Plugins

Four read-only tools over a WeKnora knowledge base: list knowledge bases, hybrid passage search, reassemble one document's chunks in order, and WeKnora's own cited RAG or ReAct-agent answer with a resumable session id.

Pins the model's reasoning language to a tier picked in Settings - General - Thinking language (Simplified Chinese, or a default tier that only constrains replies and leaves reasoning to the model), by injecting a system-prompt section at order -1000000 and re-hoisting it to first place at the end of the assemble waterfall, plus an optional per-turn copy placed ahead of each user message for models that follow static sections weakly.

Visually configure an agent's prompt, tools, visible skills, subagents and fallback models, and assemble several of them into a team. It never writes or edits any host preset file.

DeepSeek Harness Default System Prompt Management Plugin: Browse, search, and override the final system prompt assembled into each session round within the Web GUI "Settings".

OMK — Observe. Measure. Know. Evidence-backed knowledge changes for AI applications.

Role-based multi-agent team for DeepSeek Harness — your digital workforce. One goal assembles a team of product, architect, frontend, backend, reviewer and QA roles that deliver through a nine-phase gated pipeline, plus a persistent team you can keep directing across sessions. Zero runtime dependencies, no build step.

Chinese localization for DeepSeek Harness: translates all system prompts, tool descriptions, and runtime contexts into Chinese at runtime via the system-prompt/assemble waterfall (1788 entries, zero source modification, uninstall restores English).

Context Assembler plugin for DeepSeek Harness: topic-block context assembly, water-pressure topic splitting, tool trace/rewrite, reality recall injection and handoff planning.

A full-screen novel-writing workbench: chapter board, draft candidates reviewed and merged paragraph by paragraph, plot summaries carrying an index header, and a writing pack assembled from the outline, character cards and earlier chapters. It also reads a shelf of finished books you import, extracts their writing habits in one run, merges them into a single style standard, and puts that standard in the prompt for every later generation.

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).

Memory-assembly team for DeepSeek Harness: every message triggers a five-step cognition loop (judge, retrieve, assemble, feedback, calibrate) carried by role-playing subagents (judge, retriever, archivist, advocate, editor, scribe), using BM25 retrieval, time-ordered material, and LLM assembly to build useful material packs injected into linghun. Works with dsh-linghun.

Model attribution panel for the dsh web chat — local, zero-network structural fingerprinting (template token leaks, glitch canaries, tokenizer usage-vector matching) that scores which of 29 AI vendors a session's responses most resemble, with a per-claim evidence ledger and a 12-probe active battery.

Council of High Intelligence: assemble an 18-member think tank of historical figures for multi-perspective structured debate, with a live DAG side panel, SQLite run history, and per-project defaults via .council.yaml.

Inspect, measure, search, and compare the exact provider-neutral context assembled by DeepSeek Harness.

Bi-temporal memory backed by a SodaMem daemon: recall is contributed to the prompt while each turn is assembled and every closed turn is ingested, with no tool call and no model call on the read path; each recalled fact cites the turn it came from and carries validity dates, so superseded facts stop being retrieved.

DSH hardware sandbox plugin: assemble a 3D scene of ESP32 / BME280 / PIR / LED components with protocol- and voltage-checked port wiring, run real Python project code in the loop against simulated I2C/GPIO hardware with fault injection, let networked device simulators report over HTTP to a real backend (validated end-to-end against a live seat-reservation system), observe a real device read-only and map its state onto the scene, and add support for new boards through data-driven pin definitions. Ships 16 hw_* agent tools plus a bundled SKILL.md.

SSQ (China Welfare Lottery Double Color Ball) helper for DeepSeek Harness: 1000-draw trend tracking, six prediction models with ensemble voting, structure analysis and dan-tuo/random generation via the ssq tool.

Turns a Minecraft Java save into a Blender cover or animation: scans the .mca region files for flat open ground, exports the terrain to OBJ, and drives Blender headless with MCprep materials, the BPS character rig and three-point lighting. After each render it reviews the frame with OpenCV for facing, ground contact, exposure, lighting and composition, then re-renders with adjusted parameters when a check fails. Requires Blender 4.2 LTS or newer and Python 3.10+ with opencv-python and numpy; the first run assembles the Minecraft assets with the bundled setup command.

Semantic code search for DeepSeek Harness: registers two native tools, semble_search (find code by describing its behaviour, returning ranked file:line hits) and semble_find_related (neighbours of a known location), which run the Semble CLI via uvx, plus one bundled routing skill. Requires uv on PATH; the first query on a repository builds its index.

Standalone prompt assembly with a source registry, custom text, strategy editing and previews, and per-session strategy snapshots through DSH public hooks.

System-prompt surface drift gate: every section any plugin or preset contributes to the assembled system prompt is hashed and baselined; a silent rewrite of what the model is told fails loud.

Host-side long-task detection for small-context local models: long outputs are scaffolded into a parts skeleton (one part per reply, then assemble), and multi-step work into a PLAN.md ledger with notes/ (plan first, one step per reply). Detection is heuristic and host-side, so it does not depend on the model judging its own limits.

Read, build and take apart EPUB 3 books with no external dependency — the ZIP reader/writer and the XML scanner are both built in. Inspect metadata, reading order and TOC for structural problems, assemble a book from chapters, or split one back into per-chapter HTML.

Wire-level LLM inspector: captures the raw HTTP request and response bodies of model provider calls and attributes each capture to the session that caused it through the llm/stream waterfall. The request pane renders the body as a collapsible tree with per-node sizes — one measured request was 98.6% a single 973-entry array — and the response pane reassembles the OpenAI-style SSE chunk stream into reasoning, content, tool calls and usage. Shown in a Conversation View tab and a loopback viewer page.