Plugins

Guthaben-/Nutzungsanzeige im Composer-Dock für DeepSeek, Kimi Coding, OpenCode Go, Zhipu, MiniMax, OpenRouter und OpenAI Codex — providerbasiert, Abfrage alle 2 s, 5-Minuten-Cache.

LoongPort DeepSeek Harness (dsh) Cordis-Bundle und Setup-CLI

DeepSeek Harness Plugin: Importiert Modelle mit Fähigkeiten aus einer OpenAI-kompatiblen /v1/models-Liste (Gateway-Fähigkeitserweiterungen) und bearbeitet modellspezifische Fähigkeiten (Denken, Vision) auf der Einstellungsseite.

Vision-Anbieter-Route, die angehängte Bilder über ein konfigurierbares Modell (über 15 OpenAI-kompatible und Anthropic-Anbieter) in Text transkribiert, während DeepSeek weiterhin antwortet.

Vision-Bridge für textonly-DeepSeek-Routen, der angehängte und lokale Bilder über konfigurierbare OpenAI-Responses-, Chat-Completions- oder Anthropic-Messages-Endpunkte analysiert, während bildfähige Routen nativ bleiben.

Vision-Toolkit für textonly DeepSeek: modellaufrufbares `vision`-Tool, Wrapper-Adapter für deepseek/opencode-go (v4 flash/pro), Antigravity-IDE-Kontingent (default, flash/pro) / jeden OpenAI-kompatiblen VLM / Gemini / lokale Ollama-Kanäle, Evidenzspeicher mit Kompaktierungs-Rehydration, Content-Hash-Cache und ein zweisprachiges Client-Panel.

Ein sub2api-Gateway mit DeepSeek Harness verbinden: OpenAI-kompatible Multi-Provider-Routen (OpenAI / Claude / Grok / Gemini) hinter einer Basis-URL, mit Modellerkennung pro Schlüssel, Nutzungsabfrage und globalen Vision-/Bild-Tools.

Nutze deine ChatGPT/Codex-Abonnements in DSH, mit Unterstützung für Bildgenerierung und Websuche.

OpenAI-Codex-OAuth-Provider und native Einstellungs-UI für DeepSeek Harness

Verbindet DeepSeek-Harness-Agenten mit authentifizierten Codex-App-Server-Sitzungen

Persönliches WeChat-ClawBot-Plugin für DeepSeek Harness (DSH): OpenClaw übernimmt den WeChat-Kanal (Login per QR-Code, Nachrichten senden/empfangen), DSH übernimmt das „Gehirn“ — beide werden über ein OpenAI-kompatibles Protokoll verbunden.

Bringt Gedächtnis, Skills und Konfiguration von OpenAI Codex per Bridge in DeepSeek Harness

Im Composer angehängte Bilder werden von einem OpenAI-kompatiblen Vision-Modell in Text umgewandelt, bevor sie textbasierte DeepSeek-Modelle erreichen.

只填 Key 的服务商预设：50 家渠道为 DSH 一键写入官方 llm-pi-ai 路由与凭据（插件），并可写入腾讯 WorkBuddy、OpenCode（Key 走其 auth.json）与 cc-switch 的配置（CLI）；核心为纯 JS，可被更多应用的外壳复用。

Keeps a project's documentation in step with its decisions on DeepSeek Harness: the same append-only decision log and derived design snapshot, kept fresh in-process instead of by a hook process.

Drive DSH from anything that speaks HTTP: 47 routes (REST + SSE) for workspaces, sessions (create/prompt, live SSE generation, history, stats, todos, cancel, ask-user Q&A), models and the global default model, providers, agent presets, settings namespaces, skills, and workspace files including resumable chunked upload — plus an OpenAI-compatible /chat/completions, a built-in OpenAPI 3.0 spec and an interactive request page. It only injects the host webServer and tools, so it rides on the DSH webserver (default prefix /api/v1) or its own port with optional API-key auth, and it ships a skill that teaches the DSH agent to call the API.

OpenAI-compatible vLLM client (default :8000).

OpenAI-compatible client for llama.cpp / Unsloth Desktop (default :8080).

Fronts stdio MCP servers with three router tools (list_capabilities, describe_tool, call_tool) so schemas load on demand instead of riding every request; shapes every tool result through a deterministic output ladder that archives the original first (retrieve_spill reads it back); prices tool groups in tokens and can hide built-in groups per agent. Bridges to a standard-library Python router, so Python 3.9+ is required.

Compaction fallback for compaction-basic: when its LLM summary throws, comes back empty or cannot fit the window, the span is compiled deterministically with a seq pointer on every line (vendored dsh-compaction-instant compiler). Adds recall and search tools, /recall and /context commands, an idle trigger at 45% of the window and an on-disk span archive. The engine mounts as one row in the agent preset (docs/dsh-integration.md).

Pool AI provider accounts per platform and route them through a local loopback proxy that bridges the OpenAI, OpenAI-Responses, Anthropic and Gemini protocols, then write the proxy into Codex, Claude Code, Gemini CLI, Grok, OpenCode, OpenClaw and Hermes config files with snapshot-and-rollback safe writes; registers no agent tools.

Registers the imagegen skill on the skill registry, so generating and editing raster images needs no hand-copied files under $DSH_HOME/skills. The agent drives a bundled image_gen.py CLI against any OpenAI-compatible GPT Image endpoint for text-to-image, reference-image editing, and transparent-background cutouts.

Per-reply read-aloud with a multi-provider settings page — MiniMax or any OpenAI-compatible /audio/speech endpoint, with voice, emotion, speed and model selection plus an auto-read toggle.

Live token cost for every chat in the DSH Web UI: the chat, its subagents and the whole session tree, priced from a bundled models.dev snapshot of seven providers and 139 priced models, with cache read and write rates wherever the provider publishes them and DeepSeek from its own peak and off-peak table. Every finished answer carries its own price in the transcript, and a chat that is not currently open is priced from the log instead of disappearing. Spend is costed per interval from an append-only JSONL ledger in the project folder, so logged work keeps the tariff it was billed at. On top of it: budget-aware work plans packed against a money limit, routing scenarios that show which models are worth connecting, a generated plan document in English, Chinese or Russian, and a warning when spend belongs to no plan unit.