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Guidance for the Terminator's qualitative stop check: judge whether the optimization has plateaued in ways the numeric scores don't capture, from the latest round's analysis text, and return stop / continue.
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。
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Fine-tune models on NeMo Platform with `automodel`, `unsloth`, or `rl` (all `submit`-only): HF dataset conversion, filesets, model entities, and job JSON (hyperparameters, batch, schedule, optimizer) + job polling. `automodel`/`unsloth` run SFT/LoRA as Docker GPU jobs; `rl` runs DPO (preference) or GRPO (NeMo Gym environment + reward) on a Ray cluster (Kubernetes). Covers building and converting NeMo Gym environment packages (native-v1, wheels-v1, adapter-wheels-v1) and Gym rollout-row datasets. Use for train, fine-tune, customize, SFT, LoRA, DPO, GRPO, RLHF, reinforcement learning, reward environment, NeMo Gym, verifiers, Prime Intellect, preference optimization, learning rate, epochs, or nemo customization.
Generate, validate, and measure an audit-spec coverage denominator for Eval Author. Use when the user wants a hand-editable audit.md file derived from Ethos, needs schema enforcement for declared tools, capabilities, failure cases, evidence, and references, or wants to measure which audit items one ATIF trace covers. Changes none of the user's source, and saves audit artifacts under `.eval-author/`.
Work on evaluation suites in a user's repository or understand an agent run from NeMo Intake. Owns the evidence standard that every Eval Author sub-flow follows. Use when the user asks "help me with my evals", "what's the state of the eval suite here?", "what happened in this trace?", or when you need to pick between the Eval Author sub-flows. Routes to a sub-flow and changes none of the user's source. The selected sub-flow uses the provider's supported tools and saves its findings under `.eval-author/`.
基于 SOC 职业分类
正在显示 SKILL.md
| name | terminator |
| description | Guidance for the Terminator's qualitative stop check: judge whether the optimization has plateaued in ways the numeric scores don't capture, from the latest round's analysis text, and return stop / continue. |
| triggers | ["has the optimization qualitatively plateaued","should we stop given this round analysis"] |
| not-for | ["the round-budget or Pareto-front convergence checks (those are deterministic)","proposing candidates (Proposer) or analyzing failures (Analyzer)"] |
| compatibility | Internal guidance prefilled into Terminator.qualitative_stop_check (`components/terminator.py`); consumed by the agent at runtime, not user-invocable. |
| maturity | alpha |
| license | Apache-2.0 |
| user-invocable | false |
You are deciding whether the optimization has qualitatively plateaued — in ways
the numeric scores don't capture — from the latest round's analysis text. This
runs only after the deterministic checks (round budget, Pareto-front convergence)
were inconclusive.
Return True (stop) only when you can cite specific evidence in analysis, such as:
Return False (continue) when:
analysis is short, missing, or describes ongoing improvement,analysis.When in doubt, return False — stopping early forfeits the remaining round budget.