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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.

DeepSeek Harness용 자기 학습 습관 엔진: 사용자 수정 신호를 감지하고, 임계값에서 저비용 모델로 습관을 판정하며, 2단계 사람 게이트 뒤에서 후보를 확정합니다.

온 가족이 함께하는 아늑한 오프라인 어휘 학습기: 1094개의 큐레이션된 단어, 간격 반복, 6가지 퀴즈 유형, 스펠링 게임 및 레이지 대화면 모드; 사이드바 패널로 열리며 단일 HTML 파일로 독립 실행됩니다.

DSH용 심층 협업 집필 에이전트: 편집 쌍에서 사용자별 문체 학습, clarify→outline→section-writing→red-team→reader-gallery 흐름, 프로젝트→보고서 합성(실험 보고서 / 제품 소개 / 설문 / README), 41개 MCP 도구, 앱 내 파일 미리보기가 있는 웹 선택-개선 UI.

학습된 프롬프트 프로필에 맞춰 각 요청을 다시 쓰고, 습관·환각 방지 규칙·학습 기록을 자동으로 갱신합니다.

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.