一键导入
sr-config-domain-based-skill
Generate an optimized Semantic Router configuration from model benchmark results
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Generate an optimized Semantic Router configuration from model benchmark results
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
| name | sr-config-domain-based-skill |
| description | Generate an optimized Semantic Router configuration from model benchmark results |
This skill allows the agent to automatically generate and deploy an optimal Semantic Router configuration (config.yaml) based on model benchmark results.
The routing logic computes the best model per domain using categorical accuracy, latency, and token throughput.
Use this skill when the user asks to:
This skill expects model benchmark results in JSON format. The benchmarks must first be retrieved using the available get_model_benchmarks MCP tool.
Example benchmark format:
[
{
"model": "gpt-3.5-turbo",
"results": {
"avgResponseTime": 0.6834,
"categoryAccuracy": {
"biology": 0.6,
"business": 0.4
},
"overallAccuracy": 0.4143,
"tokensPerSecond": 20.59
}
}
]
To use this skill, you MUST follow these exact steps in order:
sr-llm-backends MCP tool to fetch the current model benchmarks.generate-router-config.py passing the benchmark JSON directly as a string argument.
python scripts/generate-router-config.py '[{"model": "qwen2.5:3b", "results": ...}]'
stdout. Capture this output, save it or pass it directly to your Kubernetes MCP tools to apply it to the cluster (e.g. kubernetes.apply).In write-enabled environments, scripts/generate-router-config.py outputs:
config.yaml: The raw semantic router configuration.temp-router-manifest.yaml: The Kubernetes ConfigMap defining the configurations.routing-summary.json: A summary of which models were selected for each domain.
(If the filesystem is read-only, it skips generating physical files and pushes only the complete manifest to stdout)基于 SOC 职业分类