cafci

Open-source collection of skills compatible with the SKILL.

审核状态：已审核Quality 80Security 80

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项目 README

来源文件：README.md

抓取于 2026年7月29日

skills

Open-source collection of skills compatible with the SKILL.md standard. Developed for the AI courses at UCEMA.

Data — Global

Skills that extract market data (quotes, historical, fundamentals, screener, etc). Multi-country / global coverage: US, Europe, Asia, and global aggregators.

#	Skill	Type	Cost	API Key	Instruments
1	FRED Macro	API	✓ Free	Required	macro-data
2	Alpha Vantage	API	Freemium	Required	stocks, forex, commodities, fundamentals
3	Yahoo Finance	API/Scraper	✓ Free	-	stocks, forex, options, futures, fundamentals
4	SEC Data	API	✓ Free	-	fundamentals
5	Alpaca Data	API	✓ Free	Required	stocks, options
6	Finnhub	API	Freemium	Required	stocks, forex, fundamentals
7	Finviz	Scraper	✓ Free	-	stocks, fundamentals, screener
8	Macrotrends	Scraper	✓ Free	-	stocks, fundamentals
9	MarketScreener	Scraper	✓ Free	-	stocks, fundamentals, screener
10	MarketWatch	Scraper	✓ Free	-	stocks, options, futures, fundamentals
11	CompaniesMarketCap	Scraper	✓ Free	-	stocks, etfs
12	SimplyWallSt	API/Scraper	✓ Free	-	stocks, fundamentals
13	EarningsWhispers	API	✓ Free	-	fundamentals
14	Barchart	Scraper	✓ Free	-	stocks, futures, fundamentals
15	Nasdaq Data	API	✓ Free	-	stocks, options, fundamentals, etfs
16	CBOE Data	API	✓ Free	-	stocks, options, commodities, futures
17	Investing.com	Scraper	✓ Free	-	stocks, forex, commodities, options, futures, etfs, screener, fundamentals
18	Morningstar	API	✓ Free	-	screener
19	TradingView	API	✓ Free	-	stocks, etfs, bonds, options, futures, forex, crypto, screener, fundamentals
20	Google Finance	API	✓ Free	-	stocks, etfs, options, fundamentals
21	History of Market	API	✓ Free	-	indices history, sectors, macro

Data — Regional (Argentina)

Skills specific to the Argentine market: BCRA, BCBA, MAE, CAFCI, etc.

#	Skill	Type	Cost	API Key	Instruments
1	BCRA Macro	API	✓ Free	-	macro-data
2	Data912	API	✓ Free	-	stocks, forex, bonds, options, etfs
3	MAE	API	✓ Free	-	bonds, macro-data, forex
4	BYMA	API	✓ Free	-	stocks, bonds, options, etfs
5	CAFCI	API	✓ Free	-	etfs
6	INDEC	API	✓ Free	-	macro-data

Brokers

Skills that allow executing real trades (orders, positions, account) on broker accounts.

#	Skill	Type	Country	Instruments
1	Alpaca Trading	REST	USA	stocks, options
2	Primary	REST+WS	Argentina	futures
soon	Tradier
soon	Interactive Brokers
soon	Invertironline
soon	Portfolio Personal

Tools

Calculation and financial support tools (backtesting frameworks, screeners, options and greeks calculation, etc).

#	Skill	Concepts
1	Option pricing	Black-Scholes, Binomial CRR, Trinomial, Monte Carlo (antithetic), Longstaff-Schwartz, Bjerksund-Stensland/BAW (American), Heston (smile), Bates (smile + crashes), greeks (delta/gamma/vega/theta/rho), implied vol, P(ITM) and P(Profit). 15 CLI modes. Flat Python + numpy, 419k options/sec (BS)
2	Backtesting	Academic backtesting framework. 30+ risk/performance ratios, 10 classes of indicators, event-driven engine with 8 built-in strategies, Markowitz optimization, forward-looking simulation (Johnson SU + t-Copula), walk-forward CV, stress testing, fundamental analysis (Altman Z, Piotroski, DuPont). Flat Python + numpy, 33 checks validation suite.
3	Portfolio	Portfolio construction and optimization: Markowitz (scipy.optimize + Monte Carlo frontier), Black-Litterman (CAPM inverse prior, absolute/relative views, Idzorek omega, Bayesian posterior), HRP/HERC/NCO (hierarchical clustering, risk parity, nested clustered optimization with constraints). All flat numpy + scipy, no Riskfolio-Lib/PyPortfolioOpt required. 12 CLI modes, verified against real yfinance data matching library outputs exactly.

