Плагины

P0–P4 deterministic-first permission gate with permissive tier, learning sedimentation and approval-history UI; hard-deny credentials and protected paths, auto-allow internal read-only tools.

Forces periodic Engram memory recall and save reminders for DeepSeek Harness agents: injects a dynamic system-prompt context that reminds the model to load prior session memory at the start of a session, and to save new decisions/bugfixes/learnings every

Fail-closed network recovery, recommended answers, and safe unattended continuation 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.

Движок самообучающихся привычек для DeepSeek Harness: обнаруживает сигналы пользовательских правок, оценивает привычки недорогой моделью по порогу и фиксирует кандидатов за двухуровневым человеческим шлюзом.

Уютный офлайн-тренажёр словарного запаса для всей семьи: 1094 курируемых слова, интервальное повторение, 6 типов викторин, игры по правописанию и ленивый режим для большого экрана; открывается как боковая панель и работает автономно как один HTML-файл.

Агент глубоко совместного письма для DSH: изучение стиля письма пользователя по парам правок, поток «уточнение → план → написание разделов → red-team → читательская галерея», синтез проекта в отчёт (лабораторный отчёт / описание продукта / обзор / README), 41 инструмент MCP и web-UI «улучшить выделенное» с предпросмотром файлов внутри приложения.

Переписывает каждый запрос согласно выученному профилю промптов и автоматически обновляет привычки, правила против галлюцинаций и записи обучения.

Legal training workbench: three-pane academic UI that reads the "Minglü" (lawagent preset) learning profile (case dossiers, flashcards, wrong-answer book, notes) and sends instructions into a Minglü session with one click.

Coordinates bounded project recall from compatible memory and learning providers, with shared settings and provider lifecycle management.

Report stage — academic paper writing for asset pricing and machine learning research. Ships one skill.

Data stage — data discovery and quality assessment for asset pricing and machine learning research. Ships one skill.

Literature review stage — search, synthesis, and citation-chain tracking for asset pricing and machine learning research. Ships one skill.

Model training stage — end-to-end analysis, model training, and estimation for asset pricing and machine learning research. Ships one skill.

PPT stage — presentation generation (Beamer/Quarto) from the paper for asset pricing and machine learning research. Ships one skill.

Project-based learning coach: a subject-agnostic teaching engine plus swappable domain packs (Unity/C# included), shipped both as a DSH skill and as an installable bundle. The skill protocol enforces attempt-first teaching and graded hints; a zero-dependency validator rejects ability claims their evidence does not support and blocks stage acceptance while blockers are open.

dsh (DeepSeek Harness) plugins for project-level persistent context: session archive with a mechanical index, memory consolidation (CONTEXT.md + MEMORY.md), low-frequency skill autolearn, and automatic handoff.

Algorithm learning toolkit syncing Codeforces and AtCoder submissions, profiling weaknesses, generating AI training plans, and tracking spaced-repetition reviews across a 114-lesson algorithm curriculum.

Bridges the agent to a persistent OKF memory vault over MCP stdio: a Python server (SQLite FTS5 + Markdown) with 10 tools for searching and storing decisions, facts, learnings, conventions, profiles and sources.

Real Red Alert 2 AI battlefield for DeepSeek Harness with fog-safe control and post-match learning

Evidence-backed Skill self-evolution for DeepSeek Harness: redacted trajectory learning, quarantined proposals, arena validation, canary monitoring, automatic rollback.

Beginner starter pack for DeepSeek Harness: welcome flow, 0→1 learning path, scenario-based plugin recommendations and a self-check checklist, paired with the dsh-handbook-zh Chinese tutorial repo.