Plugins

BrowserSkill bridge for controlling visible Chrome and Edge Agent Windows from DeepSeek Harness, with native browser tools, accessibility and VOM observations, screenshots, owned multi-session control, and a live Web UI overlay.

An agent preset for evidence-checked research: every step advances only through system-verified observations, L3+ claims go to an independent evaluator, and the session folds into a replayable ledger that grows into a browsable domain ontology. Requires DSH >=0.1.7-alpha.1.

Accessibility-first macOS computer use: fresh observations, stale-state rejection, scoped permissions, and safe input.

Reference logged-in Chrome/Edge tabs, authorized local HTML and running Windows apps through a searchable native sidebar or @. Open browser candidates and the webpage sidebar refresh when the extension connects, disconnects or changes tabs, including an unchanged search query. Windows application lists skip protected processes with unreadable identity metadata and preserve other running apps. Read scoped snapshots, batch actions, capture screenshots and use available WebMCP tools; supports authorized uploads, drag/drop and opt-in network metadata. Browser actions preserve failure evidence, accept an explicit post-action observation scope and can check one final visible/text/value/URL condition without replaying effects. Expandable context preserves model content; dockable annotations support a session-bound staging queue with edit/delete/restore, and append to chat drafts without sending. App control prefers a matching installed MCP or approved installation, then Cua Driver with text, hotkeys and paged controls; desktop reads present the window once per connection or when recovery is needed. Background input depends on the app. Requires Chrome or Edge 125+ with the companion MV3 extension manually loaded and paired; installing the plugin does not install the extension.

ROS2 debugging toolset and robot-state vision analysis for DeepSeek Harness: node/topic/service/action/interface/TF enumeration, whole-graph topology JSON, rosdep checks, approval-gated builds and custom message scaffolding, GUI screenshots and multimodal vision observation, plus headless RViz2 offscreen rendering (low-poly meshes, direct pixel read, GPU passthrough - motion rendering at 30Hz) with parallel VLM realtime analysis.

Cross-session memory with zero-token deterministic recall (bigram-Jaccard fused with FTS5 BM25 via RRF, Chinese-aware tokenization), reinforcement that strengthens repeated observations, and procedures crystallized into SKILL.md files refined in place through an active-stale-archived lifecycle with reversible archive and rollback. Ingest and disposal each expose a user-chosen autonomy tier; conflicts and high-importance writes always go to human review, and nothing is ever auto-deleted. Requires Node 22.5+.

Windows Computer Use for DeepSeek Harness with 21 tools and verified targeting: controls are addressed through their UI Automation identity and patterns, and numbered marks are checked against the live window before input, so a stale target is refused instead of clicked. A known sequence runs in one computer_batch call, one model round trip; incremental observations, compact receipts (normally no more than about 6 KiB extra), bounded handoff waits (HANDOFF_WAIT_TIMEOUT/HANDOFF_HELD_TIMEOUT as continuations), the desktop-block cycle boundary and a deterministic 100 s owner-scoped leave backstop keep context small and prevent a stuck handoff from waiting out the tool deadline. Approval modes settle an unanswerable ask honestly: in a danger-full-access session with no approval card the action runs under that grant, every other sandbox — and every delegated subagent session — refuses and names the unavailable channel instead of reporting a user rejection, and gate diagnostics land in policy.jsonl. Human handoff, a model-visible stop brake and prebuilt native workers.

Zero-touch runtime telemetry for DSH: every session auto-writes task, model tier, tools, failures, duration, status into a human-readable journal with a stats section (pure observer — no tools, no LLM calls, no injection).

Opens a browser console to prepare Minecraft Java on Windows x64 and run DeepSeek-controlled survival through native-player MCP tools, with spatial observations and an optional livestream overlay from a separate extension. Requires an installed Minecraft Java Edition 1.21.x client and an official DeepSeek API key with at least ¥1 balance.

Windows desktop observation and control: accessibility tree, screenshots, OCR fallback, mouse and keyboard, with session-scoped approvals and an ESC halt gate. Computer use and desktop automation for DSH, including GUI automation fallbacks.