Installation

Install selected skills globally:

npx skills add gauss314/skills -g

Install all skills globally:

npx skills add gauss314/skills --all

Individual skills can also be installed with the commands in the tables below:

Data — Global

Skill	Command
FRED Macro	`npx skills add gauss314/skills --skill fred-macro`
Alpha Vantage	`npx skills add gauss314/skills --skill alpha-vantage`
Yahoo Finance	`npx skills add gauss314/skills --skill yahoo-finance`
SEC Data	`npx skills add gauss314/skills --skill sec-data`
Alpaca Data	`npx skills add gauss314/skills --skill alpaca-data`
Finnhub	`npx skills add gauss314/skills --skill finnhub`
Finviz	`npx skills add gauss314/skills --skill finviz`
Macrotrends	`npx skills add gauss314/skills --skill macrotrends`
MarketScreener	`npx skills add gauss314/skills --skill marketscreener`
MarketWatch	`npx skills add gauss314/skills --skill marketwatch`
CompaniesMarketCap	`npx skills add gauss314/skills --skill companiesmarketcap`
SimplyWallSt	`npx skills add gauss314/skills --skill simplywallst`
EarningsWhispers	`npx skills add gauss314/skills --skill earningswhispers`
Barchart	`npx skills add gauss314/skills --skill barchart`
Nasdaq Data	`npx skills add gauss314/skills --skill nasdaq-data`
CBOE Data	`npx skills add gauss314/skills --skill cboe-data`
Investing.com	`npx skills add gauss314/skills --skill investing`
Morningstar	`npx skills add gauss314/skills --skill morningstar`
TradingView	`npx skills add gauss314/skills --skill tradingview`
Google Finance	`npx skills add gauss314/skills --skill google-finance`
History of Market	`npx skills add gauss314/skills --skill historyofmarket`

Data — Regional (Argentina)

Skill	Command
BCRA Macro	`npx skills add gauss314/skills --skill bcra-macro`
Data912	`npx skills add gauss314/skills --skill data912`
MAE	`npx skills add gauss314/skills --skill mae`
BYMA	`npx skills add gauss314/skills --skill byma`
CAFCI	`npx skills add gauss314/skills --skill cafci`
INDEC	`npx skills add gauss314/skills --skill indec`

Brokers

Skill	Command
Alpaca Trading	`npx skills add gauss314/skills --skill alpaca-trading`
Primary	`npx skills add gauss314/skills --skill primary`

Tools

Skill	Command
Option pricing	`npx skills add gauss314/skills --skill option-pricing`
Backtesting	`npx skills add gauss314/skills --skill backtesting`
Portfolio	`npx skills add gauss314/skills --skill portfolio`

Structure

Each skill is a directory following the Agent Skills standard structure:

.
├── skills/
│   └── <skill-name>/
│       ├── SKILL.md           # required: frontmatter + instructions
│       ├── references/        # optional folder: complementary documentation
│       ├── scripts/           # optional folder: executable example scripts
│       └── assets/            # optional folder: templates, configs
├── README.md
├── LICENSE                    # MIT
└── .gitignore

How it works

The command npx skills add gauss314/skills --skill bcra-macro installs:

SKILL.md → loaded into the agent context when you use /bcra-macro
references/ → complementary documentation (catalogs, references)
scripts/ → executable scripts (Python, Bash, etc)
assets/ → templates, configs, auxiliary files

Files in references/, scripts/ and assets/ are loaded only when the skill needs them, following the Progressive Disclosure principle to optimize tokens.

SKILL.md format

---
name: skill-name
description: Short description (<100 characters)
license: MIT
---

# Skill Name

Instructions for the agent and SKILL content...

Required fields: name, description
Optional fields: license, metadata, disable-model-invocation

Installation

# Install a specific skill in the project
npx skills add gauss314/skills --skill bcra-macro


# Install all skills from the repo into the project
npx skills add gauss314/skills --all


# Global install (accessible to any of the user's projects)
npx skills add gauss314/skills --skill bcra-macro -g

Skill Description

Data — Global

FRED Macro: 840,000+ macroeconomic series from the Federal Reserve (GDP, CPI, rates, employment, M2, VIX, treasuries, mortgages). Historical series since 1996 with search by name/category, tags, releases, and daily/monthly/quarterly/annual frequencies. Official free API.

Alpha Vantage: 20+ global exchanges, 200,000+ tickers (stocks, forex, crypto, commodities). Freemium with 25 calls/day free. Covers TIME_SERIES_INTRADAY/DAILY/WEEKLY/MONTHLY, 50+ technical indicators, fundamental overview, FX rates, crypto ratings, commodities (metals, energy, grains).

Yahoo Finance: global coverage — stocks, ETFs, crypto, forex, bonds, indices, options, futures, fundamentals and news. Quotes delayed 15min, daily/intraday OHLCV, financial statements, options chains, futures on commodities and indices, news, analyst recommendations, insider transactions. Unofficial endpoints via direct HTTP requests (no wrapper).

SEC Data: all companies filing with the SEC (10K, 10Q, 8K) — US public companies + internationals using IFRS. Data from the last 5+ years with quarterly + annual. Concept-level data navigable. Supports US-GAAP and IFRS with automatic concept mapping. Income/balance/cashflow statements in JSON/CSV from XBRL facts.

