Plugins

Dual-brain agent presets for DSH: one model plans the framework and delegates execution to a subagent pinned to a second model. Ships brain-ds (DeepSeek-V4.1-Flash plans, MiMo-V2.6-Flash executes) and brain-mimo (the reverse), switchable per session. Needs two providers configured (Xiaomi MiMo and a DeepSeek account), and the main brain must be selected manually.

Agent preset for DeepSeek Harness: the standard tool stack plus four working rules written into the persona prompt — a project keeps at most four documents (intro, decisions, change log, current state and next steps), those documents describe only the current state with no AI edit history or superseded decisions, no git commit or push or pull request until the user explicitly asks, and a careful-coding workflow (restate the goal and done-criteria first, surgical diffs, evidence before claiming success) with plain-language reports.

Bidirectional dispatch for DSH: opens visible desktop sessions for tasks dispatched in by Grok Bot / Yobiru, adds /dispatch-grok and /dispatch-zcode slash commands to send tasks out, and reports the session result back to the dispatching peer on completion (peers are user-configured; nothing is sent until configured).

Deterministic ticket triage: a rule prefilter classifies clear cases and returns everything else as needs_review instead of guessing.

Expose a running DeepSeek Harness instance over MCP, so an external agent can list, create and continue that instance's own sessions, delegate a turn and collect its result, answer the questions the DSH agent asks, and set how future approval requests resolve.

Test work framework for DSH that scaffolds a product, module and version directory tree, registers seven test_framework_* tools and four runtime skills, and adds a prompt-level gate that reports which requirement-document splits, versions, related documents and environment credentials are still unrecorded before testing starts. Requires the playwright MCP server (no fallback).

Injects a very short conventions reminder (absolute INDEX path + version) once per session per version; when the conventions version changes, the next turn re-injects it (chat turns included). Optional hard guard: on operation-like turns, write/edit tools are rejected until the session has read the index (chat turns and shell are not blocked by default). Ships example checklists or points at your own directory; zero dependencies, configurable paths. Solves: conventions are written down but never pulled up when the work starts.

Coordinates PUA and Completion Guard tasks from /task-run or a CLI, requiring frozen acceptance and a current candidate/Goal certificate before completion, with an 8-round and 90-minute ceiling. Verified on macOS Desktop 0.2.0-rc.2. Since 0.3.7, PUA 0.3.27, Completion Guard 0.9.0 and SpecWorkflow 0.1.1 are bundled, so only Task Runner itself needs installing.

Keeps an unattended DeepSeek Harness session moving when nobody answers ask_user_question: it fronts the question waterfall, leaves a human answer untouched, and after a configurable window (120 s by default) submits the option marked (Recommended)/（推荐）, or the configured first/last fallback; plan reviews stay with the human unless explicitly enabled.

Windows-only: adds a switch to Settings → General that powers the computer off when the finished conversation ends (default delay 60 s, configurable 5–86400 s), behind a native countdown dialog that cancels the pending shutdown in one click (shutdown /a).

Read-only orchd panel for the DSH right sidebar: task board, ideas board, dependency graph, review timeline and an install entry for the project of the current session.

Arms a watch on a directory and delivers a batched, deduplicated diff when the tree goes quiet, filtering out the writes the session made itself; delivery is per watch, either waking a new turn or queueing for the next step.

Shows what each installed skill costs in the model context and hides a skill from the catalog without deleting it. Lists every skill found on disk with the exact catalog line the model receives (description normalized and truncated at the catalog cap), its body size and token estimate; previews the catalog message byte-for-byte as the agent will receive it, compares that with the copy already published in the current session, and reports skills on disk that never reach the catalog (invalid frontmatter, or shadowed by a higher-priority root). Hiding writes disable-model-invocation into frontmatter and keeps the file, so the skill still works via /name. Also imports skills from ClawHub, Skills.sh and GitHub URLs and uninstalls user-owned ones.

