Plugins

Engineering workflow agent preset for dsh: five gated phases (requirements clarification, plan approval, TDD, parallel subagents, verified finishing) with six workflow skills adapted from obra/superpowers.

12 tested engineering skills (git commit, code review, test-first, plugin dev, debugging, changelog, PR review, task breakdown, and more): every SKILL.md passes a format validator aligned with the official skill rules plus an isolated load smoke test.

Context-aware next-message suggestions in the DSH composer, with repeated Trigger cycling and explicit user review before sending.

Six engineering skills for Claude Code and DSH: research, architecture scouting, implementation orchestration, three-axis diff review, complexity audit and mutation testing. Skills delegate through the host’s generic subagent tools when available.

Shows which graft index a dsh session is serving and whether it still matches the code, and gives the model graft_ask, graft_grep, graft_callers, graft_skeleton and graft_map as native tools.

Governed long-term memory for DeepSeek Harness: append-only evidence ledger with write guards, 7-tier authority claims, supersede chains, and budgeted pre-step injection with per-domain quotas.

Superself's self CLI as model-facing tools (project context, work units, reports, decisions) plus a /self command — cross-session, cross-project project state kept outside the code repo.

Learning Mode agent preset: a coding agent that teaches while coding — concrete scenario-grounded explanations, Socratic guidance, and TODO(你) practice blanks, modeled on Claude Code's Learning output style; installable via dsh plugin add (dsh-learning-mode on npm).

Config-only DeepSeek Harness bundle that mounts the in-box MCP client bridge against morluto/rea over stdio, for reverse engineering anything with agents, from app behavior down to native binaries.

Full upstream REA 6.4.0 integration over the official DSH MCP bridge: all 170 tools, the version-matched reverse-engineering skill, and the six original MCP prompts with argument completions reachable from the composer / menu, all sharing one REA process and complete structured Evidence. External analysis engines are bring-your-own.

Engineering intelligence for coding agents — persistent, evidence-aware, project-isolated engineering memory with unified hybrid search (memory + RAG documentation) for DeepSeek Harness.

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.

Advisor reviews that converge over rounds, an engineering mode whose writes require a design token, escalation of a task to a stronger model, and parallel multi-model consultation; a settings page edits the model routes, timeouts and budgets.

Registers the fight-scene-director skill for designing AI-video fight scenes, ported from the Codex skill of the same name. Emits structured Chinese prompts for Seedance 2.0/2.5 and MiniMax H3, plus trajectory-map and initial-position image prompts.

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.

Learns what you leave unsaid in your prompts from messy turns and your own corrections, and injects the learned directives into the system prompt of every new conversation; the directives are editable in Settings and a composer button rewrites the current draft with them.

Adds persistent execution contracts, recursive work graphs, critical clarification, and evidence gates for long or underspecified Harness tasks.

Prompt optimization, next-sentence prediction, inline completion, and per-session draft stashing for the DSH input box.

Multilingual writing, chunked long-form collaboration and continuity review, with on-demand DSH delegation discovery. Requires Python 3.9+ on PATH.

Settings page for the REA reverse-engineering tool gateway (rea-mcp-lite) that lives in the same repository: tick which tool bundles load into context, see the tool count and token cost of each bundle, hot-swap them without restarting DSH, and check this repository and REA for updates, including classifying the tools a new REA release added. Requires REA (rea-agents) and the gateway registered as the rea MCP server (see repo README).

Context governance for DSH: folds oversized tool results, collapses the tool surface and keeps the prompt cache warm, so long sessions stop getting more expensive.

Locks the model's reasoning, replies and tool-call descriptions to a chosen target language (Simplified Chinese by default) with system-prompt sections at the head, middle and tail of the prompt, offering three strength levels, an auto mode that follows the DSH interface language, a settings panel that shows the language currently in effect, and a sidebar quick switch.

Rewrites drafts with one model call or predicts the next reply when empty; independent tasks, safe draft replacement, result previews, and original-text recovery.

Cone crusher selection and crushing-plant design for MACKORN NH/NS single-cylinder hydraulic cone crushers: requirement-form intake, equipment selection, plant configuration, proposal generation, product-size simulation and field-data calibration. 21 tools, 6 skills, an MCP server, no runtime dependencies.