Alpaca Data: 5,000+ US stocks + crypto + options with historical and real-time data. IEX feed. Snapshots, bars (OHLCV), trades, quotes. Multi-asset with symbol normalization. Generous free tier for historical data, real-time with limit.

Finnhub: 32 free REST endpoints with US/EU/UK coverage + forex + crypto. Quotes, company profile, financials, earnings calendar, recommendations trends, price targets, insider transactions, company peers, ESG scores, news, economic data, WebSocket. Freemium 60 calls/min.

Finviz: 8K+ US stocks (NYSE, NASDAQ, AMEX) + Canada. Fundamental data (P/E, EPS, PEG, margins), technical (RSI, MACD, SMA, ATR), insider trading, institutional ownership, news, online screener with filters (market cap, P/E, sector, performance). Scraper of the Finviz site.

Macrotrends: ~6,500 tickers from US markets (NYSE, NASDAQ, AMEX), including international ADRs from +30 countries. Financial statements, ratios, employee count with 15+ years of historical data. Income/balance/cashflow with 5-30 years of history, profitability ratios, debt, margins, per-share data, segment data.

MarketScreener: 20K+ global stocks including ADRs. Quote, profile, financials (income/balance/cashflow), valuation, analyst consensus, news, earnings transcripts list, insider trading, shareholders, corporate governance, earnings calendar, recommendations, ownership structure. Global multi-country coverage.

MarketWatch: US stocks and global ADRs. Quotes, financials, SEC filings, analyst estimates, options chain, futures, historical OHLCV. Point-in-time data per ticker, comparable companies panel, screeners by category. Futures data on indices, commodities, rates and currencies.

CompaniesMarketCap: global financial rankings (market cap, earnings, revenue, employees, P/E, margins, assets, debt, cash), historical marketcap of stocks and ETF holdings. Global coverage — top companies by any metric, historical capitalization, ETF holdings. Uses native CSV download of the site.

SimplyWallSt: 120,000+ global stocks in 106 exchanges. Snowflake scores (1-5 stars: value, income, health, past, future, management), valuation vs sector, dividend history (19+ years) and projected, financial health score, insider transactions, price targets, P/E/P/B/ROE analysis. Internal REST API of the web frontend.

EarningsWhispers: 33,500+ global stocks tracked (US, Europe, Asia, LatAm). COMPLETE earnings transcripts (prepared remarks + Q&A) via public API without auth. No anti-bot, no aggressive rate limiting. Tested on 60+ tickers (AAPL, MSFT, GGAL, SHEL, TM, VALE, etc). Metadata: date, fiscal period, participants.

Barchart: 30K+ US stocks and global ADRs, plus futures. Delayed quotes, fundamentals, insider summary, analyst estimates, opinion pages with ticker search. Futures data on indices, commodities, currencies, rates. Scraper of the Barchart site.

Nasdaq Data: US stocks (nasdaq + nyse + other US exchanges). Internal REST API of Nasdaq.com with access to quotes, short interest (semi-monthly), financials, 13F filings (institutional holdings), insider transactions, options chains, dividends, earnings, news, ETFs where the stock is a Top 10 Holding.

CBOE Data: CBOE indices (VIX, SPX, DJ, RUT), options, VX futures (VIX futures chain) + bond futures (IBHY, IBIG) and variance (VA), options with greeks, intraday 1-min bars, market summary per exchange (BZX, BYX, EDGX, EDGA), most-active equities and options, symbol lookup, historical HV/IV.

Investing.com: 81K+ equities, 10K+ indices, 2.4K currencies, 344 commodities, futures on indices/commodities/rates, 30K+ ETFs, 4K+ crypto. Global coverage. Quotes, historical OHLCV, fundamentals (income/balance/cashflow/ratios), dividends, earnings, profile. Data delayed 15-20min. Requires curl_cffi for Cloudflare bypass.

Morningstar: 53 universes, 102K+ listings, 39 countries. Argentine CEDEARs (XBUE, 469), NYSE (XNYS, 2,343), Nasdaq (XNAS, 3,741), Frankfurt (XFRA, 14K+), Tokyo (XTKS, 3,989), Shanghai (XSHG, 2,365), Shenzhen (XSHE, 2,934), Hong Kong (XHKG, 2,757), India (XBOM, XNSE, 5K+), Korea (XKRX, 2,877), Brazil (BVMF, 2,070), BMV (XMEX, 2,233), London (XLON, 1,333), Paris (XPAR, 728), Zurich (XSWX, 507), Tel Aviv (XTAE, 546), Johannesburg (XJSE, 332), and 30+ more. 33 fields per listing: price, market cap, ratios, returns 1d/1w/1m/3m/6m/12m/36m/60m/120m, debt, dividend yield, sector, industry. Multi-currency, multi-country, multi-language.

