菜单
Sub-skill of agent-usage-optimizer: Complexity Tier → Model Mapping.
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/vamseeachanta/workspace-hub --skill agent-usage-optimizer-complexity-tier-model-mapping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。
正在显示 SKILL.md
基于 SOC 职业分类
| name | agent-usage-optimizer-complexity-tier-model-mapping |
| description | Sub-skill of agent-usage-optimizer: Complexity Tier → Model Mapping. |
| version | 1.0.0 |
| category | ai |
| type | reference |
| scripts_exempt | true |
Use this alongside Route mapping when task nature is clear:
| Complexity tier | Keywords | Recommended model |
|---|---|---|
routine | format, rename, config, scaffold, update-doc, copy | Codex Haiku |
standard | implement, review, test, fix, migrate, document | Codex Sonnet |
complex | architecture, design, cross-repo, security, compound | Codex Opus |
Key insight: the gap between theoretical and observed exposure is an implementation gap —
not a capability gap. Routing routine tasks to cheaper models closes this gap for our workflow.
See WRK-5002 to automate tier detection in task_classifier.sh.
Use this skill before any multi-item work session or when quota is a concern.
Related: ai/optimization/model-selection, ai/optimization/usage-optimization
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.