Shows what each completed turn cost in CNY on its own row under the turn, with a click-to-open breakdown naming the tokens per category, the unit rate, the matched price rule and the billing instant; per-model rates are edited on a settings page inside DSH — four token categories plus weekly recurring peak/valley rules with arbitrary multipliers and absolute time-window promo prices that override them. 0.2.3 adds account awareness: it reads the balance or remaining quota of 14 providers (DeepSeek, OpenRouter, Zhipu GLM, Moonshot, MiniMax, Kimi Coding, OpenAI-compatible relays and more) and shows today / this month / this session spend next to it; spend is accumulated in a local ledger (atomic writes, one book per API key, 1-24 month retention, JSON export), and a monthly calendar lets any day be corrected by hand - including days with no balance observation - with an optional note. Usage and rates are computed locally from what the provider reported; balance is read straight from the provider API with your own key, and nothing is sent to a third party.

Closes ai-memory's consolidation loop for DSH: when a DSH session ends it calls ai-memory finalize-session, so the observations DSH captured become wiki pages the other harnesses ai-memory serves can read. Skips subagents, queues concurrent session ends, and sweeps sessions abandoned by a forced restart.

Security-audit facts AI coding agents cannot compute, plus a verdict gate: euthyna history attributes deleted lines to commits and flags those from security fixes (--origins finds the first introducer), euthyna coverage reports which changed symbols no test ever invoked, and euthyna gate checks each finding against six gates, downgrading any without evidence to an observation.

Distills session work into background observations and reflections to preserve context across compaction.

Windows desktop automation for DeepSeek Harness: accessibility-first observe/act loop with 30 standard tools (element/coordinate targets, auto/a11y/event strategy routing), superseded-observations and no-replay safety semantics, persistent kill switch, RuntimeId/rect-drift stale protection, chat cards and a floating panel (live ops, frame wall, env auto-detect settings), tiled vision describe for low-res models, JSONL audit trail and a pid allowlist. Requires Windows 10+ and Python 3.9+ with uiautomation and pillow (installable from the settings page).

Self-contained DSH profile plugin that bridges the cua-driver 0.46.1 Windows kit into DSH as about 63 cua_* background desktop and browser automation tools (UIA observation, PostMessage input without focus stealing, CDP browser control, agent-cursor overlay, recording and diagnostics), with a bundled /cua-driver skill. Windows x64 only; bundles the prebuilt cua-driver.exe and starts it with --grant existing-profile, so the tools can attach to your already-running Edge or Chrome profile and act in its logged-in sessions.

Records the version of the edit target as an observation when the agent inspected the file with bash instead of the read tool, so the filesystem observation policy stops rejecting edits with FS_NOT_OBSERVED. Applies to edits only; blind whole-file overwrites stay guarded.

Windows computer use for DeepSeek Harness: a persistent PowerShell UIA backend behind 16 merged MCP entries (read/click/type/observe/verify), a hardened policy layer (red lines, approvals, observation binding, point-ownership and focus guards) and a performance pass (prewarm, caching, pixel-hash stability probe). Fork of dsh-computer-use-win.

DSH Plugin DevKit: runtime observation kernel, isolated debug boot, host contract tests, slot preview

Conversation-level multi-agent workbench for editable task planning, real child-session observation, plan-versus-runtime comparison, and evidence-backed recovery previews; tested with DSH 0.1.1-rc.2.

Clean Architecture reference library for DSH: 8 pinned reference repos (Go, Java, C#, Python) indexed into a local FTS5 database, with assertion rules, a review ledger, and a read-only observation panel.

Step-0 observation probe for dsh-compaction-audit: logs every compaction/* session event and probes whether shadowedSeqs still resolve to readable events in session.events. Observe-only; never injects, never blocks.

Human-in-the-loop tools (`human_task` / `human_task_ready_check`): pause the agent until the user performs a real-world action or returns an observation (GUI operation, game testing, visual verification, hardware checks), gated by session consent and an AFK presence check, returning a structured JSON result via a web task dialog.

Routes chat images to a fixed OpenAI-compatible vision model, returns factual observations to the selected main model, and reuses session-scoped observations across replay, compaction, and restarts.