TradingView: GLOBAL coverage — 100K+ stocks, 50K+ cryptos, indices, forex, bonds. Scanner API with ~300 columns (quote, pre-calculated technical indicators RSI/MACD/EMAs/SMAs/pivots, aggregated BUY/SELL ratings, valuation, financials, earnings + forecasts, analyst targets, dividends, ownership, short interest, returns). Symbol Search v3 with ISIN/CUSIP/CIK (joinable with SEC EDGAR). News Headlines (~200 per stock, Dow Jones/Reuters/MarketBeat). HTML scraping of 16+ subpages (technicals, financials-income-statement, balance-sheet, cash-flow, options-chain, forecast, ideas). Mass SQL-like screener with filters + sort + pagination. 24 CLI modes with 4 unique HTTP endpoints.

Google Finance: Internal RPC API (batchexecute) discovered by reverse engineering. NO API key, NO auth. 19 CLI modes over 14+ RPC IDs. Quote (US + Argentine BCBA), OHLC intraday 1-min + 5-min (free, not available in other providers), OHLC daily last month + 6 months, massive financials (~22 KB income/balance/cashflow multi-period), earnings history, analyst recommendations with individual detail (Goldman, etc, with firm + target + date), technical ratings, company description with physical address + employees, peers, news with thumbnails, global indices in 1 call (Dow, S&P, NASDAQ, VIX, DAX, FTSE, Nikkei, Hang Seng, IBEX, CAC), sectors heatmap. Unique differentiators: free 1-min OHLC + per-analyst detail + company address. ⚠️ Unofficial API, requires precautions — read references/LIMITATIONS_TROUBLESHOOTING.md. Exhaustive documentation with 5 references + 3 JSON assets + warnings + plan B with alternative providers.

History of Market: 88 datasets pre-generados de historyofmarket.com (CC BY 4.0, sin API key). S&P 500 desde 1871 — Shiller CAPE, EPS, drawdowns con causa, forward PE, driver decomposition, constituents, changes, sectors. Nasdaq Composite (1971→) y Nasdaq 100 (1985→) — price, volatilidad, VXN, drawdowns, rolling 5y, changes. Dow Jones (1914→). SOX (1994→) con 30 constituents y SMH holdings. Sector ETFs XLK/XLF con GICS reclassification 2018/2023 y holdings. Mag 7 — concentration, correlation, AI capex, AI valuation vs dotcom. Macro — NBER recessions, yield curve, AIAE equity allocation. Scripts para reconstituir constituyentes historicos del S&P 500 y NDX. Cacheado (max-age=300), soporta ETag.

Data — Regional (Argentina)

BCRA Macro: 1,220 total series → 638 national series (official catalog v4.0) from the Central Bank. Daily/periodic series: exchange rate (official, wholesale, MEP, CCL, blue*), reserves, monetary policy rate, BADLAR, CER, UVA, LELIQ, monetary base, M2, deposits, loans. Data since 1996.

Data912: Argentine market live — local stocks, CEDEARs, bonds, bills, options, MEP, CCL. US live — stocks and volatilities. Refreshes every 20 seconds, rate limit 120 req/min. Historical OHLCV, filter screener, fundamental data of AR companies.

MAE: 17 endpoints. Complete wholesale trading: fixed income (LECAPs, BONCAPs, BOPREALes, hard dollar bonds, corporate bonds), repos (CAARS pesos, CAUSD dollars by term), REPO, swaps, FORWORD, wholesale FOREX, deferred dollar (DDF), ARS-MAE index, primary auctions, institutional communications, fund flows for TIR/MD curves.

BYMA: 9 endpoints. Complete Argentine stock exchange: leading stock panels, CEDEARs, sovereign bonds + LECAPs/BONCAPs, corporate bonds, repos, options and SENEBI. Historical OHLCV of instruments and indices (MERVAL, BURCAP). Equity (local stocks + CEDEARs), fixed income (sovereign, LECAPs, corporate bonds), derivatives (options). Bond technical sheet with amortization schedule.

CAFCI: 1,152 funds and 4,615 classes active as of 2026-06. Categories: Money Market, Fixed Income, Equity, Mixed Income, PyMes, Total Return, Infrastructure, Closed Funds, ASG, RG900. JSON catalog (fees, IDs, metadata), daily XLSX snapshot (VCP, equity, market share, variations), individual markdown sheet (TNA returns per period), portfolio composition (top assets). 4 endpoints + local daily cache.

INDEC: Official Series de Tiempo API of the Argentine State (apis.datos.gob.ar/series). ~4,250 series from INDEC + BCRA + Min Economy + Sec Labor + DGEYC. NO API key, NO auth, NO captcha. The most stable and best-documented API in the repo — official documentation, GitHub source, no-break ABI policies. Coverage: National IPC (general level, core, regulated, by chapters and regions), EMAE (original + seasonally adjusted + sectoral), IPI Manufacturing, ISAC Construction, EPH (unemployment by region), poverty line, exports, RIPTE wages, SMVM, BCRA exchange rate, BCRA reserves, REM market expectations. Unique features: 7 builtin transformations server-side (percent_change_a_year_ago = YoY inflation, percent_change_since_beginning_of_year = YTD inflation), 6 builtin temporal aggregations (daily→monthly→yearly with avg/sum/end_of_period/min/max), multi-series in 1 request. 19 CLI modes with 9 indicator shortcuts (ipc, emae, salarios, dolar, reservas, etc). Documentation: 10 references (REFERENCE, ENDPOINTS, PARAMS, REPRESENTATION_MODES, COLLAPSE_AGGREGATIONS, SERIES_CATALOG, RESPONSE_FORMAT, DATA_SOURCES, COOKBOOK with 30+ recipes, LIMITATIONS_TROUBLESHOOTING) + 5 JSON assets with curated catalog of canonical series IDs.

