Library Skill.md library
Each one names the GitHub repo it came from, so you can check exactly what it tells your AI to do.
274991 Skill.md files
--- name: openmaic description: OpenMAIC assistant for setting up, generating, and extending OpenMAIC. Use when the user wants to use OpenMAIC, generate a multi-agent interactive classroom, or build on / extend / customize OpenMAIC and its @openmaic/* SDK (secondary development, 二开) — covers Live Demo or local setup, startup modes, provider keys, classroom generation, and secondary development (forking, providers/storage/themes, routes, or the renderer/editor). ---
--- name: vibe-trading description: Professional finance research toolkit — backtesting (10 engines + benchmark comparison panel), factor analysis, Alpha Zoo (462 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 28 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata). ---
--- name: adr-hshare description: ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment. ---
--- name: akshare description: AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance. ---
--- name: alpha-zoo description: Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks "which alphas exist", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe. ---
--- name: ashare-pre-st-filter description: A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。 ---
--- name: asset-allocation description: Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules. ---
--- name: backtest-diagnose description: Diagnose failed or underperforming backtests, locate the root cause, and fix the issue ---
--- name: behavioral-finance description: Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies. ---
--- name: bottleneck-hunter description: Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders. ---
--- name: candlestick description: Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores. ---
--- name: ccxt description: CCXT unified crypto exchange library (100+ exchanges). Free public market data. Fallback when OKX is unavailable. ---
--- name: chanlun description: 基于缠论（缠中说禅）的形态识别引擎，使用czsc库自动检测K线分型、笔、中枢，并生成一买/一卖/二买/二卖/三买/三卖等买卖点信号。支持多周期分析和形态分类（3/5/7/9/11笔形态）。 ---
--- name: commodity-analysis description: Commodity analysis (oil supply-demand balance / gold pricing / copper as an economic predictor / inventory cycles / futures premium-discount structure / seasonality), generating directional commodity signals. ---
--- name: convertible-bond description: A股可转债分析——转股/纯债/期权三维估值、下修/强赎/回售博弈、双低策略与转债轮动选债框架 ---
--- name: corporate-events description: 公司事件驱动分析：并购套利价差计算、大股东增减持信号、股权激励解读、定增配股影响评估、A股ST/退市预警 ---
--- name: correlation-analysis description: Correlation and cointegration analysis — co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation ---
--- name: correlation-regime description: Correlation-regime detection and crisis attribution — edge-density regime states with hysteresis, causal (no look-ahead) smoothing, regime-aware exposure context, first-mover crisis attribution with honest NAME / MACRO / AMBIGUOUS / ABSTAIN verdicts, and a correlation-rewiring leaderboard that catches slow bleed-outs ---
--- name: credit-analysis description: 固收与信用分析：信用债评级、利差分析、违约风险评估、城投债研究、可转债定价与策略。 ---
--- name: cross-market-strategy description: Write signal_engine.py for portfolios spanning multiple markets (A-shares + crypto, equity + forex, etc.) ---
--- name: crypto-derivatives description: Crypto-derivatives strategies — perpetual funding-rate arbitrage, futures term-structure contango/backwardation trading, and option volatility-smile / Greeks analysis. ---
--- name: data-routing description: The single ROUTER for every data need. Load this skill BEFORE any backtest, data-fetch, or research task to pick the best available source/tool, honour auth (env) requirements, and avoid ban-risk providers. ---
--- name: deep-company-series description: Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Use when the user wants textbook-level depth on one company for public publishing (a single research report or earnings note is NOT this — use investment-research / earnings-review instead). ---
--- name: defi-yield description: DeFi yield analysis and optimization — lending rates, LP yields, staking returns, yield farming strategies, risk-adjusted yield comparison, and protocol-level sustainability assessment. ---
