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
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。
Exercises real command-line programs from explicit cases or a goal, with faithful terminal recordings and observed results. Use when asked to record CLI test cases, demonstrate command behavior, validate an interactive workflow, or explore a command against a goal; not for ordinary unit-test execution or skill authoring.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Exercise the caller's selected executable and return local evidence. Accept precise cases, a broad goal, or a mixture. Preserve explicit requirements while letting the agent choose useful investigations where the caller left room.
By default, produce one chaptered session video, even for one case. Each case covers one behavior and has a very short natural-language behavior/outcome title. Actually execute and record that case's preparation, one exercise invocation, then validation before starting the next case. Approved cleanup can follow validation. Never batch all setups or validations or reorder historical clips to imply that execution order. Exact supplied steps and narrower coverage remain authoritative: ask about conflicts rather than silently adding, splitting, or reordering a caller's case.
Deliver media only by default. Infer clear output intent: a video exercising a
PR calls for an MP4, while a request for an interactive review report calls for
media plus HTML. Ask only when the requested deliverable is ambiguous. Generate
HTML with --html only when wanted; do not create or open it for video-only
requests. Keep raw captures and JSON results as internal evidence.
The internal SKILL.md files below are bundled instruction modules. Read the
needed module from this package and follow it in the current agent context.
They do not require new agents, sessions, or separately installed sibling skills.
This entry file is the routing overview. Load the selected flow, not every internal module. Execute the Go helper as a tool; read its source only when diagnosing a problem or reviewing the implementation.
Go owns prerequisite checks, execution policy, controllers, capture, verification, and rendering. Node is only a mechanical adapter to Tuistory's PTY API. The skill does not require another scripting-language environment.
| Need | Resource to read |
|---|---|
| Check readiness or request installation | Preflight flow |
| Preserve supplied cases | Exact-case flow |
| Decide how to pursue a goal | Exploration flow |
| Start or drive the terminal | Run flow |
| Verify, render, or clean up | Evidence flow |
| Normalize the caller's cases | Run-contract reference |
| Call the bundled helpers | Runtime interface |
| Coordinate cases and assemble one video | Recording sessions |
| Author a deterministic controller | Controller reference |
| Resolve missing prerequisites | Prerequisite reference |
| Inspect representative routing behavior | Input examples |
| Maintain or evaluate the package | Maintenance reference |
| Run an authoring evaluation | Agent-behavior rubrics |
These direct links keep resources one hop from the entry point. For an exact request, do not load the exploration flow unless the caller also supplied an exploratory case. Do not load controller details for a live-only run.
Classify each case, not just the entire request:
Input fidelity is independent of execution strategy. Either policy can use a bounded controller, live observation/action calls, or both. Switching strategy never grants permission to change an exact case.
Resolve only missing details that affect correctness or authority:
Default to condensed presentation, with static pauses shortened. Honor explicit real-time requests and retain the original capture timestamps. Keep timing adjustments in the recording metadata, not repeated on-screen labels.
A request such as "upload an attachment" is not permission to choose an arbitrary account or publish to the current repository. Ask for the missing target/authorization before performing a write. Prefer synthetic data and isolated configuration.
Command discovery is a separate case property. A case may provide the command and test its use, or explicitly require finding it. Do not silently turn a known-command case into a discoverability test.
Preserve the non-secret original request alongside normalized cases. Never copy credentials into the contract or recording; use approved secret references or report blocked if the requested literal recording would disclose them. Use the run contract; unsupported or ambiguous requirements must be clarified or marked blocked, not discarded.
Read the preflight module. It checks dependencies. If setup is needed, propose commands using the pinned requirements, obtain caller approval, and recheck afterward.
Installation approval and permission to modify test resources are independent. Record the executable path, version/revision, checksum, accepted case contract, and approved effect scope before starting the subject.
Use a secure private workspace outside the repository. Do not put recordings, credentials, runtime caches, or operator configuration in this skill directory. No secret values belong in the contract or a tool request.
Both use the shared run module.
It provides real terminal observation, input, controller execution, recording,
and bounded process ownership. Read only the internal modules needed.
Use a recording session to keep case and phase
order, carry approved state between related commands, and assemble their actual
recordings with evidence --session, not the per-invocation --run-dir path.
Keep validation commands in that recording. The session overview uses the
task-specific title, derived fidelity, and case results, with 10 seconds per
readable page. Progress spans the entire final video.
Always finish through the evidence module, including when the case is blocked or the subject fails.
Return the case outcome, meaningful observed behavior, requested media paths, executable identity, inspection coverage, and limitations. Keep case failure, capture failure, and render failure distinct. A video is evidence of an observed flow, not proof of general correctness or a replacement for tests.
Include a report link only when the caller requested the report. Do not leave a host, terminal, recorder, or playback server running. Publishing the evidence is a separate, explicit action.
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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。
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。
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。