Plugins

Agent-structured prompt optimizer for DeepSeek Harness: rewrites composer drafts into role/task/context/constraint/format with normal, slim, and expand modes, a confirm modal with score, continue-optimize, and local, hybrid, DSH, or cloud-API engines.

Multi-model orchestrator: the main brain decomposes complex tasks and dispatches subtasks to GLM / Kimi / Qwen sub-agents by each model's strengths, with per-model token usage.

Adds 20 locales (Traditional Chinese HK/TW, Japanese, Korean, French, German, Spanish, Portuguese, Italian, Russian, Ukrainian, Polish, Dutch, Turkish, Arabic, Hindi, Indonesian, Vietnamese, Thai, Swedish) to the DeepSeek Harness Web UI, with hand-polished translations, runtime fallbacks, and automatic translation of market and third-party text.

Dual knowledge-graph RAG for Markdown notes with half-life memory decay: prerequisite + similarity graphs (P/S-Agent traversal), paragraph chunking, n-gram/embedding hybrid search, and an explicit weight-feedback loop — a native Cordis plugin, npm-installable as @eqman00003/knowlp-rag.

Two gates for outbound AI text. (1) Guard: before any text goes out it blocks the shapes anyone could write — padding verbs plus abstract nouns ("optimised the code structure"), hollow adjectives, universal filler, template openers, git commit/PR/review boilerplate, comments that narrate the code. Covers chat, comments, email, commit messages, PR descriptions, reviews, issues, docs and code comments. Passes with OK, blocks with one line naming what to change. (2) Catch memes: when someone drops a slang term the model does not guess or look it up — it already knows. A built-in 6792-entry meme wiki is queried before the model starts thinking, and the entry usage field ("how to answer this") goes straight into context. Matches only exact title/slug/alias hits, never fuzzy, so ordinary chat never triggers it. Bundled SQLite by node:sqlite, zero dependencies, 1.6ms per query. Settings page: guard on/off, strict mode, prompt section toggle, meme wiki toggle, per-turn auto-lookup, a four-step tone slider (reserved / measured / follow the room / unrestrained) that is behavioural rather than a label, AI-completion switch (off by default, uses the session own model so no API key needed), a paste-and-test box, and three thresholds. No model call for any check.

A simulated human-brain core for chat: global workspace, emotional appraisal, dual-system memory with nightly consolidation, and an experimental sensory interface; the QQ bridge needs a local snowluma client and is off by default.

A 3D neural memory map panel for the meow-memory store inside DeepSeek Harness. Sidebar entry + desktop embed, with 8-region memory classification and retrieval. Requires the meow-memory plugin (it reads .dsh-meow/memory.db) and Node 22.5+ (node:sqlite).

Superpowers skills for the DeepSeek Harness (dsh): a full engineering methodology served through dsh's own filesystem skill provider, with an always-on bootstrap.

Local-first long-term memory for DeepSeek Harness: text packages, on-demand recall, raw event citation, multiple brains and workspace boundaries.

Real-time disaster alerts for DeepSeek Harness: Japan earthquakes (P2PQuake) and JMA EEW, tsunami and weather telegrams, global earthquakes (EMSC/USGS) and NOAA tsunamis, mainland China earthquakes (CENC via Wolfx) plus nmc.cn rainstorm and geological-hazard warnings, and US/Canadian NWS and ECCC flood, rainfall and storm-surge warnings — matched against your watch regions and thresholds.

English edition of Weiwen's Law (KISS's Law): the first and only domain-agnostic white-box causal adjudication middleware for AI agents. Unlike domain-specific causal-inference tooling (statistics/economics/ML) or evidence-gated agent firewalls, it adjudicates *every* tool call along a deterministic structural causal chain (R->S->D->H->M) before execution - across any domain, with no training or per-domain tuning. It blocks destructive and credential-file operations, escalates repeated boundary violations, cuts faulting links until verified-fixed, and gives risk verdicts on un-auditable execution; runs fully local with zero API cost.

Codex-style per-session memory draft for DeepSeek Harness: one session one draft (rollout_summaries), restrained & passive (write only on explicit user request), layered disclosure (summary + registry), idempotent integration watermark. / 一会话一草稿记忆插件。

L-clone 记忆钩子：DSH 每轮结束(turn/end)自动 capture 到外置大脑；Web GUI 侧边栏「大脑看板」按钮一键打开记忆工作台。

人脑式上下文压缩逻辑，大幅减少长任务上下文占用以及token消耗

Matrix-style CLI chat page for the DSH web UI: the agent's thinking streams down the screen as green code rain while replies print normally in the terminal.

Open the current workspace in your local editor (VS Code, Cursor, JetBrains, Vim, ...) from the session header.

Cyber whale pet for DeepSeek Harness: a floating, feedable, color-customizable whale over shell.overlay that reports token usage, quota, interactions, and session counts — one dsh-plugin bundle carrying the host chat brain and the browser surface

Expose DeepSeek Harness agent capabilities as an MCP server, so an external MCP client (e.g. Hermes) can drive Harness to execute coding tasks. Hermes = brain, Harness = arms.

Immersive weather theme for DeepSeek Harness: physical rain with splashes and ripples, snow in two crystal habits, a hearth, lightning with distance-coded thunder, a night mode, and a compact control panel.

A second brain for DeepSeek Harness where Markdown is the database and git is the sync, with zero runtime dependencies. Core memory is injected into every system prompt, one file per note with weighted search; cards produced in the sidebar web chat land in an inbox first and are approved one by one as cloud notes or local-only notes (private/ is permanently gitignored and never pushed); web-chat progress is saved to progress.md with the last 20 drafts archived. A four-step flow lives behind one header icon: open web chat, extract cards, review them, back up to the cloud. The extraction prompt is editable in the panel and applies immediately.

Context handoff sentinel for DeepSeek Harness: three behavioral recall tests (constraints / changed files / next step) run after every compaction or on /guard check, cross-checked against the session log, returning a green/yellow/red verdict plus an optional handoff document.

Lingshu (灵枢) is a brain-inspired persistent memory, persona and growth system for AI coding agents: pure Python standard library, zero dependencies, local-first, requires Python 3.9+. Hippocampus/cortex layered memory with forgetting curves, dream consolidation, fuzzy associative recall, emotion system, persona drift protection, knowledge base, self-authored skills, and a local web panel. Ships as a DSH bundled skill; the separately installable cordis startup-injector plugin auto-injects sessions only when Lingshu is installed at ~/.dsh/skills/lingshu (or brainPy is configured) and Python 3 is available; the optional update check contacts GitHub/Gitee.

Four-step WHOOP account connection (you create your own WHOOP developer app and paste its Client ID/Secret) with automatic sync on dashboard visits, plus recovery, strain, sleep and workout charts inside DeepSeek Harness.

Open file references from the DeepSeek Harness chat output in your IDE (VS Code family or JetBrains, Visual Studio Code by default), positioned at the referenced line, in its project window, brought to the foreground, and self-updating from GitHub.