Apache-2.0 general-purpose Skill that auto-routes local Skills and orchestrates harness workflows; works with DeepSeek Harness as a local Skill invoked via $vibe / /vibe.
DSH integration
Compatible
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
Apache-2.0
01What can it help you accomplish?
Break a complex multi-step task into planned work units, route local Skills to each part, and check the combined result
Approved requirement, L/XL level plan, per-Skill assignments with expected deliveries, and a final acceptance check across all planned items
Users of harness-style AI agents who have accumulated many local Skills and want complex tasks planned and delivered end to end
Auto-discover and select the best-fitting local Skills for each part of a task
A shortlist of candidate Skills (from 100+ installed) with each selected Skill tied to concrete work, expected delivery, and a check
Teams with a growing personal / team / third-party Skill library who no longer want to remember which Skill to invoke when
Resume and review long-running work from durable task records
Task records storing requirement, plan, execution progress, and final check in one place, so a later session can continue and a review can compare plan vs result
Users running multi-session tasks who need continuation and an auditable delivery review
02How to install into DeepSeek Harness
Not specified by the author
03DSH integration and capability boundaries
Installed as a general-purpose local Skill and invoked through any harness-style agent's Skills entry ($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
04Who is it for? When not to use it?
Good for
- 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
Not for
- 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.
05Compatibility, maintenance and safety notes
- 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)
06Frequently asked questions
How does VibeSkills work with DeepSeek Harness?
VibeSkills is a general-purpose Skill for any AI application that supports local Skills: install it as a local Skill, then invoke it through the application's Skills entry using `$vibe`, `/vibe`, or the syntax the application provides. The README documents no DeepSeek Harness-specific setup.
Is it native, an MCP server, or something else?
Neither — it is used as a local Skill invoked through the host application's Skills entry, not through an MCP protocol. It follows harness-framework patterns similar to Superpower and GSD.
What do I need before using it?
An AI application that supports local Skills, plus local Skill folders whose Skills have readable SKILL.md files. Additional Skill folders can be listed in `~/.vibeskills/skill-roots.json` or `<workspace>/.vibeskills/skill-roots.json`.
Will a large Skill library use a lot of tokens?
Discovery and index generation happen locally; only retained candidates are read as complete SKILL.md files, and execution uses only the Skills written into the plan. Token usage depends on retained candidates, document length, and task complexity — the overhead is bounded but not zero.
Where do task records and data stay?
The approved requirement, plan, execution progress, and final check are kept in the same task record (session_root, module-work-plan.json, module-execution.json, delivery-acceptance-report), and installation state is recorded separately in install-receipt.json.
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08Data and sources
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>…
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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