Plugins

Layered long-term memory for DeepSeek Harness: each turn injects a persona frame plus a relevance-ranked memory index while full transcripts stay on disk and are expanded on demand, so the vault never floods the context window. Facts are slot-keyed, so a changed mind retires the old value instead of recalling two contradictory answers. The persona frame is derived from memory by the model itself, behind three anti-pollution gates (rejecting development chatter, blocking echoes of its own injection, and merging restatements by shared content words). Periodic consolidation merges near-duplicates, rolls long topics into digests, and archives low-value old entries by tagging only — raw text is never deleted. The append-only store is compacted in place (measured 4.7 MB to 355 KB). Zero third-party dependencies, no build step. Note: per-turn injection inevitably lowers prompt-cache hit rates, so this plugin is not recommended for work scenarios.

Manage MCP servers and skills in DeepSeek Harness, and control which tools enter the next prompt.

Manage MCP servers and skills in DeepSeek Harness, and hide tool groups from the next prompt with token-cost estimates.

Requires the ZCode CLI installed and signed in. Relays the ZCode CLI agent into DeepSeek Harness as a model provider. A resident app-server session streams each turn, and the model catalogue is read from the CLI at runtime, so context windows, image support and provider routes follow the installed CLI and account. Stable 0.6.2 supports DSH 0.2.2-rc.1 and 0.2.2-rc.3, improves CLI discovery and includes optional Retry. Tool permission requests are denied by default; unknown approval or permission methods are always denied, and full-auto requires an explicit opt-in. DSH approval UI does not mediate this separately spawned app-server process, and the policy is not an OS sandbox. Derived from kyle123740/dsh-zcode-cli-proxy (AGPL-3.0); it registers the same `zcode-cli` provider id, so it cannot be installed alongside the original.

Community-maintained WorkBuddy and WorkBuddy AI model provider using local desktop sign-in, with model discovery and credit display. Version 0.1.20 supports per-model context-window selection and DSH 0.2.1-rc.2, rc.3, rc.5 and 0.2.2-rc.1; its CN compatibility layer differs from the functy23 fork. Derived from corrinehu/dsh-workbuddy-connect (MIT), it registers the same workbuddy / workbuddy-ai provider ids, so it cannot be enabled together with the original or other WorkBuddy bridges. Version 0.1.12 requires authenticated Host owner access for status and control routes.

Lighten the load on your agent: one-click toggles for skills and MCP, so the context holds only what this turn actually needs. Skills and MCP servers you switch off are no longer injected into the model context — cleaner, and lighter on tokens.

Preview conversation context and selectively keep, summarize, or omit exchange groups before applying persistent compaction through Harness session services.

One-tap connect ModelScope language and image-generation models into DSH (off by default), with image recognition and context window filled in automatically. You can also see the free Magicube balance, jump to a model's info page, and record local call counts, trends and rate-limit events, with a usage pill beside the input box showing the balance and today's local count. All of it needs only an API key, with no account sign-in.

Adds Delete conversation and Batch delete to the sidebar session menu; a deleted conversation and its subagent sessions are moved to the Windows recycle bin, from where they can be restored.

Project-level memory built around a ledger whose structure the AI designs per project; the plugin only handles detection, injection, write-back, archiving and git. Memories are split by meaning: the active ledger holds current state, the turn journal is append-only, and decisions, the goal tree and lessons are one-card-per-file (with a multi-dimension index over the card folders); the fundamental principles file can be changed only by the user. There is a single compaction mode: before compacting, the AI sorts the work into three buckets - what must stay in context and is re-injected verbatim, what is written to the project's _stash/ for later lookup, and what is dropped. When a conversation is opened in a container workspace holding several projects, it can be locked to one sub-project (the lock lives in the plugin's own home and survives restarts; it leaves no marker in your project). Also an activity log with five levels, where L4 stores the user's own words verbatim (never injected, queryable on demand). Before each compaction a verbatim capsule (user quotes, raw errors, changed files) is written to disk, and both the capsule and its source material survive a restart; a handoff document lets a new conversation resume the previous one. 21 tools, 12 subcommands, 39 settings (one of them switches whether the settings panel or each project's manifest wins). Runs locally, no runtime dependencies.

Recall a message you already sent: the session log stays append-only and nothing is deleted, while the recalled turns leave the model context and the transcript, the text returns to the composer, and the files those turns changed wait in a quarantine you can review and clear from its own settings page.

Codebase Memory MCP bridge for DeepSeek Harness: six knowledge-graph code tools, auto-started background daemon with visualization Web UI, and bundled agent skill.

Adds hover Recall / Delete / Delete-onward actions to every message row: removes content from the model context and the transcript together via durable append-only tombstones, with an empty-content guard so old deletions can never break strict providers. Tested on the DSH 2.0 desktop.

Select text in the DSH Web GUI and ask about it in place (floating button, or the Alt+Q shortcut): an independent subagent that inherits no parent context answers, streaming into a side card hosted by the native right sidebar, dsh-better-sidebar, or a built-in overlay — and the question can be routed to the main conversation instead.

On-demand skill tools for DeepSeek Harness: registers skill_search / skill_load / skill_ref so the model searches a staged skill library first and loads a skill body only when it needs it, keeping the library out of the resident session catalog and out of every turn's context; adds a Skills tab to the conversation view ring that reads back the whole session and lists which skills were actually loaded, and in which turn. Zero dependencies, and the records render client-side only, never entering the model's context.

Discovers OpenRouter's free models, measures their real speed and context size, and writes them into the DeepSeek Harness model list fastest-first, with speed and context in each display name, plus a Settings section to configure and run it.

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.

Mounts the mcptoon MCP gateway as one stdio server, so the agent discovers the tools of every MCP server you have configured through mcptoon's compressed view instead of full JSON schemas on each turn. Ships a doctor CLI that checks the mcptoon executable resolves and prints the exact command/args override when it does not.

Model-driven context-window rollover bundle for the DeepSeek Harness: fresh working context, durable model-managed notes, a small verbatim recent tail, and targeted history recovery — no summarization.

Automatic approval and review for tool calls that need approval: keyword buckets match red lines without context, then a judge model classifies what was requested and sends it to allow, reject or a human according to the matching row and risk level; a judgment that never ran always goes to a human, and rejections tell the model why.

Workspace-wide sticky notes on an infinite canvas: notes are plain Markdown files, created manually, captured from conversations, or distilled by the AI, and can be handed back as context; ships 12 model tools and 5 bundled skills for creating, tagging, arranging, comparing and merging notes.

Context curation for DeepSeek Harness: content-aware tool-output slimming, dead-log sweep, pinned user requirements and in-flight work, archive-then-compact with an audit trail.

Settings panel for global and per-workspace AGENTS.md, with identity and persona injection, runtime-context toggle, and result notifications.

Input-bar prompt optimizer: rewrites drafts with 6 strategies × 3 languages plus custom instructions, optional goal/todo/compaction-summary/tool-result and chat context, a confirm dialog, last-5 history and three-tier settings persistence.