Plugin

Governs multi-model access, monthly credits, manual/quality/credit/auto routing, failure rerouting, and usage auditing for DSH.

Rule-based multi-provider model routing for DeepSeek Harness with pre-first-token failover, cooldown circuit-breaking, usage accounting, and a status API.

Branching surveys in the web GUI: the ask_survey tool with per-option routing, hover insights, Mermaid diagrams, quick one-click answer templates, and resumable banked progress.

User-defined agents with automatic routing, model failover, and squad orchestration: six model tools (agent_dispatch / agent_followup / agent_list / agent_squad / agent_import_skill / agent_upsert), no built-in agents, a native right-tab panel (Overview / Agents / Squads / History), and a floating action ball.

Explicit model routing for Main, Subagents, web_search, and image generation, plus a catalog-driven Composer and Plan Review picker with Model, Effort, Context, Fast, and Thinking variants.

A permanent DSH plugin that bundles a developer workbench, an in-session conversation tree, and a plugin manager: build and run an LVAL project, browse its code, and keep per-program version snapshots with rollback; edit, retry, or branch any message in place, with message micro-versions; a feature manager plus a global-plugin library whose entries each carry an enable tier; cross-dialogue memory, one-shot temporary chats, and model collaboration; five network-permission tiers, mixed-model routing, a security audit, and browser control through a companion extension.

DeepSeek-Harness plugin for multi-model orchestration with recursive task decomposition, dynamic routing, quality gates, failure recovery, and KPI reporting.

Branching surveys in the web GUI: the ask_survey tool with per-option routing, hover insights, Mermaid diagrams, quick one-click answer templates, and resumable banked progress.

Transparent multimodal routing for text-only models: every image in every model call is fully transcribed (verbatim OCR, data, uncertainty zones, injection-hardened) by your own multimodal understander, with focused re-look via vision_relook and automatic retry on image-related failures. No bundled endpoints, no borrowed logins.

Adaptive model discovery, evaluation, tiering, and subagent routing for DeepSeek Harness.

Dual-model auto-routing plugin: low-cost direct / high-cost upgrade with an escape-learning closed loop (wrong direct answers auto-learn fingerprints, force-upgrading the same fingerprint next time), persisted and interoperable with the Python ModelRouter.

Free-tier model routing for the DeepSeek Harness (dsh) - discover OpenRouter free models, apply the best free model as agent default, auto-rotate on rate limits; paid config untouched unless asked.

Delega gerarchica a sotto-agenti: routing dei modelli per ruolo con failover, sforzo di ragionamento per route, etichette che nascondono il modello, delega via fork e un editor visuale delle route.

Routing dei modelli a livelli per DeepSeek Harness: livelli think/build, delega alla visione (il modello di visione descrive solo le immagini; il modello di lavoro agisce), generazione di immagini e una guardia ad alto impatto.

Plugin out-of-tree per DeepSeek Harness: dona gli occhi a un modello di coding solo testo instradando le immagini a una rotta Qwen-VL (DashScope) tramite ctx.llm e restituendo testo.

Protocollo di collaborazione multi-agente indipendente dal progetto per DeepSeek Harness: guardie di ciclo che fermano i cicli di agenti fuori controllo, un formato di passaggio a sei campi, routing delle revisioni per livello di rischio e una sequenza fissa verify→commit→push→review→close. Una skill invece delle stesse regole copiate nelle istruzioni di ogni agente.

Meta-skill di abbinamento attività-skill con una scala della pigrizia per il caricamento minimo, una tabella di routing modificabile dall'utente e un flusso di installazione con quarantena, scansione SkillSpector e approvazione umana.

Router di modelli a due livelli per DeepSeek Harness — instradamento con LLM-Judge, catene di fallback multi-modello, failover con backoff esponenziale e orchestrazione a livello di task (adattamento DSH di pi-shift-router)

DeepSee: visione per DeepSeek Harness, instradamento multi-modello e autoverifiche visive con Gemini prima della consegna

Profili di prompt Markdown riutilizzabili per DeepSeek Harness con selezione del modello per turno, sostituzione degli argomenti e ripristino dello stato

Extra LLM provider routes for DeepSeek Harness: OpenCode Go with per-session x-opencode-session routing, and OpenAI Codex over ChatGPT-subscription OAuth.

Semantic tool routing, skill discovery, and typed System One decisions for DeepSeek Harness powered by TypeSafe Jev.

Skill routing for DeepSeek Harness: embeds skills and each user task with a local model, keeps the clearly-relevant skills with a gap-based selection rule, and auto-injects their full bodies into the prompt.

Large-image recognition for vision-exp models: lossless 800×800 tile recognition (smart/pipeline/full), local OCR with preprocessing & handwriting routing, optional multi-vendor GPU (DirectML/CUDA/OpenVINO) with auto CPU fallback.