Plugins

One-handed, keyboard-free input for the composer: press and hold the mouse on the input box, speak, release — the text lands in the draft, and sliding up cancels without leaving a half sentence behind. No mic button to aim at and no shortcut to remember; the hand never has to leave the input area, which is the point when the other hand is busy. Recognition runs fully offline (SenseVoice via sherpa-onnx, no API key, audio never leaves the machine).

Self-tending living memory in one local SQLite file — a nightly patrol dedupes, merges, decays and cross-links entries on its own, with seven-signal RRF hybrid recall (FTS5 + jieba Chinese-first tokenization, optional vector KNN, graph PPR), a typed knowledge graph, conflict detection instead of silent overwrites, and web telemetry panels.

Local-first AI project orchestration workbench and CLI plugin for DeepSeek Harness: approval-gated planning, Git worktrees, task execution, Issues, and auditable evidence.

Adds a native Store section to DSH Settings for catalog search, tag filtering, sorting, installation, and installed-plugin management.

Local-first long-term companion plugin suite for DeepSeek Harness

Local-first A-share portfolio and watchlist monitor for DSH: sidebar quotes, holdings P&L, browser add/edit/delete, and JSON persistence.

Agent config packer for DeepSeek Harness: pack local agent assets (skills/sessions/profiles/settings/memory) into a zip for migration or sharing, with privacy scanning, diff-based restore, pack management, and a settings UI; memory ships as a read-only Markdown mirror, not the live database.

Voice notes in, spoken answers out: dictate audio that becomes user messages (transcribe), have the agent read replies aloud (speak), local-first under ~/.dsh/voice.

Cross-Workspace Memory: local-first, encrypted, per-session authorized memory for DSH.

A local-first ledger for DSH. Record an expense or income from one sentence or a screenshot, with accounts, categories, refunds, installments, loans, debts and offline HTML charts. Data stays in local JSONL files.

Reversible context folding plus external memory for DeepSeek Harness: tool output collapses into expandable lines, session facts become auditable records.

Suiji Word Book: a word book panel that shows one word per page with picture, phonetics, pronunciation, five phrases and an example sentence, plus fill-in-the-letters spelling practice and a 12-type word-recognition quiz. Word data is kept as plain files in a local directory, and an included skill lets an AI add cards from a word list. The “+Word” button (hidden until enabled in Settings) starts an AI session and by default switches it to danger-full-access; turn that off in Settings.

Suiji (随记) is a local-first notebook plugin for DeepSeek Harness: quick capture, AI filing, diary, todos, sticky notes, and MCP access for AI sessions.

Development-preview DSH plugin to import recordings from compatible BLE recorder cards, with optional transcription and fingerprint-linked archives. Requires recorder hardware, Host Bluetooth and Python dependencies.

DSH 工作区记忆画像：有证据、有版本、能解释变化；索引简报省 token、轮末本地召回、红线 / 审批队列 / 版本链 / 注入审计；零依赖、不出站。

Local-first knowledge base for DeepSeek Harness: each base is a folder of UTF-8 text, text PDFs and OCR-read images; the plugin retrieves from the session's selected bases before every turn with Chinese n-gram and BM25, answers carry citations that open in a right-sidebar reader, and the agent gets five kb_* tools (list, search, read, stats, propose text) plus /kb commands for create, use, files, search and rescan.

Local-first academic assistant: event-sourced courses, tasks, plans, reminders, course materials and 23 DSH tools.

DSH sidebar and panel for courses, tasks, plans, connections, course materials, automation and settings.

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

Read local EPUB, TXT and Markdown novels below selected native DSH chat regions on explicit expansion, preserving real reasoning and tool output without sending novel text to the model. Requires DSH >=0.2.1-rc.2 <0.3.0-0.

Local-first memory and persona plugin for DeepSeek Harness: four file-backed memory layers (pinned, memory, facts, experience), editable SOUL/MOOD persona, zero-LLM session capture, BM25 recall and a themed mood card; no standalone web panel, Python service, vector database, or API key.

Long-term memory scoped to the DSH workspace. A session opens with a one-line-per-memory index - bodies are fetched on demand by the `memory` tool, so the same character budget holds about three times as many entries - and when a turn ends a purely local search over the last user message pulls the matching memories into the next briefing at no model cost. Optional auto-capture distils a stretch of conversation into structured memories; in draft mode they wait in an approval queue instead of landing directly. A portrait layer keeps a workspace and a global picture, rewritten from the journal and the records, with the same draft-or-direct choice. Settings → Memory holds the archive, list and timeline views, a health check (duplicate clusters, stale entries, broken links), the supersede version chain with rollback, and a read-only scan that imports existing WorkBuddy / Qoder memory files.

Cross-session long-term memory backed by a local Go service (~50MB RAM, SQLite-vec + verify-augmented claims): zero-lag recall injection on every user message, session-end transcript ingest, and memory_search / memory_logs / memory_ingest tools. Install with `dsh plugin --profile web add @djasdh/interest-memory-dsh-bridge`. Protocol bridge only: the Go service is not installed by `dsh plugin add` — install and start it per the repository README first; until it runs, the tools silently return nothing.

Read-only Skill inspector for DeepSeek Harness. It shows which Skills a conversation actually loaded — from observed loading evidence only — and which Skills the current environment can discover, and gives each Skill a detail page whose left column switches between the framework, the evidence, validation, evaluation, step evidence, run logic, the before/after of a modification when there is one, the definition facts and the raw SKILL.md. Declaration and run stay apart: the body is split into structure, declared workflow and progressive disclosure, the run logic is read from local receipts, and every declared step carries its own evidence — no observed evidence is reported as exactly that, not as executed or not executed, and the run's instruction hash sits beside the current definition as match, mismatch or unavailable. The evidence view gives each subject one of three states (declared, observed, unavailable), walks definition → load → use → outcome, names what each stage cannot reach, declines to attribute an outcome to the Skill, and can copy the snapshot out as byte-stable JSON. Validation checks the Agent Skills open standard and the platform profiles separately, each finding on a rule id and labelled standard compliance or platform compatibility. Evaluation keeps repeatable cases with the conditions each run was recorded under and the four stages of runtime evidence, comparing two runs of one case item by item without scoring them. The trigger boundary keeps should-trigger and should-not-trigger prompts, has you run them in a real new session, then records what was observed — whether a load record appeared and whether it was model-invoked or user-explicit; a typed /name is not a natural trigger, and an absent load record is reported as not observed, not as not triggered. The same projections are exposed to the agent as a read-only skill_review tool, which returns a structured review packet and says what it omitted when the packet will not fit whole. The inspector, the evidence chain and these reviews are computed on this machine from the definition and the session receipts; the Chinese reading version sends the body through the host's model when you ask for it, keeping code fences, paths and frontmatter verbatim and storing the result on this machine. A Skill can also be cloned into a local copy. It does not write back to the source Skill and does not run its scripts.