Persistent multi-model workflow teams for DeepSeek Harness — dynamic lead planning, bounded DAGs, per-agent model/tools, Run Center and Token insights.

DeepSeek Harness 的桌面自动化：给整台 Windows 机器装上眼睛和手。可以做人能做的任何事，包括且不仅限于CAD，WPS，浏览器操作，发微信。安全，快速，可靠，聪明，人性化。

为 DeepSeek Harness 提供电脑控制插件：新鲜 Accessibility 观测、过期状态拒绝、作用域权限与安全输入（目前支持macos）｜Accessibility-first macOS Computer Use bundle for DSH with fresh observations, stale-state rejection, scoped permissions, and safe input.

Local MCP server that gives any AI agent safe desktop control on Windows, macOS and Linux. Reads the screen as text and structure (accessibility + OCR) and acts by element name, with screenshots as the last resort. Multi-monitor aware, verifies every action, one safety gate.

DSH × Blender 直连实时插件 v2.6.1：15 个模型工具让 AI 驱动 GUI Blender；支持 dsh-TUI（选中 chip / 页脚状态 / @ 提及 / /blender），活动状态置顶并可一键暂停 AI，原生选中/集合/子元素直送 DSH，随包建模 skill，支持 WSL / Windows / macOS。

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc

Train your AI coders the way you'd train a vine — discover your codebase's actual patterns, then encode them as Claude Code rules, skills, agents, hooks, and a guided pipeline so generated code lands inside your conventions on the first try, not the fifth.

Two main-stage panels for the DSH web GUI: a multi-column kanban board with real execution through DSH sessions, schedules, session rules and auto-cruise automation, plus a task-list workbench whose rows are themselves cards on one canvas, with a navigati

Use after a failure, user correction, repeated workflow problem, or validated success reveals a reusable lesson. Captures bounded redacted candidates, runs executable behavior evals, and separates validation from application in durable guidance.

Enables AI agents to prepare, launch, monitor, inspect, and recover existing Nextflow workflows through MCP tools, while client-side expert skills guide pipeline-specific input selection, parameters, QC, and troubleshooting.