此技能识别 GitHub Agentic Workflows 相关请求的类型,并引导代理读取对应提示或技能文件,涵盖设计、创建、配置、更新、调试和升级。
- 设计或创建新工作流
- 更新现有工作流
- 调查工作流错误
此技能按原样执行指定的命令行测试,保留命令、输入、顺序和预期条件;逐项准备、操作并验证,再依据实际证据标记通过、失败、受阻或仅作观察。
Internal exact-case flow for cli-exercise. Preserves supplied commands, inputs, ordering, assertions, and output constraints.
实测评分 未实测
查看安装方法此技能提供执行指定命令行测试的步骤,并保留请求者给出的命令、输入、顺序、案例编号和预期条件。每个案例只涉及一种行为,依次完成准备、操作和验证后,才开始下一个案例。多个命令可以用于准备或验证;但如果需要改变案例结构,代理会先询问,不会自行拆分、调整顺序或添加步骤。操作前会观察实际屏幕;即使出现意外提示,也不会擅自重新解释测试。判断结果时,会将指定预期与实际观察到的状态或输出进行对照。与预期相矛盾的结果标记为失败;无法执行或验证的案例标记为受阻。没有正确性条件的执行记录为观察结果。
如果您要忠实复现已经确定的命令行测试案例,并保留结果证据,可以使用此技能。当输入值或执行顺序很重要、预期结果本身是错误,或案例包含写入操作时,也适合参考。它能帮助区分原先指定的条件与实际发生的情况。
入口技能必须提供保留原样的请求、经过验证的约定、预检记录和获准的影响范围。如果缺少这些信息,代理会返回入口流程,不会自行假设补齐。案例中获准的写入必须按指定内容执行,否则应报告为受阻。此技能会使用共享执行模块,以及用于证据整理和清理的相关技能。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。
不计入评分。 由 AI(OpenAI Codex)于 2026年10月26日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
Require the entry skill's preserved request, validated contracts, ready preflight receipt, and approved effect scope. If any is missing, return to the entry skill instead of filling gaps with assumptions.
Keep the caller's original case text. Turn supplied commands and interactions into the canonical actions in the contract. Keep test ordering and case IDs stable.
Each recording case covers one behavior, with at most one exercise invocation. Give it a very short behavior/outcome title, not an inventory of commands or flags. A supplied case may include multiple preparation or validation commands. If its exact steps conflict with this structure, ask before splitting, reordering, omitting, or adding anything. Explicit narrower coverage takes precedence over the default preparation/exercise/validation coverage.
If the caller specifies literal keys, preserve those keys. If they specify an option by name, a mechanical selector can locate it. Ask before inventing values or steps that materially affect the case. Do not add negative tests, extra metadata, new retries, or exploratory branches just because they seem useful.
Keep before/after conditions controlled: equivalent initial state, commands, geometry, data, and timing, with separately identified executables.
Use the shared run module. The exact-action gate must remain active for both live AI actions and generated controllers.
Execute each case's preparation, exercise, and validation in that order before starting the next case; approved cleanup can follow its validation. Do not batch phases across cases or later rearrange captured runs to suggest a different execution order. Preserve and clarify conflicting caller-supplied ordering.
Observe the real screen before actions. Condition-based waits can synchronize unspecified timing, but cannot replace or defeat a stated timing requirement. Never match an expected output merely because it appears in the typed command.
An unexpected prompt is not permission to reinterpret the test. Use only mechanical actions consistent with the supplied case. Otherwise pause, preserve evidence, and report what needs clarification.
If an authorized write is part of the case, execute it as specified or report blocked. Do not replace it with Cancel and claim that the case passed. Follow all applicable environment/tool policies.
Check every expectation against actual observations, with references to the appropriate state, output, file, or approved external readback. Do not rewrite the expectation to match the observed result.
Use failed for contradicted expectations and blocked for requirements that
could not be exercised or verified. An execution with no correctness assertion
is observed, not passed. A nonzero exit can pass an expected-error case;
exit zero cannot excuse a contradicted assertion.
Finish with evidence and cleanup.
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此技能识别 GitHub Agentic Workflows 相关请求的类型,并引导代理读取对应提示或技能文件,涵盖设计、创建、配置、更新、调试和升级。
Exercise a real command-line program with specified cases or a stated goal, then capture and verify the observed behavior. It produces local media evidence such as a session video, helping reviewers see what the command did without treating the recording as proof of general correctness.
中文简介正在准备中。当前显示英语简介。
将命令行练习的预期与记录证据逐项核对,按需渲染并检查媒体,返回各案例结果和本地成果文件路径;默认不发布证据。
此技能根据目标和已批准的范围选择可观察的命令行检查,逐项完成准备、操作和即时验证,并整理证据、尚未达到的行为及检查结果。
这是供 `cli-exercise` 内部使用的准备检查模块,用于核验构建、终端和媒体工具是否可用;缺少依赖时先征得安装同意,安装后重新检查。
通过共享的 Go 运行时和 Tuistory 适配器执行并记录 CLI 案例,按获批流程完成准备、操作、验证和清理,保留终端表现、执行顺序及失败时的部分证据。