Plugins

Web mascot that acts out each live tool call (read, write, edit, search, run, plan, sub-agent, wait, error) with matching step cards in chat; ships two swappable character packs.

Bridges a OneBot QQ bot into DSH: the owner's direct messages open a session on the same home, answers go back to QQ, and files named with \[SEND_FILE: path] are delivered. Needs a OneBot v11 implementation with a forward WebSocket (such as NapCat) and ownerId set in the config, otherwise every message is ignored. The home it shares with the main session is provided by the separately installed dsh-wakeup plugin. It also runs an unauthenticated local status endpoint on 127.0.0.1:3002 with open CORS that returns the owner QQ number, the login QR code and recent log lines (including the first 60 characters of incoming messages).

A work assistant for WeChat: it records what you tell it into a SQLite task ledger, ranks items by urgency, reminds you on schedule, extracts people and their relations from your notes, and hands tasks to separate assistant sessions. Ships its own WeChat bot channel (scan to log in; private chat only) and a full-page dashboard.

Sensitive-character replacement for DeepSeek Harness: matches messages and tool results against a YAML blocklist you maintain (global, per provider id, or per model) before the model reads them, and scrubs the outgoing request again before dispatch, so blocked characters cannot trip the official API Content Exists Risk 400.

Cross-session memory for DeepSeek Harness: a three-tier store (behavior notes, deep details, session archive) plus a 600-character core card injected every step, work/companion/learning state lines, multi-person and multi-group records with visibility partitions, session-scoped suppression or deletion of memory fragments, summaries and fact candidates that can route to a separate model, and backup plus self-check tools. Data is plain text under the home directory, and entries can carry images and audio stored by content hash.

Forced collaboration: a composer picker sets 0–6 sub-agents, and while it is on every hands-on task must be split across exactly that many child agents through a collab_fanout tool that has no count parameter, then merged by the main agent.

Hybrid model router for DeepSeek Harness: declarative rules, user-configurable cost control (cost-first / quality-first / balanced), optional LLM task classifier, and failure fallback chains.

Adds a draggable lucky-block button to the DeepSeek Harness web UI. Single-click plays zero-cost little performances (popups, a fake blue screen, daily fortune, trivia, cold jokes, music, and a set of visual stunts); a 1.4-second long-press mines the block open and starts a new session to write a random paper, read the news, invent something, or spin a short story. It also appends a "lucky block" choice to the model's multiple-choice questions and rolls a random pick, reversible by saying "rollback". Block appearance and event weights are adjustable in settings.

Journey to the West floating desktop pet: a frameless, transparent, always-on-top cartoon widget of Tang Monk and his disciples. Click to switch characters; idle plays a chasing scene.

Agentic roleplay for DSH with structured worlds, autonomous characters, persistent sessions, world authoring, speech, and image generation. Requires Firefox2100/dsh-agentic-roleplay#state to be installed and enabled.

Pre-publish credential audit: walks a directory tree and flags API keys, tokens, private keys, database connection strings and hard-coded passwords before you commit, publish or ship. Zero dependencies, fully offline, findings are redacted to the first six characters.

Checks installed DSH plugins for version-declaration and API risks against the current host, with source locations when available, certainty, impact basis and coverage limits. Adds a compatibility settings panel, plugin-detail findings/badges, a compat_check agent tool and a skill provider.

Move an entire dsh installation to another machine and check it item by item. Three layers go across: the declaration layer (settings, credentials, plugin declarations and vendored link: sources), the state layer (sessions, storages, skills, attachments) and, optionally, the workspace project code. Sessions are rewritten at the zstd header frame only, workspace paths are remapped through an explicit path mapping, and at the end 14 checks (I1–I13 plus I6b) report PASS / FAIL / WARN. A LAN route pairs two machines and pulls the bundle over directly; merge mode diffs a bundle against a machine that has already been in use and stops before writing anything while conflicts are undecided.

Quiet break, water, long-session and late-night reminders delivered by three bundled chibi companions, with cloud bubbles and a light bounce. Works in-page without extra downloads; the optional macOS desktop window uses a separately downloaded Electron helper.

By default, blocks marking a team task complete without a passed independent verification record whose commit matches the worktree HEAD, and adds a dependency DAG board and a settings page.

In-conversation workflow run timeline for DSH Web: a horizontal rail with phase stations, agent pills, loop arcs and parallel bands replaces the stock workflow tool card and workflow-run chat node. Planned stations render before the run starts via client-side script scanning; status dots and icons follow DSH official primitives.

Saves architecture and validated plans, runs native AgentTeams workers with approval, parallel coding and independent review, and summarizes progress and verification results.

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.

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.

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.

Meeting notes for dsh: one meeting per conversation, transcribed and speaker-separated on the machine itself, with points, assignments, handwritten notes, reviews and monthly scores in the same panel. Exports SRT/Markdown/Word and packs a meeting into a portable zip another dsh can import; confirmed assignments go to DingTalk/WeCom in one click, following your robot settings. Recognition models are swappable (a stronger local model can be imported from a file), and cloud recognition is off by default with no request made before consent. Word export needs Python and python-docx; the first run downloads a recognition model (28 or 77 MB).

Search public-domain libraries (Project Gutenberg, Wikisource, Internet Archive, Standard Ebooks) by title, author, character or half-remembered plot, or import EPUB / PDF / TXT / DOCX / Markdown, and listen in the sidebar with Microsoft Edge neural voices, offline Kokoro or the device voices; keeps your position, takes voice notes anchored to what you just heard, and discusses the book with your DSH model.

Reads a local video or page link with ffmpeg and Paraformer ASR, returning a timestamped transcript plus key-frame images the agent can open; finds candidate videos by keyword and installs its own runtime dependencies on first use. Requires Python 3.8+ on PATH; video_discover also needs a one-time douyin/xiaohongshu login.

All-media (video/audio/image) sibling of liustack/modlens: turns local media into structured evidence (timeline / transcript / layout regions) injected into the conversation so text-only models can cite concrete frames, seconds and speakers — Evidence, not imagination. One failover chain per media, recorded hop-by-hop in meta.attempts (a fallback is never silent): host-registered multimodal models first, then a configured OpenAI-compatible endpoint (comma-separated multi-key rotation), then local ffmpeg frame-sampling / a local ASR CLI, degrading to metadata-only as a last resort. Absorbs modlens battle-tested rules — cooldown keyed only on auth/quota failures and softened to chain-tail (never dropped), redact-before-clip error text, magic-byte sniff that never trusts file extensions. Optional vision-bridge registers a media-lens wrapper into the host model selector so a pure-text model gains image/audio/video sight. Registering / authenticating multimodal models and true inline audio-video upload lean on two companion plugins, declared as optional peers (absence auto-degrades, never errors): dsh-thinking-levels fronts the model config / registration surface, dsh-llm-openai-completions takes over the OpenAI-compatible transport that serializes file blocks into video_url / input_audio. Privacy: media goes to remote providers by default; strictLocalOnly keeps content on-machine. Prerequisites not shipped with the package: ffmpeg/ffprobe on PATH (frame sampling / reencode / duration probe; degrades to metadata when missing) and a user-configured local ASR CLI for the audio fallback. Hosts 0.1.7 / 0.2.0 / 0.2.1 (6 rc, exact engines.dsh enum). npm: dsh-media-lens.