skills Skill
description: 本项目将 MeterSphere REST API 与本地脚本能力整合,为 OpenClaw Agent 提供了一套高效、可复用的 Skills,支持自动生成功能用例、接口定义及接口用例,查询组织、项目、模块、用例评审与缺陷关联等信息,简化了测试资产管理流程,提升了团队的自动化效率。
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Why use this skill
skills is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around utility skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Generic Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in metersphere/metersphere-skills, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
---
name: metersphere
description: 本项目将 MeterSphere REST API 与本地脚本能力整合,为 OpenClaw Agent 提供了一套高效、可复用的 Skills,支持自动生成功能用例、接口定义及接口用例,查询组织、项目、模块、用例评审与缺陷关联等信息,简化了测试资产管理流程,提升了团队的自动化效率。
environment:
required:
- METERSPHERE_BASE_URL
- METERSPHERE_ACCESS_KEY
- METERSPHERE_SECRET_KEY
optional: []
security:
requiresSecrets: true
sensitiveEnvironment: true
externalNetworkAccess: true
notes: 此技能需要访问 MeterSphere API，使用 ACCESS_KEY 和 SECRET_KEY 进行身份验证。请确保只向可信的 METERSPHERE_BASE_URL 发送请求。
---
# MeterSphere Skills
优先用本 skill 自带脚本,不要临时手写 curl。
## 选择工作流
按任务类型选最短路径:
### 1. 查询类
用于:
- 查组织 / 项目 / 模块 / 模板
- 查功能用例 / 接口定义 / 接口用例
- 查评审单 / 评审详情 / 评审人 / 评审模块
- 回答"哪些用例被评审过"
- 回答"这条用例关联了多少个缺陷 / 哪些用例缺陷最多"
- 当用户查询某个功能用例时,返回:用例详情 + 缺陷 + 评审记录
优先命令:
```bash
./scripts/ms.sh organization list
./scripts/ms.sh project list
./scripts/ms.sh functional-module list <projectId>
./scripts/ms.sh functional-template list <projectId>
./scripts/ms.sh api-module list <projectId>
./scripts/ms.sh functional-case list '<JSON>'
./scripts/ms.sh api list '<JSON>'
./scripts/ms.sh api-case list '<JSON>'
./scripts/ms.sh functional-case-review list '{"caseId":"<功能用例ID>"}'
./scripts/ms.sh case-review list '{"projectId":"<项目ID>"}'
./scripts/ms.sh case-review get <reviewId>
./scripts/ms.sh case-review-detail list '{"projectId":"<项目ID>","reviewId":"<评审ID>","viewStatusFlag":false}'
./scripts/ms.sh case-review-module list <projectId>
./scripts/ms.sh case-review-user list <projectId>
./scripts/ms.sh reviewed-summary <projectId> [keyword]
./scripts/ms.sh case-report <projectId> <caseId>
./scripts/ms.sh case-rep
...