Plugins

Keep SSH, WSL and Docker projects in the native DSH sidebar. Group directories by environment while choosing full remote DSH or lightweight local Agent mode per project. Manage shared full-backend connections together, keep lightweight connections independent, and selectively sync local Skills with difference previews. Aimed at the Windows desktop client with Linux, WSL or Linux-container remotes that have Python 3 and Bash (macOS and Linux hosts untested); lightweight SSH needs an existing SSHFS mount, and Docker has not yet been run against a live daemon.

Scans a workspace for plaintext API keys, flags files inside git repositories, and reports findings masked so no full secret is ever printed.

Skill and plugin audit console for DeepSeek Harness. It scans local skill folders and installed DSH plugins, checks the packaging rules that commonly break installation, scores each asset, and exports a Markdown fix report.

Read-only Ascend NPU fleet monitor for DeepSeek Harness: environments are read from envs.json, npu-smi is collected locally or over SSH with one connection per environment, and the sidebar shows each environment's idle/partial/busy occupancy, running processes, per-card and per-chip details, five-minute background polling, manual refresh, and a field-by-field configuration modal. It never starts or stops services or containers and never writes to the remote host.

Runs project checks described in a dsh-checkup.json file (files, syntax, JSON, HTTP health, asset counts, and any node/npm scripts or commands you list) and falls back to auto-detected defaults when no profile exists.

Checks a DSH profile manifest for JSON errors and dependency/bundle/patch mismatches, reads recent startup crash logs (Windows DSH Desktop only), and can back up or roll back the manifest files.

Windows only: runs a build command through cmd.exe, collects the artifacts with sizes and SHA-256 prefixes, verifies expected outputs, and writes a report for each run.

AI-assisted research workbench with editable step workflows, on-demand skill binding, a live execution graph and in-project document review (needs a local Python with python-docx, python-pptx, openpyxl and PyMuPDF), while keeping existing projects on their original configurations. Requires DSH 0.2.2 or later.

Tongzhou: named local Codex–DSH discussions and read-only conversation views, with a DSH bundle and optional one-click Codex skill setup. Initially supports macOS with DSH Desktop 0.2.2-rc.1.

Packages a working style into a versioned pack of rules, skills and presets injected into the system prompt at global, project or session scope, distills local agent sessions into aggregate reports and suggested rules you can adopt or undo, exports the pack as a tarball a teammate can install, and ships a reference FDE pack with 40 skills.

Runs the agent through GEML workflows: work items move through flow steps, practices are recorded at the step they belong to, and a board tab in the right sidebar shows where each item stands. Also mounts the GEML MCP server and skills; when the project has a fixed workflow, a supervisor gates tool calls by its current state. Needs Node.js 22+; the GEML MCP server is fetched with npx (@geml/geml) when a session starts.

Registers the video-studio skill in the DSH skill catalog. It builds a Chinese-narrated video from a script: edge-tts synthesizes the narration, the timeline is derived from each line's measured audio duration rather than estimated from text length, Pillow renders every frame deterministically and pipes the raw frames to ffmpeg as rawvideo, burned-in subtitles and SRT/ASS export come from that same timeline, physical-model sound effects and a generated score are mixed sample by sample with numpy, and QA runs in four layers: container, signal, temporal and page level (the page layer only when the browser engine is used). An optional Playwright-driven browser engine renders the whole film as Canvas2D instead, sending PNG frames through image2pipe. Needs network access for the narration, ffmpeg on PATH and Python with numpy, Pillow and edge-tts; the browser engine also needs Playwright with a local Edge/Chromium.

Experiment-orchestration SOP skill for long-running experiments. A top-level model drafts a decomposition plan — subtasks, the model for each, an acceptance figure with tolerance, and dependency order — and dispatches only after the user confirms that plan; each subtask then goes to a lightweight executor subagent as a self-contained prompt, and its report is confined to measured facts and the delta from the agreed figure, with judgement and decisions staying at the top level. Ships one plan template and five task templates (gather, execute, consistency check, read-only review, mechanical transform), acceptance gates split into a task layer and an experiment layer, and three rules that stop internal-consistency evidence from being presented as independent or external validation.

Agent skills for the Lark/Feishu CLI: exports the 27 skills embedded in the lark-cli binary (docs, sheets, base, calendar, drive, task, wiki, approval, mail, slides, vc, minutes and more) and registers them with the DeepSeek Harness skill registry so the agent can read them on demand. Excludes lark-im so it complements an IM bridge plugin rather than duplicating one. Requires lark-cli to be installed.

DeepSeek Harness port of the Impeccable frontend-design skill: 24 design playbooks covering shaping, critique, audit, polish, layout, typography, motion, accessibility and design-system documentation, driven by the reference files shipped in the skill plus PRODUCT.md / DESIGN.md in the project instead of a downloaded engine binary. Registers the impeccable skill; reads project files and writes design artifacts under .impeccable/. The binary-only features (live browser bridge, pixel measurement, font matching) fall back to static variants, screenshots and explicitly declared manual estimates. Ported from pbakaus/impeccable (Apache-2.0).

Registers a frontend-design skill that asks for a design plan and token system before writing code, and ships Chinese typography rules plus a measured no-screenshot self-check. It provides one skill and no tools, makes no network requests and writes no files.

E-commerce order analysis skill for DSH: refund-type classification, return-rate statistics, interception and refund-abuse loss accounting, after-sales analysis with product warnings, delivered as a single Excel workbook with a per-order detail sheet and a sampling verification sheet (the agent builds it with Python: pandas, openpyxl, matplotlib).

Auto-detects whichever memory tool local Hermes uses, then reuses its skills and memory in DeepSeek Harness, with always-available recall. Needs python3 and a local Hermes installation; it only reads from Hermes.

Skill manager with content-addressed version history: snapshots, per-file and line-level diff, rollback, zip export and import, and per-skill enable or disable.

Provides a PMNote skill for finding public resources on game project management, careers, and AI tools.

Installs a TeleAgent skill-authoring standard as a DSH skill: the full spec, a validator that reproduces the platform checks (and fails on .pyc bytecode), a scaffolder, a packer and a TeleAgent installer.

Ships the Alibaba Java coding guidelines, 44 selected rules, as an embedded harness skill, so writing, changing and reviewing Java code or DDL follows the handbook with a numbered self-check before delivery.

Matches each new task against installed skills with deterministic keyword scoring and injects the best one without being asked; records effective operations as per-skill notes for later runs.

Runs loudness normalization, silence trimming, format conversion and batch processing through local ffmpeg, with presets for game sound effects, music uploads and voice cleanup.