플러그인

설치된 DSH 플러그인 집합을 위한 읽기 전용 상태 클리닉: 로더 상태, 의존성 무결성, 버전 호환성, 설치 스크립트 위험, 중복, 패치 무결성을 모델 도구·설정 대시보드·JSON 리포트로 제공합니다.

DeepSeek Harness용 프로젝트 독립적 멀티 에이전트 협업 프로토콜: 폭주하는 에이전트 루프를 멈추는 루프 가드, 6필드 핸드오프 형식, 위험 등급별 리뷰 라우팅, 고정된 verify→commit→push→review→close 순서. 같은 규칙을 모든 에이전트에 복사하는 대신 하나의 스킬입니다.

SparkElf-maintained fork of dsh-quant: AI-native & DSH-native quant toolkit — 59 tools across 6 pluggable domains (data/alpha/ML/risk/execution), one end-to-end PDAT→PET research pipeline. Fork adds A-share-correct annualization, cross-sectional IC, and u

Track DeepSeek Harness's own releases and update in one click: reads the npm dist-tags and the GitHub changelog, shows every change between the running build and the target, grades the risk, then hands the install to a detached helper that backs the current install up with an atomic rename, runs npm, verifies what landed, relaunches, and rolls back when the new build does not come up.

Test-insight plugin that turns repository facts and change risk into evidence-backed test plans and isolated, reviewable test drafts.

Turns DSH permissions into 9 selectable presets (3 sandbox modes x 4 approval tiers), with rule plus LLM risk classification across four risk levels, password-gated approval for high-risk operations, and a JSONL audit log.

Semantic risk grading and progressive authorization: classifies tool calls into safe/risky/redline, asks before irreversible actions, auto-allows only approved-and-succeeded signatures.

DSH plugin security guard: 28-rule static scanner, risk scoring, whitelist/blacklist policy, local web dashboard and in-DSH risk popups.

Decides whether Python code is original or a derivative work: reports the longest identical expression span against an upstream tree, an exemption rationale per finding, and a risk list. Pure stdlib `ast` analysis — offline, no model calls.

Local and remote SSH terminals for DeepSeek Harness Web with three permission levels, human approval for risky AI commands, and encrypted credential storage.

Analyze the current branch diff and generate a Conventional Commits PR title, motivation/scheme/testing/risk description and a self-review checklist, write PR_DESCRIPTION.md, optionally open the PR via gh.

Role-based plugin recommender for DSH: asks the user's role (developer, designer, writer, researcher, ops, student...), recommends suitable plugins, and audits each with a local static security scan (dangerous code patterns + 0-100 risk score) and a reputation check (npm downloads / GitHub stars).

Local tool-call failsafe for DSH: blocks a fixed list of high-risk shell patterns and credential-file reads before execution, with a per-session hash-linked JSONL audit log, and an optional external Gateway for centralized policy.

Delete sessions of a DeepSeek Harness profile from the UI: header danger button + session-row menu item with a risk-consent dialog; host endpoint + tool delete the log, projection cache and workspace accounting. Works in web and the desktop client alike.

Adaptive task and risk routing, loop budgets, and progressive tool-result pruning for DeepSeek Harness

Codex-inspired managed approval for DeepSeek Harness: risk-based MCP review, one-time grants, explicit denials, and human fallback on reviewer failure.

催办风险灯（Urge Risk Light）— 根据流程节点停留时间与 SLA 对比批量判定红/黄/绿催办风险的 DeepSeek Harness 工具插件，含结果卡片与右下角悬浮组件

DeepSeek Harness 高峰期模型自动切换插件：高峰前自动切到指定替代模型，高峰后切回官方模型。· Auto-switch to an alternate model before DeepSeek peak hours and back to the official model afterwards. · 本插件由 AI 全权开发，作者无编程经验，请自行测试安全性；安装说明可能含 AI 幻觉。· Developed by AI; test at your own risk; install

Zero-interruption audit and fuse blocking for DeepSeek Harness: silently records every tool call with deterministic risk scoring, cumulative-risk bonuses, risk-level breakdowns and retention-based cleanup; blocks irreversible catastrophes (protected-path deletion, disk wipe, force-push to protected branches/refs, credential exfiltration), and renders a redacted /risk-guard operation bill with --since filtering.

Desktop (native) control for dsh as a stdio JSON-RPC bridge over a Python core: capture the screen, inject pointer/keyboard input, drive accessibility-tree actions, and gate high-risk operations behind required confirmations.