Plugins

Connecte DSH à Timeline Studio pour l'inspection de projets `.timeline`, les aperçus d'édition sémantique, les éditions transactionnelles et le rendu MP4 vérifié.

Analyse les images de conversation via des invites spécifiques au mode (caption, UI, document, grounding, topologie, etc.) et injecte des preuves structurées avec des primitives de coordonnées (boîtes, points, références) sous forme de texte, avec mise en cache au niveau de la session pour réutilisation lors du replay et de la compaction.

Console de service de développement local pour DSH : répertorie tous les services d'écoute sur la machine et permet aux utilisateurs d'inspecter, d'arrêter ou de redémarrer en toute sécurité les services démarrés pendant le développement.

Relie le serveur MCP Python harmonyos-dev-mcp à DSH : découverte d'appareils HarmonyOS, build/install/run/uninstall, automatisation UI (click/input/keys/swipe/screenshot), inspection E2E de l'arbre UI, et analyse de logs/crashes en 18 outils mcp__harmonyos__*.

Unifie chaque joint d'extensibilité DSH derrière un DSL déclaratif : un service d'exécution `fabric`, des outils `fabric_extend`/`fabric_inspect` et une page de paramètres de graphique de capacités.

Discipline de développement spec-driven pour dsh : cinq compétences keel (anchor/spec/probe/build/audit), trois outils de revue et six modèles de spec imposent les specs avant le code, la vérification des hypothèses et le contrôle du périmètre.

Tests d’acceptation indépendants dans un navigateur pour les livrables d’agent : une spec JSON en entrée, un verdict basé sur un vrai Chromium en sortie.

Inspection des .uasset d’Unreal Engine (resolve/registry/load) et édition limitée de nœuds Blueprint via le Python UE intégré.

Un plugin de méta-agent natif au modèle pour DeepSeek Harness : le modèle synthétise à l'exécution des workflows spécifiques à la tâche à partir de cinq patrons de workflow agentique (chaînage de prompts, workers parallèles, routeur, superviseur, boucle d'évaluation) et coordonne les outils et les sous-agents.

Cross-session memory as plain Markdown per workspace: weighted injection, bi-temporal invalidation, TTL review with stale markers, credential redaction, error and session retrospectives, SOP skills, and approval-gated self-improvement proposals.

Let a DSH model inspect and operate Windows applications through an installed Codex Computer Use runtime, with per-session app approval.

Visual sketchboard for DeepSeek Harness: draw with Excalidraw, let AI agents inspect and annotate sketches, and review proposed edits.

Read Windows service state from WSL: list services, or inspect one by name with its dependencies, logon account and binary path.

Read-only Skill inspector for DeepSeek Harness. It shows which Skills a conversation actually loaded — from observed loading evidence only — and which Skills the current environment can discover, and gives each Skill a detail page that keeps its declaration apart from its run: the body split into structure, declared workflow and progressive disclosure, the run logic read from local receipts, and the per-step evidence behind each claim. Evidence is never promoted into a conclusion — a step with no observed evidence is reported as such, not as executed or not executed — and the run's instruction hash is shown beside the current definition as match, mismatch or unavailable. It also reads the body, offers a Chinese reading version, and can clone a Skill into a local copy. It never writes back to the source Skill and never runs its scripts.

Auto-monitor and reveal active file operations and changes in DeepSeek Harness (DSH) right sidebar

Cordis-native, evidence-driven full-stack engineering workflow: inspect-first planning, explicit verification evidence, and approval-aware gating for sensitive operations.

Task-to-skill pairing meta-skill with a laziness ladder for minimal loading, a user-editable routing table, and a quarantine, SkillSpector scan, human-approval install flow.

DSH settings section for managing a local Sirchmunk knowledge backend: graph inspection, cluster management, audit history, runtime metrics, and privacy controls.

One MCP tool instead of every schema you own — a lazy capability broker that cuts resident tool context by a measured 94.4%. Search, inspect, enable and call MCP servers and skills on demand.

Browser patrol & website inspection plugin for DeepSeek Harness (DSH). Teach once, replay deterministic runbooks with managed Chromium, screenshots, credentials, checkpoints and selector recovery.

Coding retrospect distilled memory for DSH: distills rejected solutions, pitfalls and constraints from each turn via a local LLM, tags them into MemOS (type:rejected_solution), and boosts weighted recall on coding tasks via agent/pre-step.

WebUI panel for the dsh-guardian watchdog: start, stop and inspect two macOS LaunchAgents with status, log tail and explicit failure states.

Image generation workbench and agent tool for DSH: one tool (ark9_generate_image) paints text-to-image and image-to-image via any OpenAI-compatible API. Channels are entirely user-configured - no bundled endpoint, you bring your own baseURL, key and model name (manual input supported). Agent-initiated generations are approval-gated by default: requests queue in the floating panel and nothing bills until you approve; approval is non-blocking (the agent replies "awaiting your approval" immediately, and results, denials or timeouts are injected back so the agent reports them in the next turn). Floating glass workbench with five tabs (Generate / Approvals / Prompts / History / About), aspect-ratio grid with quality-budget + 16px-alignment sizing, transparent background, batch up to 10 images as aggregated sub-tasks, prompt library with custom JSON sources fetched through a host proxy, persistent generation logs with one-click retry, multi-channel aggregation with per-channel model fetching, bilingual UI (zh/en), stroke-SVG icon set, config import/export. Registers as a Better Sidebar tab when dsh-better-sidebar is installed; stacks above the dsh-cua FAB when both are present.

Three independent language channels for model output — tool-call descriptions (every tool-call card title, including run_code in PTC-style presets), thinking, and replies — each set to follow the web GUI language, force a specific language, or turn off (default: only descriptions, following the GUI). One global runtime-context prompt directive on the host; the client half reports the GUI language; a Settings plugin card switches the modes.