Plugins

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.

Startup self-check that compares against the last healthy baseline and reports missing loader entries, client modules and plugins skipped by compatibility checks, especially useful after DSH upgrades.

DIY workflow builder for DSH - compose multi-step prompt workflows with self-check gating, engine-enforced jump rules, agent-driven execution and a live run panel.

Treat the session as a mainland-China user: consistent persona (ID region code, phone prefix, carrier ASN, locale, time zone), a landing checklist and a self-check, plus one command that packages it for a friend.

Turns archived sessions into durable memory instead of dead disk. Distils each archive into reusable skills, a fact ledger with ADD/UPDATE/NOOP decisions plus validUntil and supersededBy so stale facts retire instead of piling up, and topic clusters by single-linkage similarity. Also digests sessions that are still in progress through a read-only seq watermark, leaving the session log byte-for-byte untouched, and can self-check the growing memory to merge duplicate facts, retire stale ones and consolidate duplicate skills — measuring only unless you opt in, and never hard-deleting, only superseding.

Brings McDonald's China official MCP (mcp.mcd.cn) into DSH. Paste a token you generated yourself and the plugin handshakes, calls tools/list, and registers every remote tool as a native DSH tool (mcd_ prefix) — upstream additions and removals need no code change, just a restart. Ships four always-on tools (mcd_status, mcd_test, mcd_tools, mcd_call) for self-checks and direct fallback calls, plus a settings page with a one-click connection test. The token is stored only on this machine in ~/.dsh/dsh-mcd-mcp.json (mode 600); it never enters the repository or the conversation context.

Run Blender headless from DeepSeek Harness: execute bpy scripts, render scenes and export glTF assets, with modeling cookbook, 5.x pitfall list and asset-pipeline playbooks bundled as skills so the agent self-checks geometry before handing assets back.

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.

Installs the codex-cua skill so DeepSeek Harness can use Codex computer use: drives Chrome and native macOS apps through the runtime bundled with the ChatGPT/Codex desktop app, ships a persistent session client and a doctor self-check. Requires the ChatGPT/Codex desktop app with its Chrome extension, Python 3.9+, and macOS.

Thesis and empirical-analysis workbench that drives IBM SPSS from the conversation: spss_check (environment self-check), spss_variables (list .sav variables with labels, types, measurement levels, case counts), spss_data (sample case rows to inspect missing and outlying values), spss_run / spss_run_file (execute SPSS syntax or an .sps file, relative CD and GET FILE paths auto-rebased), and spss_analyze (one-shot frequencies, descriptives, crosstabs with chi-square, t-tests, one-way ANOVA, correlation, linear regression). Results come back as structured tables extracted through OMS. SPSS 25-31 is auto-detected; the bundled MCP server is zero-dependency stdio.

Inline image chips for the DeepSeek Harness composer — paste an image and it lands in the text as a numbered chip (image-1.png) with hover preview. Images still ship through the official attachment pipeline, names restart per message, the dock and sent-message thumbnails carry a sequence badge, and a pre-send self-check warns when the names in the text no longer match the images in that message.

A structured decision layer for the DeepSeek Harness agent loop: a danger-call gate, output self-check, tool narrowing, and loop guard backed by a pluggable adjudication model.

Routes eight DSH lifecycle events through dedup, quiet hours and digest batching into seven channels. Severity maps to the fields Bark, ntfy, Telegram and webhooks actually support; failed deliveries retry with jittered backoff and survive a restart; per-channel circuit breakers stop hammering a dead endpoint; and a self-check reports a degraded relay instead of going silent.

Beginner starter pack for DeepSeek Harness: welcome flow, 0→1 learning path, scenario-based plugin recommendations and a self-check checklist, paired with the dsh-handbook-zh Chinese tutorial repo.