47 RESULTS
v0.5.0-alpha3 · MIT
A toolkit for low-level operations in DSH, designed to reduce token usage and task execution time / DSH 底层操作工具插件,旨在减少 Token 消耗与任务耗时
v0.3.3 · MIT
dsh-routing-suite — injector + router-standard kit: install the runtime injector first, then the task-aware reasoning-mode router preset (measured P1-P23).
v0.2.8 · MIT
Time-shifting task delegation for DeepSeek Harness (dsh): plan tasks at leisure, they run unattended off-peak, come back to a report. Human-adjudicated, desktop + web.
v3.0.3 · MIT
:rocket: The Ultimate Image Uploader for Efficient Creators. Supports Obsidian, Typora, VS Code etc. and 60+ image hosting services (S3, GitHub, Cloudflare R2, Imgur, Aliyun OSS...). Paste, upload, done.
v0.4.39 · MIT
💡DSH 的 Prompt提示词工具箱:常用prompt保存后点击即可注入到对话框——省时省力,/prompt 自动提示已有Prompt。 💡The Prompt toolbox for DSH: save frequently-used prompts and inject them into the dialog with one click — saving time and effort, with /prompt auto-suggesting your existing prompts.
v0.1.10 · Apache-2.0
Native local Taskboard plugin for DeepSeek Harness. SQLite-backed projects, Agent claim/review, and a native Web UI — no iframe, no second chat runtime.
— · Apache-2.0
An open-source Office workspace where people and AI agents create, collaborate, and review together.
v0.33.0 · MIT
把 TiddlyWiki 5 变成你和 Agent 共用的本地文档中心(纯文本 + git):你在 DSH 界面里阅读、写作、记日记,Agent 用 16 个 tiddlywiki_* 工具读写同一批笔记;每条笔记都是你能读、能改、能审计、能随时带走的文件——不是自动记忆插件,不做后台抓取。可选一键发布到微信公众号草稿箱。 Turn TiddlyWiki 5 into a document center you and your agent share, in plain text under git —
v0.1.6-rc.4 · MIT
Know when your DeepSeek Harness tasks need you. Local alerts, quiet notifications, and a task inbox.
v0.15.2 · MIT
Task-specific React apps for DeepSeek Harness with state carried into the next Agent turn
v0.9.7 · Apache-2.0
deepseekharness 任务看板插件
v0.4.1 · MIT
让 Agent 的工作方式可组合、可审查、可持续改进,最终实现 Agent Self Evoling。 DeepSeek Harness Web plugin with composable task controls and isolated, human-reviewed self-evolution.
v2.2.4 · MIT
Extends the working range of DSH into WSL. Files in the distribution can be read, written, edited and searched by DSH directly. Depending on the task, the AI chooses for itself: run a one-off command, or open its own terminal and run several commands ther
v1.0.0-alpha.1 · MIT
Zoom, pan, download, gallery, and region-note image viewer for DeepSeek Harness
v0.2.2 · MIT
Branch from any answer and keep branching at any depth. Recursively isolated follow-ups that leave the main DSH task untouched.
v1.2.16 · MIT
Display-layer translation for the DSH web app: thinking chain, task cards, and optional answer text, in 9 target languages (local Ollama / any OpenAI-compatible or Anthropic endpoint / Google / Bing, fail-open)
v0.6.0 · MIT
左下角便签:随手记点子/感想/TODO,实时保存到归档目录,清单+悬浮归档
v0.24.0 · MIT
Context retrieval for DeepSeek Harness: ranks docs, code and media by relevance into a sidebar Context Pack. Lexical and fully local by default. Hybrid is off until you configure a provider; it sends your query and file titles/summaries, not bodies.
v0.2.19 · Apache-2.0
Local-model (Ollama) integration for DeepSeek Harness: discover, pull, remove, and inspect local models, route requests to them by task type or keyword with automatic fallback to the cloud, and get a one-shot status overview via /ollama.
v1.0.0 · MIT
Model router for DeepSeek Harness: keep a pool of models and every step is routed to one of them with its reasoning effort set — a click-only composer panel, three authorization scopes (a manual pick only counts in the scope that owns it, and effort scope
v1.9.1 · MIT
DeepSeek Harness 智能看板:任务卡片 + 一次性 Worker/Verifier 派发 + 依赖调度 + 管线分档 + Team 模式(歧义上报主窗口裁决)
v0.9.8 · MIT
DeepSeek Harness (dsh) plugin: prompt your home servers into existence through home-hosted — Gitea, Jellyfin, anything — and manage the panel, the entries and boot autostart from dsh.
v0.6.31 · MIT
Role-based AI swarm orchestration for DeepSeek Harness: duty-table model pinning per role, parallel task DAG with review loops, and a live Swarm dashboard tab in the web GUI.
v0.2.32 · MIT
DSH web plugin: a LiveBench tab in the Trajectory view (right of 对话/轨迹). Run LiveBench evaluations against every model configured in the DeepSeek Harness — pick provider/model, category, task, release and question range from dropdowns, watch progress, and