Brokers

Alpaca Trading: paper trading (free) and live trading of US stocks, crypto and options. REST API over Alpaca Broker. Market data via IEX feed. Market/limit/stop/trailing-stop orders, short selling, multi-leg options. Positions, account, watchlists, calendar. Official SDK: alpaca-py.

Primary: Trading API for Matba ROFEX (Argentina's derivatives exchange). Futures (USD, soybean, corn, wheat), options on futures, stocks, bonds, CEDEARs. Token-based auth (24h). REST + WebSocket for real-time market data, order entry/cancel, and execution reports. Risk API (HTTP Basic Auth) for positions and account reports. No SDK — direct HTTP via requests.

Tools

Option pricing: flat-Python, numpy-vectorized option pricing for backtesting. 9 methods covering vanilla, smile, and tail risk: Black-Scholes (closed-form, european), Binomial CRR (tree, american + european), Trinomial Boyle (tree, american + european), Monte Carlo with antithetic variates (european) + Longstaff-Schwartz (american via simulation), Bjerksund-Stensland 2002 / BAW (closed-form american), Heston 1993 (stochastic vol, smile via Fourier integral), Bates 1996 (Heston + Merton jumps, captures crash risk). Plus analytic greeks (delta/gamma/vega/theta/rho), implied volatility solver via bisection, and risk-neutral P(ITM) and P(Profit). CLI with 15 modes including validate and bench. Real benchmarks (Python 3.14 + numpy 2.4.4, same inputs for all methods): BS 2.4 us/op (419k/s), BS2 3.6 us/op (276k/s), P(ITM) 1.1 us/op (908k/s), Heston 398 us/op (2.5k/s), Bates 6.2 ms/op (160/s), Binomial N=500 5.6 ms/op (178/s). Validated against Hull 9th ed (Examples 15.6 and 21.1) and put-call parity (15/15 pass).

Backtesting: academic backtesting framework for quantitative research. 30+ risk and performance ratios (flat, numpy-vectorized, no classes), 10 classes of indicators (trend-following, oscillators, contrarians, flow, combined, discrete counts, seasonality, statistical, referential, fundamental). Event-driven BacktestEngine with 8 built-in strategies (SMA crossover, RSI mean-reversion, MACD, Bollinger Bands contrarian, ADX trend, momentum, growth+momentum combo). Markowitz efficient frontier with random portfolio sampling and Monte Carlo simulation. Forward-looking simulation with Johnson SU marginals + t/Gaussian copula, drift, and fan-chart projection. Walk-forward cross-validation with expanding window and IS/OOS gap. Stress testing with parametric scenario shocks. Fundamental analysis: Altman Z-Score (bankruptcy prediction), Piotroski F-Score (9-criterion quality), DuPont decomposition (5-factor ROE). 30+ risk/performance ratios: Sharpe, Sortino, Calmar, Kelly, MaxDD, Ulcer, Recovery Factor, Rachev A/B/C, Common Sense Ratio, Payoff Ratio, Profit Factor, Win/Loss Ratio, VaR (empirical/normal/Johnson SU), cVaR, tracking error, information ratio. 31-check 4-level validation suite (py scripts/validate.py) covering CLI modes, mathematical consistency, edge cases, and regression.

Portfolio: portfolio construction and optimization from the course material (MPT, NCO, Black-Litterman). Markowitz via scipy.optimize or Monte Carlo simulation with efficient frontier and CML tangent line. Black-Litterman full pipeline: market-implied risk aversion (delta), CAPM-inverse prior returns, absolute and relative views with confidence levels, Idzorek omega, Bayesian posterior returns and covariance, Markowitz on BL posterior. HRP/HERC/NCO hierarchical methods: correlation-to-distance clustering (single/complete/average/ward), recursive bisection risk parity, nested clustered optimization with intra/inter-cluster Markowitz, NCO with per-asset and per-class constraints. Risk measures: VaR, CVaR, MAD, MSV, max drawdown, CDaR, diversification ratio, risk contribution. Covariance estimation: historical, Ledoit-Wolf (sklearn-compatible), OAS, EWMA. 12 CLI modes (markowitz, montecarlo, frontier, bl-prior, bl, hrp, herc, nco, nco-con, clusters, risk, stats). All flat numpy + scipy, zero external financial libraries required. 28-test suite with mathematical consistency checks + real yfinance verification matching PyPortfolioOpt/Riskfolio-Lib outputs exactly.

