Separate capabilities clearly
Use the plugin-first architecture to reason about model access, tools, and workflow responsibilities as separate parts.
DeepSeek Harness
Use DeepSeek Harness as a reference for composing your own agent workflows. Study its plugin architecture to separate model access, tools, and task capabilities, then evaluate focused extensions for work organized in Floatboat.
Separate a workflow into replaceable plugins
Describe the collection, transformation, or external-tool gap in an existing task. Prepare a composition request that assigns plugin responsibilities and checks compatibility, with a small representative run before the proposed Harness workflow becomes a recurring dependency.
Describe inputs, processing stages, provider settings, and failure behavior.
Copy the request into a new Floatboat conversation and provide the required material.
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Reference: DeepSeek Harness https://github.com/deepseek-ai/deepseek-harness Read the original documentation and check the current environment and dependencies. Propose any new installation or tool connection before proceeding. My task: Refer to DeepSeek Harness to split my weekly public-news digest into collection, deduplication, and summary plugins. Define each input and output, provider configuration, and retry behavior. Check the current interfaces before drafting a prototype plan. Expected deliverable: A plugin responsibility map and a small validation plan for the first end-to-end run.
About DeepSeek Harness
DeepSeek Harness, also called dsh, is an open-source agent harness developed by DeepSeek. It provides a runtime around model interaction rather than a new model in itself. The project’s central design is to express the system through composable plugins.
Its value for workflow design is the separation of capabilities into composable parts. A Floatboat user can use that idea to identify the tools, context, and responsibilities a task requires before considering a specific plugin or runtime integration. The reference architecture does not establish a ready-made connection to Floatboat.
DeepSeek describes the project as a developer preview with compatibility-breaking changes expected. Its MIT source license is separate from model-provider access and third-party dependencies. Evaluate it as an evolving platform, with version-specific documentation and deliberate workspace boundaries.
Workflow Mechanisms to Learn From
DeepSeek Harness organizes work through distinct capabilities and their composition. Review the boundaries between plugins, the input/output chain of a focused workflow, and the version changes that may affect extensions before applying that structure to recurring work.
Compose a focused workflow
Evaluate only the capabilities needed by the task, rather than treating a large plugin catalog as a complete working solution.
Plan for extension changes
Treat version compatibility as part of workflow design so a runtime update does not silently change the expected behavior.
Use Cases for DeepSeek Harness
Digest pipelines, custom connectors, and small runtime experiments need explicit plugin responsibilities. Compare how DeepSeek Harness separates collection, transformation, and tool access, with compatibility and failure behavior checked before recurring work depends on a composition.
A modular information workflow
A news digest can be divided into collection, transformation, and review capabilities. Start with approved public sources, deduplication rules, and the digest's review requirements, then define what each plugin receives and returns. The resulting mapping makes responsibility visible when a source is repeated or a summary needs correction.
A custom tool connection
When a workflow lacks a particular external tool, inspect its API documentation alongside the current plugin contract. Define the allowed operations and reference the approved credential source without embedding secrets. A connector design should explain authentication boundaries and failure handling as clearly as the successful tool call.
A controlled workflow experiment
A preview-stage runtime should first be assessed with a small composition and representative inputs. Limit the capability set, define acceptable failure behavior, and record what actually happens. Use that experiment to decide whether recurring work has enough evidence for adoption or should remain on hold.
Assess an extension upgrade
A changed plugin version can affect more than its own installation step when other capabilities depend on its input and output. Compare the current contract and representative behavior with the proposed version before updating a composition. Record incompatible assumptions and the checks needed for dependent stages, keeping a preview runtime's change risk visible rather than treating the upgrade as a routine replacement.
Check DeepSeek Harness extension requirements
Different Harness extensions may require different credentials, versions, and runtime dependencies. Check the specific capabilities in the proposed composition rather than preparing a generic plugin environment and assuming that every connection shares the same execution conditions.
Check a candidate composition
Review capability boundaries, credentials, and version compatibility for the extensions you actually need.
Design a capability chain with DeepSeek Harness
A plugin composition should follow the task's real processing stages. Map those responsibilities, check extension compatibility, and inspect handoffs and failures in a representative run before expanding the chain or relying on it for repeated delivery.
List the task capabilities
Define which information, tools, and intermediate results each stage needs, independently of a particular plugin name.
Test the handoffs and failure path
Verify that a failed tool or changed plugin produces a visible result and a clear decision about the next step.
Related Software Projects
Compare the workflows, operating requirements, and source licenses of these projects.
DeepSeek Harness FAQ
Is DeepSeek Harness an official DeepSeek project?
Yes. It is published in the deepseek-ai organization, and its README explicitly identifies DeepSeek AI as the developer. Use that repository rather than assuming a similarly named community fork is official.
What does everything-is-a-plugin mean here?
It describes the project’s Cordis-based composition model. The runtime’s capabilities are assembled through plugins; the actual tools and behavior still depend on the plugins and configuration selected for a session.
Is dsh a separate AI model?
No. dsh is the runtime and application entry point. Model inference comes from the provider configured for the session, with that provider’s own credentials, availability, and commercial terms.
How stable is the project interface?
The official README marks it as a developer preview and explicitly expects compatibility-breaking changes. Pin and evaluate the version you use instead of assuming an extension will work unchanged across releases.
Does MIT cover connected models and services?
MIT applies to the project source. Model APIs, external services, and third-party components retain their own terms, costs, and operational requirements.
Must I switch products to use these workflow ideas?
No. You can first use DeepSeek Harness as a reference for task structure, tool boundaries, and result review in your existing workflow. Reusing software is a separate choice that requires compatibility, permissions, and license checks; applying the design ideas does not automatically connect the software to Floatboat.