Plugins

Optimize prompts in the DeepSeek Harness composer: identify missing requirements, insert a sentence from 90 method cards, or compare and edit a full rewrite before you send.

Engineering-discipline guard: requirements grill before the first edit, red/green test-evidence gates, forked adversary review, and a delivery report with a per-dimension verification workflow.

One-click prompt enhancement in the composer — rewrites the draft into a structured prompt (role and goal, executable steps, output format, acceptance criteria, edge cases) with before/after preview, fill-back, and undo. Never changes intent, never fabricates requirements.

QA project workbench for dsh: manage test projects, requirements, test cases, defects, milestones, reports and approval gates through a browser UI and 18 QA-domain tools.

Requirements interview before work starts: judges whether a request is clear, asks focused questions when it is not, in strict mode denies editing tools until the user confirms a four-part summary in chat, and keeps confirmed preferences in two-layer memory.

Workflow and automation (`workflow`): task dispatch, optional Agent Teams, and tiered verification; usage and billing (`usage`): session token tracking and automatic context folding (no bill calculation); sessions and messages (`session`): persistent fold state, protected recent messages, and watermark-triggered handoff; models and account access (`model`): six dispatch pools and optional intent-based routing (no account integration); skills (`skill`): read-only MySQL/Redis checks, commit-message generation, and requirements/design docs; development and runtime (`dev`): Host/Client settings UI and tool-call safeguards. Together, these features are designed to balance output quality with token efficiency and model-use costs.

Terminal coding agent for DeepSeek Harness with a full-screen alternate-screen UI, turn-aware session navigation, and a gated /ship workflow that takes one-line requirements through specification, TDD, and rerun acceptance checks.

Runtime requirement drift guard that keeps long-running DSH agents aligned with the user's approved goal, constraints, and decisions.

Engineering workflow agent preset for dsh: five gated phases (requirements clarification, plan approval, TDD, parallel subagents, verified finishing) with six workflow skills adapted from obra/superpowers.

Data lineage tracking tool, mapping the full lifecycle of data to meet source legality requirements.

Bilibili live-room danmaku for DSH: read live chat, send messages only after explicit confirm, hang out in a room in the background, optionally let the model decide whether to reply, and optionally transcribe stream audio to text; no video understanding. Setup needs a Python 3 venv with the bundled requirements plus Bilibili cookies; transcription additionally needs ffmpeg and a Whisper engine.

Adds a "Task Planning" agent preset that asks requirements in versioned rounds, then writes step-by-step plans and prompts without implementing them itself.

Math Agent Framework for DeepSeek Harness: symbolic derivation engine (CES / quadratic forms / ODE / PDE), SymPy verification, Lean 4 compiler-verified formal proofs, and QED-style multi-agent adversarial verification, exposed as native DSH tools through a TS shell bridging a Python engine. Requires cloning the math-agent-framework repo and `pip install -r requirements.txt`, with MATH_AGENT_HOME pointing at the repo root. Every result carries provenance (engine versions, seed, tolerances) and is marked untrusted data.

Breeze TTS client for DeepSeek Harness: connects to your own Breeze TTS server to read replies aloud and manage voice cloning (the server and its model must be deployed separately; hardware requirements are on the server side), with an optional cross-machine mode. Merges @dsh-external/dsh-plugin-local-ai-tts (abbccdd/dsh-localtts) and dsh-breeze-voices, carries code from 1624318455/dsh-plugin-tts, and must not be installed alongside the two upstream plugins it merges.

Injects durable user-set must-do and must-not requirements into the model at conversation start.

Turns one journal paper PDF into three deliverables by default — a Chinese group-meeting literature report deck (.pptx) in the Nanyang Normal University physics template, a matching Word speaker script, and a two-colour defence-prediction Q&A HTML (ten-odd questions with short direct answers). Requires Python 3 with python-pptx, python-docx, Pillow and pymupdf (pypdf fallback); install them with pip install -r requirements.txt.

A DeepSeek Harness (DSH) plugin that lets you control Universal Robots (UR) collaborative arms directly from natural language — by default an offline, kinematics-level simulated arm, with a real-robot channel that stays off until it is explicitly enabled. Needs a Python install with numpy and paramiko (pip install -r requirements.txt). Developed by Suzhou Nonead Robot Technology Co., Ltd. based on the same logic as the company's own nUR MCP Server. (Suzhou Nonead Robot Technology Co., Ltd.)

Model-linked characters and a shared world for DSH, with separate editable personas and memories, optional AI diaries and sleep consolidation, Chinese/English controls, task-aware desktop pets, themes and usage statistics; updates preserve user customizations. Supports Windows, Linux and macOS; see the guide for each system's installation requirements. Diaries and consolidation consume tokens. Before installing or updating, we recommend closing visible desktop pets and bubbles; open files may cause installation to fail. Codex and OpenAI API account queries have been user-validated; other sources await user validation and feedback.

Context curation for DeepSeek Harness: content-aware tool-output slimming, dead-log sweep, pinned user requirements and in-flight work, archive-then-compact with an audit trail.

DSH Agentrix supports coordinator-led direct or delegated work, configured worker models, dispatch-time coordinator models for independent review, progress and request usage tracking, registered requirements linked to verification evidence, and a read-only acceptance view that distinguishes plan completion from request coverage. Verified only on Windows with PowerShell 7 (pwsh) and Git; other systems are untested.

Turns one journal paper PDF into a Chinese group-meeting literature report deck (.pptx) in the Nanyang Normal University physics template, plus a matching Word speaker script. Requires Python 3 with python-pptx, python-docx, Pillow and pymupdf (pypdf fallback); install them with pip install -r requirements.txt.

This project aims to bridge the Blender MCP Add-on, so that models on DSH can conveniently connect to the current user's working Blender to assist creation, while meeting basic security requirements.

A context usage ring with configurable character-based automatic compaction, manual compaction, protected user requirements and recoverable original history for DSH 0.2.0-rc.2.

Keeps DSH agents from forgetting your requirements during long tasks. It saves important conditions and completion evidence locally, brings them back after context compaction or session resume, and stops partial work from being reported as the whole task done.