Plugins

Applies OpenQuantum branding, browser metadata, and PWA assets to the native DeepSeek Harness web UI.

Quantitative R&D toolkit for DeepSeek Harness — 59 tools across six domains covering market data, indicators, factor evaluation, walk-forward ML validation, risk (VaR/CVaR/drawdown/Beta with Kupiec POF), options, bonds, FICC and fund simulation, with an end-to-end PDAT→PET research pipeline.

Quantum Algorithms Best Practices

Expert-mode agent preset for DeepSeek Harness (v0.9.2, npm dsh-expert-mode, bilingual EN/ZH): a chief coordinator plus 17 domain-expert subagents with automatic task delegation. Features: taskboard scheduler (file-system task state machine pending/ready/running/done/failed, dependency DAG, atomic claim, retry, crash recovery), quality gates (5-stage pipeline for high-risk tasks: requirement clarity, implementation, verification, independent review, integration, with 2-round rework limit), Five-Anchor constraint (review/convergence/anti-drift/collaboration-check/resource-awareness per turn), Near-distance Guidance (identity/task/output template per expert), progressive disclosure (~28% token savings), expert persistence, inter-expert file message bus (direct P2P comm, zero coordinator relay), cross review, experience pool, fast-track for simple tasks, fault recovery with auto-retry. Experts: data analyst, copywriter, legal review, product manager, frontend, UI/UX, architect, social media ops, growth hacker, quant finance, finance, backend, DevOps, database, QA, security.

DeepSeek Harness (dsh) plugin for stock analysis, screening, and strategy backtesting across A/HK/US/JP/KR/TW markets

"Single-Core Writing" plugin for DeepSeek Harness: Thoroughly tool-streamlined and output-hardened for quantized small models, suitable for running long-novel pipelines locally with on-device models.

A-share stock analysis and screening skill: real-time quotes, technical timing scores, buy/sell signals and a managed watchlist.

RigorQuant preset + skill pack: unattended walled multi-agent research for empirical and computational mathematics (economics, finance, portfolio), with a four-part pre-implementation check battery and a jacobian/Lean escalation lane.

Bayesian stock prediction plugin that fuses MACD, RSI, moving-average, volume and other technical signals into an up-move posterior probability with multi-horizon comparison, trend-state classification and position risk metrics for A-share and US stocks.

DSH tools for reproducing and validating the ml-quant-trading protocol v1 CPU benchmark, then drafting an issue-ready report. Requires a local clone of initial-d/ml-quant-trading (set as the workspace or repoPath) plus Python and PyTorch; this plugin does not fetch the repository or install its dependencies.

A股 / 港股量化工作台：自选股、K线图、公司数据、策略配置与回测。 · A-share & Hong Kong stock quant workbench for DeepSeek Harness.

Financial quantitative workbench and stock ticker: floating ball and drawer panel, real-time A/HK/US/global market quotes, multi-period K-lines and 5-day intraday charts, depth order book, capital flow, sector heat map, and AI investment research.

Route OpenRouter requests through a configured provider list and quantization cap, injected as provider.only/order with allow_fallbacks, plus provider.quantizations.

Command-triggered red/blue adversarial review for ANY plan (not quant-specific). /plan-adversarial <plan>: arms a gate that requires the agent to derive two independent subagents (red=attack, blue=defend), have them converge on a consensus plan with NO th

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

DeepSeek Harness plugin that exposes QBoson Kaiwu Ising solving as an agent-callable solve_ising tool.

A plugin for dsh: quantify every dsh Agent interaction as a visualized performance metric and automatically sync it to WakaTime -- use data to showcase your productivity with Agent.

Ranks OpenRouter providers for the current DSH model by quantization, speed, cache-heavy price, context, and uptime, with one-click switching to help reduce token costs.

Community DSH plugin for constrained, auditable diePi research backtests