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dsh-plugin-workflow-laoboshi

Workflow Updated 2026.08.16

Run the following command in DeepSeek Harness:

dsh plugin install Modole/dsh-plugin-workflow-laoboshi

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install Modole/dsh-plugin-workflow-laoboshi in your terminal; the source is available at https://github.com/Modole/dsh-plugin-workflow-laoboshi. After installation, restart dsh web to activate the workflow plugin in your Agent.

About this plugin

Building multi-step DAG workflows inside a DSH Agent setup often means stitching nodes together by hand, debugging in isolation, and manually promoting changes to production. Laoboshi DSH Plugin closes that gap by bringing a full create-test-publish-execute lifecycle directly into the DSH plugin ecosystem, so developers no longer have to juggle external orchestration tools alongside their Agent.

The plugin supports every stage of a DAG workflow: authoring new flow graphs, testing individual nodes and branch paths, publishing a validated version, and executing the released workflow in a live environment. After installation, restarting dsh web lets the system read the dsh.bundle manifest and inject the plugin into the active profile automatically, making the workflow features immediately available in the Agent interface.

Extracted from the built-in capabilities of Laoboshi Agent Studio, this plugin is well suited for developers and small teams already working on multi-agent pipelines in DSH who want to decompose complex tasks into visual DAGs and iterate on them efficiently. If your workflows involve conditional branches, parallel nodes, or sequential dependencies, this plugin can meaningfully reduce the overhead of building and maintaining them.

Use Cases

  • Author multi-step flows with conditional branches and parallel nodes inside DSH Agent
  • Publish a tested workflow version as a reusable production-ready process
  • Visually compose, debug, and rapidly iterate on multi-agent collaboration tasks

Best For

  • Developers already building multi-agent pipelines on the DSH platform
  • Small teams that decompose complex tasks into visual DAGs and need efficient iteration
  • Engineers handling sequential or parallel node dependencies within DSH Agent