---\n\n# Local Model Quantization Router\n\nUse this skill to choose between local quantized models and cloud fallback before running OpenClaw workloads.\n\n## Workflow\n\n1. Describe available hardware and task requirements.\n2. Run `scripts/local_model_quantization_router.py` with CLI flags or JSON input.\n3. Review the recommended model family, quantization level, endpoint, fallback, and risk notes.\n4. Use the output as routing evidence for local-first or privacy-first deployments.\n\n## Parameters\n\n- `--task TEXT`: Task summary.\n- `--complexity {simple,standard,complex,critical}`.\n- `--privacy {low,normal,high,regulated}`.\n- `--vram-gb FLOAT`: GPU memory available.\n- `--ram-gb FLOAT`: System memory available.\n- `--context-tokens INT`: Required context window.\n- `--hardware PATH`: Optional JSON with `vram_gb`, `ram_gb`, `cpu_only`.\n- `--output PATH`: Optional JSON output path.\n\n## Outputs\n\n- `route`: `local-only`, `local-first`, `hybrid`, or `cloud-required`.\n- `model`: Recommended model family.\n- `quantization`: Suggested quant level.\n- `endpoint`: Suggested local endpoint type.\n- `fallback`: Safer fallback when quality or context is insufficient.\n- `reasons`: Evidence for the decision.\n\nNo model is downloaded and no config file is changed.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn73twx1jk9r68jj31vxfhtdgn82c3t9\",\n  \"slug\": \"local-model-quantization-router\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1777536320172\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nRecommend local LLM model routes and quantization levels using hardware, privacy, task complexity, context size, and budget constraints.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[stevojarvisai-star](https://clawhub.ai/user/stevojarvisai-star)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and engineers use this skill to choose local quantized model routes and cloud fallback posture before running OpenClaw workloads, especially for privacy, cost, context-size, and hardware-constrained deployments.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Optional hardware input and output paths are user-directed file operations.\n\nMitigation: Use explicit paths for --hardware and --output and avoid pointing --output at important files.\n\nRisk: Routing recommendations may be insufficient for quality-critical or very large-context workloads.\n\nMitigation: Review the returned fallback and risk notes before relying on the recommendation for deployment decisions.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [Guidance, JSON, Shell commands]\n\n**Output Format:** [JSON recommendations with human-readable routing evidence]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May write an optional JSON output file when the user provides an explicit output path.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: Local Model Quantization Router Owner: stevojarvisai-star Summary: Recommend local LLM model routes and quantization levels using hardware, privacy, task complexity, context size, and budget constraints. Use for Qwen/Ollama/... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-30T08:05:20.172Z | user Initial release 2026-04-30 Archive index: Archive v1.0.0: 4 files, 3614 bytes Files: scripts/local_model_qua","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: local-model-quantization-router\ndescription: Recommend local LLM model routes and quantization levels using hardware, privacy, task complexity, context size, and budget constraints. Use for Qwen/Ollama/local endpoint cost-performance decisions.\n---\n\n# Local Model Quantization Router\n\nUse this skill to choose between local quantized models and cloud fallback before running OpenClaw workloads.\n\n## Workflow\n\n1. Describe available hardware and task requirements.\n2. Run `scripts/local_model_quantization_router.py` with CLI flags or JSON input.\n3. Review the recommended model family, quantization level, endpoint, fallback, and risk notes.\n4. Use the output as routing evidence for local-first or privacy-first deployments.\n\n## Parameters\n\n- `--task TEXT`: Task summary.\n- `--complexity {simple,standard,complex,critical}`.\n- `--privacy {low,normal,high,regulated}`.\n- `--vram-gb FLOAT`: GPU memory available.\n- `--ram-gb FLOAT`: System memory available.\n- `--context-tokens INT`: Required context window.\n- `--hardware PATH`: Optional JSON with `vram_gb`, `ram_gb`, `cpu_only`.\n- `--output PATH`: Optional JSON output path.\n\n## Outputs\n\n- `route`: `local-only`, `local-first`, `hybrid`, or `cloud-required`.\n- `model`: Recommended model family.\n- `quantization`: Suggested quant level.\n- `endpoint`: Suggested local endpoint type.\n- `fallback`: Safer fallback when quality or context is insufficient.\n- `reasons`: Evidence for the decision.\n\nNo model is downloaded and no config file is changed."},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn73twx1jk9r68jj31vxfhtdgn82c3t9\",\n  \"slug\": \"local-model-quantization-router\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1777536320172\n}"},{"path":"skill-card.md","content":"## Description:\n\nRecommend local LLM model routes and quantization levels using hardware, privacy, task complexity, context size, and budget constraints.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[stevojarvisai-star](https://clawhub.ai/user/stevojarvisai-star)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and engineers use this skill to choose local quantized model routes and cloud fallback posture before running OpenClaw workloads, especially for privacy, cost, context-size, and hardware-constrained deployments.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Optional hardware input and output paths are user-directed file operations.\n\nMitigation: Use explicit paths for --hardware and --output and avoid pointing --output at important files.\n\nRisk: Routing recommendations may be insufficient for quality-critical or very large-context workloads.\n\nMitigation: Review the returned fallback and risk notes before relying on the recommendation for deployment decisions.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [Guidance, JSON, Shell commands]\n\n**Output Format:** [JSON recommendations with human-readable routing evidence]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May write an optional JSON output file when the user provides an explicit output path.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Recommend local LLM model routes and quantization levels using hardware, privacy, task complexity, context size, and budget constraints. 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