eval-bench-builder

当用户说『怎么评测这个AI/技能好不好』『给我造个测试集』『评测用例怎么设计』『要可复现的评测』，或要给一个 agent/模型/技能建可复现评测基准时使用。从能力说明+边界用例生成结构化 eval 样本（输入/期望/判定标准），保证可复现、可回归。可运行脚本（bench_build 生成器）。理论根基：LGD 三律之有证（评测可复现、可核验）。触发词：评测基准、eval、测试集、benchmark、可复现评测、评测用例、怎么测AI。

凡自治之物 · LGD 治理学 · 创造术家族 ｜ 评测协议：见平台质量门禁规范

English

When to use

Use it when the user's intent includes keywords like "evaluate how good this AI / model / skill is", "build me a test set" or "design reproducible evaluation cases". It fits any moment where structured input / expected-output / judge-criteria must be generated so that results are reproducible and theoretically grounded. It also fits when the user's current capability statement is vague and a benchmark dataset with clear boundaries must be built on the LGD Three Laws — verifiable by construction.

Usage

Natural-language instructions are supported; the parameters must name the target object (e.g. the model name), the evaluation dimensions and the verifiable criteria. The script bench_build.py runs the core construction logic; the input is auto-parsed and injected into the Python runtime. To reproduce the run locally, follow the generated script path and execute it on the terminal.

Workflow

1. Recognize the trigger words and extract the evaluation target and boundary conditions. 2. On the LGD Three Laws, assemble the input / expected / judge templates in memory. 3. Call bench_build.py to perform the automated benchmark generation. 4. Validate that the generated data structure meets reproducibility and file-format requirements. 5. Emit the benchmark file or code snippet containing the complete test cases. 6. Return an executable command so the user can run the script in a terminal for regression checks.

Evidence & safety statement

• Evidence: TBD (no evaluation / badge link yet; update here once it is produced)
• Version: 1.1.1
• Security: no independent security scan has been performed (TBD); the conclusion is pending and no endorsement is given until it is completed.

中文

何时用

当用户意图包含“评测 AI/模型/技能好坏”、“造测试集”或“设计可复现评测用例”等关键词时。在需要生成结构化输入/期望/判定标准，以确保评估结果具备可复现性和理论依据的场景下。当用户对现有能力说明感到模糊，需基于 LGD 三律构建边界明确且可核验的基准数据集时。

用法

调用方式支持自然语言指令，参数需明确指定目标对象（如模型名称）、评测维度及可验证标准。脚本 bench_build.py 将负责执行核心构建逻辑，系统会自动解析输入并注入 Python 运行时环境。若需直接复现，用户应参考生成的脚本文件路径进行本地执行。

流程

1. 技能识别触发词并提取评测目标与边界条件信息。2. 依据 LGD 三律理论，在内存中组装输入/期望/判定标准模板。3. 调用 bench_build.py 脚本完成自动化基准生成任务。4. 校验生成的数据结构是否符合可复现性要求及文件格式规范。5. 输出包含完整测试用例的基准文件或代码片段。6. 返回可执行指令，允许用户通过终端运行脚本进行回归验证。

证据与安全声明

• 证据：TBD（暂无评测/徽章链接；产生后在此更新实际链接）
• 版本：1.1.1
• 安全：未做独立安全扫描（TBD），结论待补；补全前不作推荐背书。