菜单
Use when choosing, comparing, benchmarking, routing, or replacing generative, embedding, vision, speech, or classical ML models; extends core architecture selection with quality, latency, cost, privacy, deployment, and model-lifecycle trade-offs.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/blackprince001/skills --skill ai-ml-setup-model-selection命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Always-on coding style and organization rules for naming, file shape, comments, types, module boundaries, pattern use, errors, and avoiding unnecessary abstraction across languages and tracks.
Use when setting up or modifying CI pipelines — the quality-gate sequence every change passes, shift-left ordering, feeding failures back to agents, and keeping the pipeline under 10 minutes.
Use when designing any public interface — REST/GraphQL endpoints, module contracts, component props — Hyrum's Law, contract-first, consistent error semantics, validate at boundaries, extend rather than break.
基于 SOC 职业分类
正在显示 SKILL.md
| name | ai-ml-setup-model-selection |
| description | Use when choosing, comparing, benchmarking, routing, or replacing generative, embedding, vision, speech, or classical ML models; extends core architecture selection with quality, latency, cost, privacy, deployment, and model-lifecycle trade-offs. |
Apply core scoping, architecture, security, and NFR rules first. Select from measured task behavior, not leaderboards or permanent provider defaults; model catalogs, prices, and capabilities change.
Record task and modality, representative inputs, required output contract, minimum quality, failure cost, latency/throughput limits, context needs, privacy/residency, availability, deployment target, budget, and monitoring/exit plan. Establish the simplest meaningful baseline—including a deterministic or non-ML solution—before comparing models.
ai-ml-verify-evals for the evaluation design.Use hosted models when managed capability and iteration speed outweigh data/control constraints; use self-hosted or local models when privacy, offline operation, customization, predictable volume, or deployment control justifies hardware and operations ownership.
Add model routing only when evals show separable task classes and routing errors do not erase the benefit. Make the default path reliable before adding cheap-first escalation. A fallback must preserve the output contract and safety policy; degraded behavior is explicit to the product when quality materially changes. Never silently retry non-idempotent tool work through another model.
Version the chosen model/provider configuration with prompts and eval results. Document quality, latency, cost, privacy, availability, fallback, rollback, and replacement triggers. Re-evaluate on provider change, model deprecation, material traffic shift, or detected quality drift.