Plugins

Turns explicit DSH session experience into reviewable native Skill drafts that are saved only after user approval.

WSL workspace support for DeepSeek Harness——无缝的 WSL 工作区使用体验，无需在 WSL 之中再安装一个dsh，安装该插件后在 GUI 里直接添加 WSL 工作区即可。WSL workspace support for DeepSeek Harness — Enjoy a seamless WSL workspace experience without needing to install dsh inside WSL. Once this plugin is installed, you can directly add a WSL workspace right from the GUI.

Bridges WeChat private chats into DSH, with two-way text, image, file, audio and video messaging and slash commands for sessions, workspaces, models, permissions and more; the goal is DSH's native experience inside WeChat.

Project memory for dsh with native task-system integration: session todos and file reads auto-condense into cross-session task records (task↔file links), so workflows are switchable and resumable without re-contextualizing. Read-time memory (no upfront scan): extracts code symbols, summarizes docs, and cross-links docs↔symbols; optional TS semantic enhancement (auto on when TS installed). Experience notes auto-dedup. Every recall includes a traceable citation. Pure JS, single dep, works out of the box, silent operation, zero overhead.

VSCode-style editing experience for DeepSeek Harness: a Monaco-based central file editor (tabs, Ctrl+P quick open, status bar) and interactive diff review of agent edit/write results with per-change Keep/Undo, rollback and archiving, persisted across restarts.

Input experience enhancement: voice input, AI polish, anti-overwrite, prompt optimization with diff preview, and auto locale switching following DSH UI language.

Provides a better model experience, including but not limited to: filling model parameters in multiple ways, improving model compatibility, remembering reasoning efforts, subagents following the parent's model and reasoning effort in real time, and AI self-adjusting reasoning effort.

Disciplined engineering workflow system for AI coding agents: planning, TDD, auto-fix loops, codebase knowledge graphs, and multi-agent wave execution. Needs codebase-memory-mcp (pip install "codebase-memory-mcp>=0.10.8,<0.11.0"); /scan stops without it.

Tavern management panel for DeepSeek Harness: character cards, worldbooks and presets with per-session isolation, memory summaries, a character relationship web, one-click plot options and an NSFW mode. The full experience also needs the companion plugins dsh-muv-engine and dsh-muv-table, which render the status bar, the plot options and the variable tables. Card-provided status bars, variable folds and option cards now render inside a sandboxed iframe, so card styles no longer leak into the host UI.

Finds potential UX issues in your project: automatically reviews React/TypeScript code, pinpoints each problem, and gives concrete suggestions.

The memory organ of Agent-Body: after 2 minutes of silence it enters light sleep and after 5 minutes deep sleep, where deterministic rules distil real experience into memory cards (pitfalls, playbooks, hotspots, unresolved issues, facts). Incoming commands recall related cards by keyword and inject them into context; cards unused for long decay on a half-life and are archived rather than deleted. It also de-noises body-wide homeostasis warnings: repeats inside a window are counted instead of re-printed, and a warning that keeps recurring without ever healing converges into a known steady-state offset and goes quiet. Consolidation and recall make no model calls.

Experience memory and reflection for DSH agents: automatically extracts candidate lessons after tasks and recalls user-reviewed lessons in relevant later tasks. Supports source provenance, project-scoped retrieval, conflict handling, usage feedback and expiry review; capture and recall have separate controls, and writing lessons to AGENTS.md requires preview and explicit application. A step-by-step first-run guide (4 steps) now opens inside its settings panel.

Expert-mode agent preset for DeepSeek Harness (v0.9.2, npm dsh-expert-mode, bilingual EN/ZH): a chief coordinator plus 17 domain-expert subagents with automatic task delegation. Features: taskboard scheduler (file-system task state machine pending/ready/running/done/failed, dependency DAG, atomic claim, retry, crash recovery), quality gates (5-stage pipeline for high-risk tasks: requirement clarity, implementation, verification, independent review, integration, with 2-round rework limit), Five-Anchor constraint (review/convergence/anti-drift/collaboration-check/resource-awareness per turn), Near-distance Guidance (identity/task/output template per expert), progressive disclosure (~28% token savings), expert persistence, inter-expert file message bus (direct P2P comm, zero coordinator relay), cross review, experience pool, fast-track for simple tasks, fault recovery with auto-retry. Experts: data analyst, copywriter, legal review, product manager, frontend, UI/UX, architect, social media ops, growth hacker, quant finance, finance, backend, DevOps, database, QA, security.

Harness² recursive self-improvement for DSH agents: Propose–Probe–Compose, isolated probes, anonymous selection, and a per-domain experience library.

Automatic session retrospective: after a conversation wraps up, distills useful experience into reusable skills stored in the skill library for the model to call, with signal-accelerated triggers, manual runs, an approval mode, and skill-library governance (archive/restore, never deletes directly).

Installs the browser4-cli runtime and registers the Browser4 skills (browser4-cli, browser4-experience, browser4-plugin, scent-miner) into the DSH skill registry, providing an AI-native browser engine for autonomous agents, intelligent extraction, and large-scale web automation.

DeepSeek Harness plugin that connects to a PowerContext Server over HTTP for recall, memory, handoff, experience, and skills.

Bring the deepseek.com web/app chat experience to DSH: edit your prompt and regenerate answers in place, with a per-message <i/N> version pager (tree model, stable across conversations).

A judgment core for DeepSeek Harness: a cognition loop that gives every decision a source and attribution and drives planning to a close, hippocampus memory that condenses running experience into knowledge (journal ledger, episodic archive, low-confidence decay), judgment identity responsibilities in the architecture (consistent rubric, judgment bound to its source), and an A2A memory-management team via dsh-linghun-assembler. Requires DSH ^0.1.0-rc.7 (rc.2 supported).

Execution-discipline suite for DSH with identical-retry interception, forced reflection, todo enforcement, memory deduplication, and experience-to-skill promotion (11 plugins + 6 skills).

Local-first Agent Experience Network for DeepSeek Harness, distilling task-level experiences from traces and live manifests, grading by H0-H4 evidence, discovering by compatibility, and promoting only after explicit review.

Lets text-only models handle pasted chat images, with a native vision experience, batch image viewing, and a built-in OpenAI-compatible analyze_image tool; vision-capable models are unaffected.

An Everforest colour theme for DeepSeek Harness, for both web and desktop. Forest tones, with six dark and light palettes. Chat content (headings, code, links, tables and more) is restyled with independent colours, and every tone and saturation level is tuned individually for a warm, gentle, easy-on-the-eyes experience.

Evidence-based experience learning for DeepSeek Harness