Plugins

Unofficial Command Code LLM provider: registers a `commandcode` route with a live model catalog and reasoning-effort support.

Adaptive reasoning slider skin that maps each model's available reasoning efforts onto a 0–30 visual intensity scale, with synchronized portraits, background, and interface colors.

Adds an animated DeepSeek Chan reasoning-effort slider to DSH's native model selector, following model-declared levels with a running animation that speeds up while dragging.

Drags the official reasoning-effort row into a continuous track: blue-to-violet fill, a starfield on the higher levels, and the official value text tinted to the current level. Off stays gray; no official text is rewritten.

Desktop feature aggregate that installs the DSH Web UI collection, dshmarket, Codex Connect, and reasoning-effort controls at pinned versions.

🌹 Save every repetitive, tiresome templated prompt — one mouse click injects it into the composer and spares you the effort.

ChatGPT account sign-in for DeepSeek Harness through a Codex browser OAuth flow with no API key and no workspace selection: subscribes the login to pi-ai's Codex provider, lets you set the global default Codex model and reasoning effort for new sessions, refreshes the model list from the signed-in account's own catalog endpoint, and shows the five-hour and weekly usage windows in the composer.

Per-model reasoning-effort and input-modality configuration for third-party providers: suggestions fused from a model knowledge base and raw /models endpoint evidence with confidence labels, edited inside the official Models page card.

One DSH plugin that aggregates the models of WorkBuddy (personal multi-account, enterprise and the WorkBuddy AI international edition), Qoder CN and Trae CN into a single model picker with a shared conversation history, live credit multipliers, per-model reasoning effort, the largest context window the international edition declares, and daily check-in where the channel offers one.

Configure reasoning-effort levels and sub-agent defaults for custom DSH models.

ChatGPT account sign-in for DeepSeek Harness through a Codex browser OAuth flow with no API key and no workspace selection: subscribes the login to pi-ai's Codex provider, lets you set the global default Codex model and reasoning effort for new sessions, refreshes the model list from the signed-in account's own catalog endpoint, and shows the five-hour and weekly usage windows in the composer.

Let Your Ideas Flow — Tydora is a modern desktop Markdown editor combining WYSIWYG editing, bidirectional links, mind maps, and an infinite canvas — empowering deep thinking and effortless expression.

Auto thinking-effort routing for dsh: a locally fine-tuned 7-question classifier (ModernBERT, 842MB, <1s CPU inference) picks low/high/max for no-intent messages; in-session retries escalate the tier (low to high to max) under exclusion constraints; the judge service fails safe to low if unreachable. A tier chip in the input bar shows the live tier and the last 20 judgments. All inference is local - task text never leaves the machine.

Claude Code Desktop-style theme for DeepSeek Harness (DSH) Desktop 0.2.0-rc.2 on macOS and Windows, with compact sidebar and chat layouts, light and dark themes, model and reasoning-effort controls, and an interactive pixel whale with spout, hop and tail-wag reactions on new sessions.

A Claude Code-style reasoning-effort slider for the DSH Web UI: stepless drag, snap-on-release, a WebGL fire trail, and real, working thinking-effort control for any custom third-party model or provider.

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.

Replaces the model button in the DSH composer with its own two-level panel (provider list, then a searchable model list) built around a continuous draggable reasoning-effort slider: blue-to-violet fill, nebula and star particles on higher levels, and a distinct color per level. Picking a model keeps the panel open and moves the slider to that model's middle level. The implementation started from Microqian2th/dsh-codex-effort-slider 1.0.0 (credited in the README); the panel, the seat takeover, the offline test suite and the docs are new here.

Match your models and token budgets to each role, then let DSH delegate specialized subagents by task type. Covers architecture analysis, UI/UX, code implementation, codebase exploration, and documentation research, with automatic setup plus per-role reasoning effort and tool permissions.

Liang Saint Slider as the DeepSeek Harness model + thinking-effort selector.

Kiro model provider with Builder ID, Identity Center, Google, GitHub and credential-import sign-in, automatic token and profile refresh, account usage, live model discovery, selectable reasoning efforts, streaming tool calls, provider stop-reason and context-overflow handling, and token accounting that uses Kiro's exact counters when it sends them and otherwise scales the context-usage percentage it does send by the model's advertised window.

A Codex-like effort switcher for Deepseek Harness.

Per-round thinking-level (reasoning_effort) control for DeepSeek Harness: pick Auto and it schedules low/high/max from the recent tool-call history, or fix a wire level (off/on/minimal/low/medium/high/xhigh/max) manually, with custom wire mapping, a model-aware capability guard, and context-window presets (64K–1M); its per-model capability card pairs with a separate plugin, [dsh-llm-openai-completions](https://github.com/drscrewdriver/dsh-llm-openai-completions) (installable from dsh-market), so custom OpenAI-compatible gateways (vLLM / LM Studio / self-hosted) can try reasoning — the card writes llm-pi-ai capabilities, the adapter drives the wire (compat.thinkingFormat), and this plugin auto-maintains the takeover list.

Settings page for per-model capabilities: reasoning-effort levels with their wire spellings, input modalities, and token capacities, written as declarations or sparse catalog overrides.

Codex-style reasoning-effort slider embedded in the model selector.