菜单
Compatibility shim for Codex installs. Use when a developer asks to install, update, enable, reinstall, remove, or verify the Understudy Codex plugin - "install Understudy in Codex", "add the Codex plugin", "make Codex see the skills". Route to install-agent-adapter with platform codex.
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。
基于 SOC 职业分类
| name | install-codex-plugin |
| description | Compatibility shim for Codex installs. Use when a developer asks to install, update, enable, reinstall, remove, or verify the Understudy Codex plugin - "install Understudy in Codex", "add the Codex plugin", "make Codex see the skills". Route to install-agent-adapter with platform codex. |
| metadata | {"understudy":{"mode":"automatic","safety":"local-first","cli_required":false}} |
This skill is a compatibility shim. The consolidated setup surface is
install-agent-adapter.
Load install-agent-adapter and run it with platform codex.
Use its reference.md for the
current marketplace registration:
codex plugin marketplace add "$REPO"
Activation is in Codex:
/plugins
Choose understudy-skills, install or enable understudy, then ask:
Use the Understudy onboarding skill for this project.
Local-first and free: registering the Codex marketplace only makes local skills available. It must not authenticate, upload data, inspect secret values, download model weights, start hosted jobs, or make provider calls.
~/.codex/config.toml by hand unless the user explicitly asks.No Understudy CLI command is required. Use codex plugin marketplace add when
available; otherwise show the manual /plugins activation path from
install-agent-adapter/reference.md.
End with: that this routed through install-agent-adapter; whether the Codex
marketplace was registered/refreshed/shown for manual run; the /plugins step;
the next onboarding prompt; and the marketplace removal command.
Use when a developer wants to compare candidate models — any mix of local, gateway, or frontier — on the same eval and see quality, latency, cost, and reliability side by side. "Which model should I use", "sweep these models on my benchmark", "compare Gemma vs the frontier on my eval". To stand up and serve a local candidate first, use run-local-model-lab.
Use when a developer's agent — a multi-turn tool-calling loop — should get cheaper, faster, or better. "My agent is too slow", "this workflow costs too much", "test a cheaper model in my tool-calling loop", "A/B the policy model". Covers read-only search loops and state-mutating API workflows alike.
Use when a coding agent must operate the full benchmark lifecycle over local benchmark dirs — "build a benchmark from my traces and run models on it", "review and calibrate the eval", "queue a prompt experiment", "is an executor running", "read the rigor report". Covers traces → build-benchmark → review/feedback → calibration floors → candidate and prompt-override runs → rigor/CI reading → app-replay regression, via the benchmarks MCP server or CLI verbs, plus the run-executor daemon lifecycle.
Use to build a simulated, seeded environment (AutomationBench / verifiers style) so any model can run a captured agentic workload end-to-end and be scored on final state — "simulate this workload's tools", "build a validator for these traces", "let a small model attempt the whole task", "score recall/precision against gold", or any handoff from understand-workload toward whole-case model comparison.
Use to acquire, cache, organize, and explain local open-weight models — "download a model", "what models do I have", "where did the weights go", "free up model disk", "which Gemma/Nemotron should I pull", "how do open models work". Covers where weights come from and live, formats/quantization, gated weights and HF tokens, disk budgeting, start-small-and-cache, and the local→cloud graduation path. American families (Gemma 4, Nemotron 3). To score a local model on a workload, use run-local-model-lab.
Use when a user asks what Understudy is, how Understudy Desktop works, how local model serving, Fusion sidekick, evals, model candidate results, rollout labs, Product Knowledge, or Understudy product capabilities should be explained to developers, customers, or agents.