Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add YangsonHung/awesome-agent-skills --skill routerbase-model-routing-cngit clone --depth 1 https://github.com/YangsonHung/awesome-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yangsonhung/awesome-agent-skills/routerbase-model-routing-cn)<a href="https://agentmods.dev/skills/yangsonhung/awesome-agent-skills/routerbase-model-routing-cn"><img src="https://agentmods.dev/badge/skills/yangsonhung/awesome-agent-skills/routerbase-model-routing-cn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yangsonhung/awesome-agent-skills/routerbase-model-routing-cn"><img src="https://agentmods.dev/badge/skills/yangsonhung/awesome-agent-skills/routerbase-model-routing-cn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.00420 |
| Opus 5 | $0.00018 | $0.00210 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
routerbase-model-routing-cn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
RouterBase 模型路由
Overview
使用 routerbase 在统一的 OpenAI 兼容集成表面下规划模型选择。本技能将工作负载需求转化为模型候选清单、fallback 链路、成本与延迟策略，以及上线前验证清单。
何时使用
当用户提出以下需求时使用本技能：
- 为聊天、代码、推理、视觉或多模态工作负载选择 RouterBase 模型
- 为供应商故障或质量下降设计 fallback 链路
- 在成本、延迟、上下文长度、输出质量和可用性之间做取舍
- 为生产应用或 Agent 工作流编写模型路由规则
- 制定模型行为上线前验证计划
不要使用
以下场景不应使用本技能：
- 未查验当前来源数据就断言精确价格、可用性或模型表现
- 不说明假设和 fallback 行为，只推荐单一模型
- 在应用负责人或生产运维人员不知情的情况下隐藏路由变更
使用说明
- 明确工作负载类型、质量阈值、延迟预算、上下文大小、预期流量和失败容忍度。
- 按角色整理候选模型：主模型、经济型 fallback、高质量 fallback 和专项 fallback。
- 清楚标注路由假设，并把需要查验当前目录或价格的数据单独标记。
- 推荐小步上线流程：测试提示词、golden case、日志、重试策略、限流处理和回滚条件。
- 示例尽量保持 OpenAI 兼容，让应用集成保持可迁移性。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 35 lines · 37 tokens per session scan A 301c7d3d455c
routerbase-model-routing-cn is a skill published in the GitHub repository YangsonHung/awesome-agent-skills (18 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 420 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
ai-workflow-architect
Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong. Use this to automate part of an operation, design an agent or MCP workflow, reduce repetitive manual work, connect tools into a…
few-shot-examples
Curated few-shot examples for construction AI tasks: classification, extraction, analysis. Domain-specific examples for improved LLM performance.
813-regulations-iso-42001
Use when reviewing, designing, or modifying Java enterprise systems that use GenAI, LLMs, AI-assisted coding, RAG, AI agents, generated code, generated dependencies, prompt workflows, external model providers, or AI-enabled business logic and need ISO/IEC 42001 AI management system-aware engineering guidance. Part of…
down-skilling
Distill Opus-level reasoning into optimized instructions for Haiku 4.5 (and Sonnet). Generates explicit, procedural prompts with n-shot examples that maximize smaller model performance on a given task. Use when user says "down-skill", "distill for Haiku", "optimize for Haiku", "make this work on Haiku", "generate…