Investment research bundle: 10 tools (search, page fetch, source audit, claim verification, numeric rigor, channel health, workflow loader, chart engine, PDF renderer, PDF gate) and 23 skills adapted from ai-berkshire. Runs a six-phase workflow where four parallel agents (Duan Yongping, Buffett, Munger, Li Lu) research a company, cross-examine each other, and ship a chart-rich PDF with cover, tables, captions and page numbers. Requires Python 3.10+; the chart and PDF tools also need python-docx and matplotlib.

Skill-creation console for the Web UI: fetches the official creator skills, hands the requirements to the Huawei Cloud, generic, or REST API creator, then scores the generated skill against a 10-point rubric and returns the itemised deductions to the creator until it reaches 8.5/10.

Teaching-first CTF solving skill: narrates each step's pattern, tool choice and underlying mechanism, then writes a reproducible writeup under solves/<challenge>/ gated by a nine-section check.

Competitive-informatics coach plugin for DeepSeek Harness: coaching-rule prompts, learning roadmap, knowledge points, problem setting, real C++ judging with g++, and ima knowledge-base retrieval with four-notebook archiving, for GESP / CSP-J/S / NOIP / NOI teaching. Needs g++, and an ima account with ima-skill and the four notebooks configured; replies carry a WeChat-account promo and QR code by default.

Publish size check skill: verifies a deliverable's geometric specs against a target platform before publishing - pixel dimensions, aspect ratio, dpi, file size, format, physical print width and colour mode. Geometry only: it does not review content compliance (QR codes, text-to-image ratio, copyright and prohibited content are out of scope). Pure Python standard library, no third-party packages, and inputs are never modified. Python 3.8+. Covers 10 platforms and 20 slots with per-platform confidence labels - WeChat MP, Xiaohongshu, Bilibili, Zhihu, Weibo, Douyin, WeChat Channels, journal submission, print and client delivery - and resolves a platform by id, Chinese name or alias with fuzzy matching. Two trigger modes: asked directly, or as a parasitic self-check that runs by itself once an image, poster, chart or export workflow has written a file and the user mentioned a publishing target, reporting in one line when everything passes and only expanding when something is wrong. For journal submission and print it judges by physical width (pixels / dpi), which catches files whose dpi metadata passes but whose actual printed size is too small; for multi-image posts it checks whether the whole set is consistent and names the odd one out rather than listing every file.

Skill that reviews your coding-agent session transcripts (Claude Code and Codex only; DSH's own transcripts are not supported) over the last two weeks and reports which skills, hooks and rules work, need attention or go unused. Requires Python 3.

Engineering checklists distilled from Gillespie's Fundamentals of Vehicle Dynamics: 164 cards over the book's ten chapters, one skill entry with two-level routing. Each card gives the inputs a computation needs, the procedure, the boundary conditions where the method stops being valid, and a page reference — the explanation itself stays in the book. No passage of the book's prose is reproduced; about 77 defects in the source (misprints, OCR damage, internal contradictions) are documented in the cards. A study aid, not a substitute for the book.

OCR for scanned, image-only PDFs into Markdown, built around the fact that a book takes about an hour: every page is checkpointed so an interrupted run resumes, a progress log reports live status, and a quality report names empty pages and low-confidence lines. Requires Python 3.10+ and pip install -r requirements; the first run downloads the OCR model.

Turns PDF, Word, PowerPoint and Excel files into Markdown or plain text with heading levels preserved where the source allows it, and refuses scanned PDFs that have no text layer instead of writing an empty file, naming the OCR alternative instead. Requires Python 3.10+ and pip install -r requirements.

Bundles the kb-curator skill, which keeps a local three-layer knowledge base and refuses to index anything the user has not approved. The L2 retrieval layer needs @kenz1117/dsh-engram installed separately, and the engine runs on Windows only.

Doxygen comment workflow for C/C++ (including Qt) sources: file headers, functions, module groups, trailing and short comments, plus a scan that flags comments whose signature fingerprint went stale. Requires Python 3.10+ with `pip install tree-sitter tree-sitter-cpp`.