Apache-2.0 通用型 Skill，自动路由本地 Skill 并编排 harness 工作流；作为本地 Skill 通过 $vibe / /vibe 在 DeepSeek Harness 中调用。
DSH 适配
兼容
作者声明
安全审计
未审计
最后核验
2026-08-21
许可证
Apache-2.0
01它能帮你完成什么？
Break a complex multi-step task into planned work units, route local Skills to each part, and check the combined result
Approved requirement, L/XL level plan, per-Skill assignments with expected deliveries, and a final acceptance check across all planned items
Users of harness-style AI agents who have accumulated many local Skills and want complex tasks planned and delivered end to end
Auto-discover and select the best-fitting local Skills for each part of a task
A shortlist of candidate Skills (from 100+ installed) with each selected Skill tied to concrete work, expected delivery, and a check
Teams with a growing personal / team / third-party Skill library who no longer want to remember which Skill to invoke when
Resume and review long-running work from durable task records
Task records storing requirement, plan, execution progress, and final check in one place, so a later session can continue and a review can compare plan vs result
Users running multi-session tasks who need continuation and an auditable delivery review
02如何接入 DeepSeek Harness？
作者未说明
03DSH 适配与能力边界
Installed as a general-purpose local Skill and invoked through any harness-style agent's Skills entry ($vibe / /vibe); the README documents no DeepSeek Harness-specific setup
Skill discovery & routing
configured local Skill folders (Skills install directory plus folders in skill-roots.json)→local index and shortlisted candidates; only Skills written into the plan are read and executed
Discovery and index generation happen locally and write index/record filesExtra Skill folders are configured via `~/.vibeskills/skill-roots.json` or `<workspace>/.vibeskills/skill-roots.json`Planning state machine (L / XL)
task scope, steps, dependencies, and parallel-work opportunities→a recommended L or XL level and a modular work breakdown; XL can run up to two non-conflicting parts at the same time
Delivery acceptance checks
actual results vs every planned item→a final acceptance decision; incomplete, failed, or blocked required work prevents acceptance
Durable task records
one task's requirement, plan, execution, and final check→session_root, module-work-plan.json, module-execution.json, and delivery-acceptance-report files enabling resume and review
Writes record files to disk (session_root, module-work-plan.json, module-execution.json, delivery-acceptance-report.json/.md)install-receipt.json records the files written by the installer so `check` can find missing or changed files
04适合谁？何时不该用？
适合
- Users of harness-style AI agents who have accumulated many local Skills and want complex tasks planned and delivered end to end
- Teams with a growing personal / team / third-party Skill library who no longer want to remember which Skill to invoke when
- Users running multi-session tasks who need continuation and an auditable delivery review
不适合
- Orchestration adds model-context overhead: more retained candidates, longer Skill documents, or a more finely divided task use more tokens; the cost is bounded by a local index, candidate shortlisting, and on-demand reading.
05兼容性、维护与安全提示
- The README describes VibeSkills as a Skill for any AI application that supports local Skills and mentions harness frameworks similar to Superpower and GSD, but documents no DeepSeek Harness (dsh)-specific installation or integration path.
- A local Skill needs a readable SKILL.md, a non-conflicting name, and a clear fit for the current work before it can be selected; Skills without these stay out of the plan.
- Orchestration adds model-context overhead: more retained candidates, longer Skill documents, or a more finely divided task use more tokens; the cost is bounded by a local index, candidate shortlisting, and on-demand reading.
Apache-2.0 · actively maintained (latest release v4.0.0, 2026-07-17)
06常见问题
VibeSkills 如何接入 DeepSeek Harness？
VibeSkills 是面向任何支持本地 Skill 的 AI 应用的通用 Skill：把它安装为本地 Skill 后，通过应用的 Skills 入口用 `$vibe`、`/vibe` 或应用提供的语法调用即可。README 未提供针对 DeepSeek Harness 的专门配置说明。
它是原生集成还是 MCP？
都不是——它是作为本地 Skill 通过宿主应用的 Skills 入口调用的，不走 MCP 协议。它遵循类似 Superpower 和 GSD 的 harness 框架模式。
使用前需要什么前置条件？
需要一个支持本地 Skill 的 AI 应用，以及包含可读 SKILL.md 文件的本地 Skill 目录。额外的 Skill 目录可以写在 `~/.vibeskills/skill-roots.json` 或 `<workspace>/.vibeskills/skill-roots.json` 中。
Skill 库很大时会消耗很多 token 吗？
发现和索引生成都在本地完成；只有被保留的候选 Skill 才会以完整 SKILL.md 形式读入，执行阶段只使用写入计划的 Skill。token 用量取决于候选数量、文档长度和任务复杂度——开销有上限，但不为零。
任务记录和数据保存在哪里？
已批准的需求、计划、执行进度和最终检查都保存在同一份任务记录中（session_root、module-work-plan.json、module-execution.json、delivery-acceptance-report），安装状态单独记录在 install-receipt.json 中。
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08数据与来源
VibeSkills is a general-purpose Skill that automatically routes local Skills and intelligently orchestrates harness work…
In any AI application that supports local Skills, invoke VibeSkills through the application's Skills entry, using <code>…
页面基于项目公开文档、仓库元数据和 DSH Plugins 的结构化解析生成；最后核验于 2026-08-21。发现错误？提交更正。
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