Plugins

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.

Codex-style turn folding for the DSH Web UI: keeps final answers visible, groups consecutive reasoning and tool activity, and shows configurable live duration, tool-call, and token metrics with multilingual summaries.

Make any project AI-agent-ready in one command. Adaptive tech stack detection (7 languages × 14 frameworks × 61 components), auto-generates AGENTS.md, docs skeleton, CI/CD, and testing infrastructure. 一句话让任何项目具备 AI 开发能力。

Lossless context folding for DeepSeek Harness: the oldest span is replaced by a brief, and the originals stay in the session log.

Codex-style tool call folding, collapsing multiple tool calls into a single line.

DeepSeek Harness plugin that aggressively saves tokens without affecting task quality: lossless head/tail truncation of tool results (default), grep-result folding, pressure-triggered dsh-compaction-basic delegation, and an LLM-summary CompactionEngine subclass for the aggressive mode. Saves 97.6% of input tokens on an 8-scenario synthetic suite, with all reductions routed through dsh event flow so model-visible = logged stays intact.

Provides Codex-style session message folding for DeepSeek Harness.

Plugin development toolkit for DeepSeek Harness with scaffolding, contract checks, plugin vetting, impact analysis, and upstream compatibility tracking.

Project documentation scaffolding skill: question-driven setup of project standards, AI-collaboration workflows, and memory discipline across DSH, Reasonix, Claude Code, and Codex.

DeepSeek Harness Web plugin that keeps image-bearing Tool results visible after Compact transcript folding

DSH plugin: an unattended AI software team. Feed it a bare Linux server, a set of secrets (env-var references only) and a git remote, and the plugin-driven AI team drives the whole project lifecycle — environment bootstrap, scaffolding, task decomposition

DSH project scaffold preset: strict 5-phase init runbook, engineering standards and six runnable starter templates (node-ts / react-vite / python / go / spring-boot / monorepo).

Education toolkit for DSH agents: lesson-plan skeletons by grade band, quiz scaffolding and answer-key validation, analytic rubrics, Anki/markdown flashcards, and readability levels.