Compatibility

Tested with:

Claude Code, Antigravity, Cursor, Windsurf, Gemini CLI, Codex, OpenCode, CommandCode CLI, Kimi CLI, Trae

License

MIT

文档与办公

中风险

来源需自行核对维护者身份。
包含脚本或命令调用，安装前请复核。
未检测到明显外部权限要求。
未检测到高风险命令。
扫描发现：4 条。

Codex — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gauss314/skills.git
将 "skills/cafci" 文件夹复制到 Codex 的 skills 目录中。
重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Codex 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gauss314/skills.git
将 "skills/cafci" 文件夹复制到 Claude Code 的 skills 目录中。
重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Claude Code 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gauss314/skills.git
将 "skills/cafci" 文件夹复制到 Cursor 的 skills 目录中。
重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Cursor 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gauss314/skills.git
将 "skills/cafci" 文件夹复制到 GitHub Copilot 的 skills 目录中。
重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 GitHub Copilot 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gauss314/skills.git
将 "skills/cafci" 文件夹复制到 Windsurf 的 skills 目录中。
重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Windsurf 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Windsurf 让新的 skill 生效。

查看 SKILL.md 原文

name: cafci
description: "Datos de fondos comunes de inversion argentinos via CAFCI (Camara Argentina de Fondos Comunes de Inversion). Combina catalogo JSON (1152 fondos, 4615 clases, fees, IDs, metadata), snapshot diario XLSX (VCP, patrimonio, market share, variaciones), ficha individual markdown (rendimientos TNA por periodo) y composicion de cartera (top activos). Sin API key."
license: MIT

CAFCI — Fondos Comunes de Inversion Argentinos

Skill para consultar informacion publica de fondos comunes de inversion en Argentina via las 4 fuentes oficiales de CAFCI (Camara Argentina de Fondos Comunes de Inversion).

Cubre 1152 fondos y 4615 clases activas al 2026-06: Money Market, Renta Fija, Renta Variable, Renta Mixta, PyMes, Retorno Total, Infraestructura, Fondos Cerrados, ASG, RG900.

⚠️ Aviso Legal

API publica sin documentacion oficial. Los endpoints pueden cambiar sin aviso (como paso con la API REST anterior, discontinuada en 2026-04).
Respetar terminos de uso del CAFCI.
Los datos son delayed (cierre del dia habil, ~18hs ART).
Para uso comercial intensivo, contactar al CAFCI para feeds oficiales.
Rendimientos pasados no garantizan rendimientos futuros.

📌 Importante: API REST anterior DISCONTINUADA

La API REST api.pub.cafci.org.ar/tipo-renta, /fondo/{id}, /estadisticas/... fue discontinuada en 2026-04 (HTTP 403 "Route not allowed"). El unico path que sigue activo en ese host es /pb_get.

Esta skill usa las 4 fuentes alternativas que la reemplazaron:

/consulta-de-fondos.json (catalogo completo)
/pb_get (XLSX diario)
defuddle.md/.../fondos/{F}?clase={C} (ficha markdown)
.../fondos/{F}?clase={C} (HTML para composicion de cartera)

Scripts

Script	Descripcion
fetch_cafci.py	Script principal: todos los endpoints + funciones de consulta

Requiere: pip install openpyxl (para parsear el XLSX diario).

Uso rapido

# ── DATASETS ENTEROS (con cache local diario) ──────────────────────────

# Catalogo completo: 1152 fondos, 4615 clases con IDs, honorarios, metadata
py scripts/fetch_cafci.py catalogo
py scripts/fetch_cafci.py catalogo -o catalogo.json    # guarda 2.7MB
py scripts/fetch_cafci.py catalogo --no-cache          # fuerza refetch

# Snapshot diario: VCP, patrimonio, variaciones (dia/mes/YTD/12m)
py scripts/fetch_cafci.py diario
py scripts/fetch_cafci.py diario -o diario.json
py scripts/fetch_cafci.py diario --no-cache

# ── CONSULTAS SOBRE EL CACHE ───────────────────────────────────────────

# Buscar fondo por nombre (parcial, case-insensitive)
py scripts/fetch_cafci.py buscar "ahorro"
py scripts/fetch_cafci.py buscar "delta"
py scripts/fetch_cafci.py buscar "renta fija"

# Resolver IDs (mas compacto: solo fondo_id, clase_id, nombres)
py scripts/fetch_cafci.py resolve "ieb estrategico"
py scripts/fetch_cafci.py resolve "1810"

# Top N por patrimonio en una categoria del diario
py scripts/fetch_cafci.py top "Mercado de Dinero Peso Argentina"
py scripts/fetch_cafci.py top "Mercado de Dinero Peso Argentina" --limit 20
py scripts/fetch_cafci.py top "Renta Variable Peso Argentina"
py scripts/fetch_cafci.py top "Renta Fija Peso Argentina" --limit 5

