Plugins

Reports which skills each turn invoked (loaded by the model, typed as /name, or none) and manages the local skill directory: install from a pasted repository, folder, SKILL.md or zip address, set a Chinese display name in the skill meta.yaml, and remove a skill while keeping a backup.

Claude Code-style declarative permission rules: ordered allow/deny/ask YAML rules matching tool names, arguments, workspace paths, and agent identity on the tools/pre-execute waterfall, with full session-log audit, dry-run mode, and hot reload.

Drive OriginLab Origin scientific plotting from AI chat via MCP: 62 tools. Modal-dialog watchdog, LabTalk safety gate, release/reconnect, fit with initial/fixed/weighted params, fine-grained plot and sheet edits, matrix tools, FigureSpec declarative YAML (diff-able and replayable), MCP Resources read-only snapshot, matplotlib-figure bridge, PowerPoint assembly with panel labels, Graph Gallery template search, EPS export, file-access whitelist, visual regression baseline, and editable OPJU delivery with deterministic readback verification.

Native Rapid-MLX provider for DeepSeek Harness (dsh) — dsh reads model facts from the server instead of your settings.yaml.

Preset workbench for editing per-agent personas, system prompt sections and tool descriptions, selecting tools and skills, previewing request context, and importing or exporting JSON/YAML configurations.

Evaluation harness for DSH plugins: YAML cases drive real headless agent runs, assert on tool calls, args, results and token usage, with a baseline gate for CI regression.

Web-novel writing assistant preset for DeepSeek Harness: the main agent acts as editor-in-chief and delegates outlining, drafting, review, lore-keeping and research to five helpers across a seven-phase workflow, with the whole novel kept as a structured markdown/YAML novel/ directory (character cards, a foreshadowing state machine, timeline, narrative memory), plus a sidebar creator panel and six zero-dependency CLI commands.

Patent-writing gatekeeper for dsh - screens the idea against Chinese prior art first, then runs five-party alignment interviews, eight-chapter disclosure drafting, figures and simulation runs, seven-dimension review and docx/PDF export. Skills work out of the box; the search, export and experiment tools come from a bundled Python MCP service that ships disabled - one line in ~/.dsh/patent-services.yaml enables it, and uvx fetches the wheel from PyPI.

Configurable default model for subagent delegations via settings.yaml, with single-model and multi-model round-robin or random strategies.

Reports which skills each turn invoked (loaded by the model, typed as /name, or none) and manages the local skill directory: install from a pasted repository, folder, SKILL.md or zip address, set a Chinese display name in the skill meta.yaml, and remove a skill while keeping a backup.

Manages OpenCode Zen free-tier and Go-tier models in settings.yaml — fetches the live model listings from opencode.ai, reports configured-versus-live drift per route, and adds or removes entries through four agent tools and an OpenCode Models settings section.

Rewrites the Chinese copy of a market-installed plugin into English using a model already configured in the harness — automatically when the plugin is installed, then live-reloaded. Each file is translated in one whole-file pass, validated by a real parser where one exists (node --check for JavaScript, JSON and YAML parsers otherwise) and handed back to the model with the error for up to three repair attempts; files above 48 KB, or that never parse, fall back to a line-range pass that checks every returned line against the original for structure. Trigger phrases and locale tables can optionally be preserved rather than translated, and the untouched original is kept beside the file as a .zh.bak.

Config-driven lifecycle hooks, including context injection: declare event, handler and outcome in YAML, where the handler is an in-harness tool (MCP included), a shell command or an HTTP endpoint, and the outcome is model-visible context, a denied tool call, or fire-and-forget.

Claude Code-style permission rules engine: hard/deny/ask/allow tiers with a hard tier above full access, workspace-scoped rules, wildcard path protection, and a visual staged editor; rules persist in settings.yaml.

Keep your automation tasks in files you can version and review: write each task's schedule, prompt, model, permission and working folder in YAML, JSON or JS, and DSH's own Schedule is kept exactly in line, creating, updating, disabling and deleting tasks within a second of a save. Each task runs in its own conversation, persistent or fresh per run; /automations syncs on demand and names any file mistake; and a sync that finds no tasks never wipes the Schedule.

Connects GitHub's official remote MCP server as a DSH plugin: stores the PAT in DSH's credential center (.credentials.yaml) via github_set_token, and exposes GitHub tools as mcp__github__*.

Runs AItelier pipelines from DSH over MCP: 39 tools (22 read, 17 write) to generate, run, inspect, edit, export and import multi-step SkillFlow pipelines. The steps execute in AItelier rather than in the DSH session and are polled by run_id, pause at engine-enforced checkpoints answered with answer_checkpoint, and leave per-step traces and step outputs to read afterwards; add_provider / map_model / edit_role point an individual role at a local or cheaper endpoint. Bundles an aitelier-pipelines skill.

Model capability editor: edit each model reasoning-effort levels and image-input (multimodal) declarations inline in the official model page, written back to settings.yaml as a whole group, with a floating entry as fallback when the anchor breaks.

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.

Use AItelier as a subagent from DeepSeek Harness: generate, edit, run, export and import SkillFlow pipelines. Client only — requires a running AItelier backend (Docker; see README).

World-model research toolkit: inspect frames, name 3D / pixel / latent routes, score pred vs GT, and RSI skills / wm.yaml.

Defines multi-agent team rosters as YAML or JSON files, validates them, bridges each member into dsh-agent-teams member arguments, and verifies the result against the running team.

Defines multi-agent team rosters as YAML or JSON files, validates them, bridges each member into dsh-agent-teams member arguments, and verifies the result against the running team.