# ── FICHAS INDIVIDUALES ────────────────────────────────────────────────

# Ficha markdown (rendimientos TNA: 7d/1m/90d/180d/YTD/12m + datos del fondo)
py scripts/fetch_cafci.py ficha 304 308              # 1810 Ahorro
py scripts/fetch_cafci.py ficha 1717 5772            # otro fondo
py scripts/fetch_cafci.py ficha 304 308 -o ficha.md  # guarda markdown

# Composicion de cartera (top activos + porcentaje)
py scripts/fetch_cafci.py cartera 304 308
py scripts/fetch_cafci.py cartera 1717 5772

# Ficha COMPLETA todo-en-uno (combina catalogo + diario + ficha + cartera)
py scripts/fetch_cafci.py fondo 304 308
py scripts/fetch_cafci.py fondo 304 308 -o 1810_ahorro_completo.json

# ── COMBINADO ──────────────────────────────────────────────────────────

# Catalogo + diario juntos (sin fichas individuales)
py scripts/fetch_cafci.py all -o cafci_snapshot.json

# ── OUTPUT ─────────────────────────────────────────────────────────────

# Modo silencioso (solo JSON/markdown, sin logs)
py scripts/fetch_cafci.py top "Mercado de Dinero Peso Argentina" -q

Endpoints disponibles

Modo	Data	URL
`catalogo`	Catalogo: 1152 fondos, 4615 clases, IDs, honorarios, metadata	`GET /consulta-de-fondos.json`
`diario`	Snapshot diario: VCP, patrimonio, market share, variaciones	`GET /pb_get` (XLSX)
`ficha FONDO CLASE`	Ficha markdown: rendimientos TNA por periodo	`GET defuddle.md/.../fondos/{F}?clase={C}`
`cartera FONDO CLASE`	Composicion de cartera (top activos + %)	`GET .../fondos/{F}?clase={C}` (HTML)
`buscar QUERY`	Buscar fondos por nombre parcial	(local, sobre catalogo)
`resolve QUERY`	Resolver fondoId/claseId desde nombre	(local, sobre catalogo)
`top CATEGORIA`	Top N por patrimonio en una categoria	(local, sobre diario)
`fondo FONDO CLASE`	Ficha completa todo-en-uno (combina los 4 endpoints)	(local + 4 requests)
`all`	Snapshot catalogo + diario	(local + 2 requests)

Total: 4 endpoints HTTP + 5 funciones de consulta sobre cache.

Cache local

Los datasets pesados (catalogo + diario) se cachean una vez por dia en el directorio temporal del sistema:

$TMP/cafci-catalog-YYYY-MM-DD.json    (~2.7 MB)
$TMP/cafci-daily-YYYY-MM-DD.json      (~1-2 MB)

Windows: C:\Users\<user>\AppData\Local\Temp\
Linux/Mac: /tmp/

Si vas a hacer multiples consultas en el dia (typical workflow), reusan el cache automaticamente. Forzar refetch con --no-cache.

Tipos de renta soportados

Tipo	Cantidad fondos
Renta Fija	542
Renta Mixta	271
Mercado de Dinero	96
Renta Variable	76
PyMes	65
Retorno Total	42
Infraestructura	23
Fondos Cerrados	22
ASG	10
RG900	5

Categorias del DIARIO (para `top`)

Las categorias del diario combinan tipo_renta + moneda + region como string. Ejemplos comunes:

Categoria	Cobertura
`Renta Variable Peso Argentina`	Acciones argentinas
`Mercado de Dinero Peso Argentina`	Money Market $
`Renta Fija Peso Argentina`	Bonos $
`Renta Fija Dolar Estadounidense Argentina`	Bonos USD argentinos
`Renta Mixta Peso Argentina`	Fondos mixtos $
`Retorno Total Peso Argentina`	Total return $

Para ver lista completa: py scripts/fetch_cafci.py diario -q | jq '.categorias'

Consideraciones tecnicas

Datos devueltos por `catalogo`

Top-level:

Campo	Descripcion
`generated_at`	Timestamp ISO del catalogo.
`total_fondos`, `total_clases`	Contadores.
`filtros`	Catalogos de enums: `tipo_renta`, `region`, `moneda`, `benchmark`, `duration`, `horizonte`, `sociedad_gerente`, `tipo_dinero`, `tipo_renta_mixta`.
`fondos[]`	Array de fondos.

Cada fondos[] tiene id, nombre, codigo_cnv, estado, objetivo, tipo_dinero, valuacion, dias_liquidacion, inicio, sociedad_gerente, sociedad_depositaria, moneda, tipo_renta, region, duration, benchmark, horizonte y clases[].

Cada clases[] tiene id, nombre, moneda, inversion_minima, honorarios (ingreso, rescate, transferencia, administracion_gerente, administracion_depositaria, gasto_ordinario_gestion), suscripcion, liquidez, rg384, log_abierto, ticker_bloomberg, ticker_isin.

⚠️ honorarios.* son strings (no floats). Castear con float() antes de comparar.

Datos devueltos por `diario`

{
  "fecha_reporte": "2026-06-04",
  "categorias": ["Renta Variable Peso Argentina", ...],
  "fondos": [
    {
      "nombre": "Allaria Equity Selection - Clase A",
      "categoria": "Renta Variable Peso Argentina",
      "moneda": "ARS",
      "region": "Arg",
      "horizonte": "Cor",
      "fecha": "2026-06-04",
      "vcp_actual": 1642.85,
      "vcp_anterior": 1628.345,
      "variacion_dia_pct": 0.891,
      "vcp_reexp_pesos": 1642.85,
      "variacion_mes_pct": -1.181,
      "variacion_ytd_pct": 11.939,
      "variacion_12m_pct": 61.542,
      "cantidad_cuotapartes": 1276470413.29,
      "patrimonio": 2097049572.01,
      "market_share": 0.107,
      "depositaria": "Banco Comafi S.A.",
      "codigo_cnv": "1603"
    }
  ]
}

Datos devueltos por `cartera`

{
  "fondo_id": 304,
  "clase_id": 308,
  "fecha_cartera": "15/05/2026",
  "composicion": [
    {"nombre": "Cta Cte $ Rem Bco Credico", "porcentaje": 17.4},
    {"nombre": "Pzo Fi $ Bco Nacion", "porcentaje": 14.8},
    ...
    {"nombre": "Resto de Activos", "porcentaje": 29.2}
  ]
}

CAFCI publica solo los top ~14 activos + "Resto de Activos" agrupado. La fecha de cartera tiene delay de ~2-3 semanas vs el diario.

Datos devueltos por `fondo` (todo-en-uno)

{
  "meta": { ...del catalogo... },
  "diario": { ...del XLSX... },
  "ficha_md": "...markdown defuddle...",
  "cartera": { ...composicion... }
}

Workflows recomendados

A) Top N por patrimonio con fees:

top "<categoria>" --limit N → lista de fondos
Para cada nombre, resolve para conseguir fondo_id, clase_id
Buscar honorarios en catalogo por clases[].nombre exacto

B) Ficha completa de un fondo:

resolve "<query>" → conseguir IDs
fondo FONDO_ID CLASE_ID → todo-en-uno

C) Cuando el usuario no especifica clase:

Usar buscar o resolve y mostrar las clases disponibles
Si hay una sola, continuar automaticamente con esa

Flags

Flag	Descripcion
`--limit N`	Cantidad de resultados (`top`). Default: 10
`--no-cache`	Forzar refetch de catalogo/diario (ignorar cache local)
`-o archivo`	Guardar output a archivo JSON o markdown
`-q` / `--quiet`	Modo silencioso (solo JSON/markdown, sin logs)

Rate limiting

No hay rate limiting documentado. Recomendado:

Minimo 0.3 segundos entre requests a CAFCI.
Para defuddle.md (proxy externo), esperar mas si hay timeouts.
Para batches grandes, usar pool de concurrencia max 5.

Manejo de errores

Status	Causas tipicas
200	OK
403 `Route not allowed`	Path discontinuado de la API REST anterior
403 (en `/pb_get`)	Faltan headers de browser (Origin, Referer) — el script ya los envia
404	URL mal formada o IDs inexistentes
Timeout en `defuddle.md`	Proxy externo lento — reintentar

Encoding

UTF-8 valido. Las consolas Windows muestran ? para acentos pero los archivos UTF-8 se guardan correctamente (el script usa ensure_ascii=False).

Estructura del skill

skills/cafci/
├── SKILL.md                          # Este archivo (guia rapida)
├── references/
│   └── REFERENCE.md                  # Documentacion completa de los 4 endpoints + cache
└── scripts/
    └── fetch_cafci.py                # Script principal

Documentacion detallada: Consultar references/REFERENCE.md para schemas JSON completos, tablas de campos exhaustivas, codigos de tipo_renta/region/horizonte/moneda, cache local, manejo de errores y consideraciones tecnicas.

Inspirado en: ferminrp/agent-skills/cafci-fondos-comunes-argentina — esta implementacion porta el mismo diseño de 4 fuentes a la arquitectura SKILL.md / references/REFERENCE.md / scripts/fetch_*.py del repo.

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CAFCI — Fondos Comunes de Inversion Argentinos

⚠️ Aviso Legal

📌 Importante: API REST anterior DISCONTINUADA

Scripts

Uso rapido

Endpoints disponibles

Cache local

Tipos de renta soportados

Categorias del DIARIO (para top)

Consideraciones tecnicas

Datos devueltos por catalogo

Datos devueltos por diario

Datos devueltos por cartera

Datos devueltos por fondo (todo-en-uno)

Workflows recomendados

Flags

Rate limiting

Manejo de errores

Encoding

Estructura del skill

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Categorias del DIARIO (para `top`)

Datos devueltos por `catalogo`

Datos devueltos por `diario`

Datos devueltos por `cartera`

Datos devueltos por `fondo` (todo-en-uno)