Eastmoney Market Data
HKUDS/Vibe-Trading
Index of Eastmoney's free, no-token market data interfaces for China A-shares and Hong Kong stocks: fund flows, dragon-tiger lists, margin trading, reports and news.
A research rule set for pulling company financials from prioritized sources by market and cross-checking every key figure against two independent sources.
SKILL.md written in Chinese; this summary is our English description.
Install Financial Data Cross-Validation
$ npx skills add xbtlin/ai-berkshire --skill financial-data -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire financial-data --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .claude/skills && cp -r skills-src/codex-skills/financial-data .claude/skills/financial-data && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "financial-data" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/financial-data into .claude/skills/financial-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/financial-dataType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add xbtlin/ai-berkshire --skill financial-data -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire financial-data --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .agents/skills && cp -r skills-src/codex-skills/financial-data .agents/skills/financial-data && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "financial-data" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/financial-data into .agents/skills/financial-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xbtlin/ai-berkshire --skill financial-data -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire financial-data --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/codex-skills/financial-data .cursor/skills/financial-data && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "financial-data" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/financial-data into .cursor/skills/financial-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/xbtlin/ai-berkshire.git --path codex-skills/financial-data--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add xbtlin/ai-berkshire --skill financial-data -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire financial-data --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/codex-skills/financial-data .gemini/skills/financial-data && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "financial-data" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/financial-data into .gemini/skills/financial-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install xbtlin/ai-berkshire financial-dataInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add xbtlin/ai-berkshire --skill financial-data -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .github/skills && cp -r skills-src/codex-skills/financial-data .github/skills/financial-data && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "financial-data" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/financial-data into .github/skills/financial-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xbtlin/ai-berkshire --skill financial-data -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install xbtlin/ai-berkshire financial-data --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/codex-skills/financial-data .opencode/skills/financial-data && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "financial-data" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/financial-data into .opencode/skills/financial-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "financial-data", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
financial-data- GitHub stars
- 17k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 402 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
A research rule set for pulling company financials from prioritized sources by market and cross-checking every key figure against two independent sources.
- Works in 5 steps: 货币单位是新台币（TWD），与港币/人民币/美元混排时必须显式标注，跨市场对比先统… → 月营收是台股独有优势：上市柜公司每月10日前强制披露上月营收，是跟踪基本面拐点最快… → FinMind… → …
- Collecting financial statements for a US, Hong Kong, A-share or Taiwan stock
- SKILL.md covers Codex adapter note, 数据源优先级, 执行规范 and 常见差异原因（不一定是数据错误）, plus 3 more sections
- Calls python3; needs FINMIND_TOKEN
What it does
The core rule is that every key figure must come from two independent sources, and a gap above 1% must be flagged. The skill lists primary, secondary and original-filing sources by market: for US stocks macrotrends and stockanalysis with SEC EDGAR filings, for Hong Kong stocks aastocks, macrotrends through ADR codes and HKEX filings, for A-shares East Money and CNINFO, and for Taiwan stocks FinMind through a bundled script, Goodinfo and MOPS.
A Codex adapter note says the skill is generated from a shared source so Claude Code and Codex users get one workflow. The agent runs the `date` command first and states the data cutoff date in the report header, uses exact arithmetic tools for valuation math and labels uncertainty and source gaps. Shared scripts such as `tools/financial_rigor.py` are run from the repository root. The excerpt is cut off inside the Taiwan section.
When your agent uses it
- Collecting financial statements for a US, Hong Kong, A-share or Taiwan stock
- Cross-checking revenue and profit figures from two sources
- Stating a data cutoff date in an investment research report
Example prompts
- “Pull the latest revenue and net income for a Hong Kong-listed company and cross-check it against two sources.”
- “Collect Taiwan stock financials with the FinMind script and verify the market cap.”
- “Flag any figures in this report that differ between sources by more than 1 percent.”
Requirements
- Python 3 and the repository's `tools/` scripts such as `financial_rigor.py`
- Web access to the listed data sources
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- 货币单位是新台币（TWD），与港币/人民币/美元混排时必须显式标注，跨市场对比先统一换算
- 月营收是台股独有优势：上市柜公司每月10日前强制披露上月营收，是跟踪基本面拐点最快的公开信号，earnings-review/thesis-tracker 类分析应优先利用（revenue 子命令）
- FinMind 损益表为单季值，工具已自动加总为年度值；不足4季的年份会标注"仅前N季累计"
- FinMind 未注册可直接用（有小时级限额）。注册后的 API token 只存本机、严禁提交到 git，工具按优先级自动读取：①环境变量 FINMIND_TOKEN；②本地文件 local/finmind_token.txt（local/ 已被 .gitignore…
- 交叉验证：FinMind 数值与 Goodinfo（或 macrotrends 上的 ADR，如 TSM）对照，误差规则同下；台积电等有 ADR 的公司注意 ADR 与台股原股的汇率/存托比率差异（1 TSM ADR = 5 股 2330）
What it can do on your machine
Read from SKILL.md and the folder at commit efa220f. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
FINMIND_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Financial Data Cross-Validation loads about 1.4k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 402 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from xbtlin/ai-berkshire at commit efa220f, republished under its MIT licence (© xbtlin). 402 words, ~1,405 tokens.
Download SKILL.mdSave it as
.claude/skills/financial-data/SKILL.md (or your agent's skills folder).- name
- financial-data
- description
- AI Berkshire skill: 财务数据获取与交叉验证规范. Source: skills/financial-data.md.
Codex adapter note
This skill is generated from skills/financial-data.md so Claude Code and Codex users share one canonical workflow.
- Treat
$ARGUMENTSas the user's request in the current Codex thread. - When the source mentions Claude-only surfaces such as Task, Agent, WebSearch, Bash, Read, or Write, use the closest Codex capability available in this session: subagents when available, web search when needed, shell commands for local tools, and normal file edits for workspace files.
- Use shared project tools from
tools/in this repository. Prefer running commands from the repository root with paths likepython3 tools/financial_rigor.py ...; if the current thread starts outside the repo, locate the actual checkout path first instead of assuming a fixed home-directory path. - Before starting research, run the
datecommand to confirm today's date; treat it as the baseline for "latest" data and state the data cutoff date in the report header. Never assume the current date from training data. - Preserve the research quality rules from
AGENTS.md: cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.
财务数据获取与交叉验证规范
本规范适用于所有涉及企业财务数据的研究。每个关键数据必须来自两个独立来源，误差>1%须标记。
数据源优先级
美股（PDD、腾讯ADR、网易ADR等）
| 优先级 | 来源 | URL | 获取方式 |
|---|---|---|---|
| 1（主） | macrotrends | macrotrends.net/stocks/charts/{ticker} | 直接访问，无需注册 |
| 2（副） | stockanalysis | stockanalysis.com/stocks/{ticker}/financials | 直接访问，无需注册 |
| 原始一手 | SEC EDGAR | sec.gov/cgi-bin/browse-edgar | 10-K / 10-Q 原文 |
港股（腾讯0700、网易9999、美团3690等）
| 优先级 | 来源 | URL | 获取方式 |
|---|---|---|---|
| 1（主） | aastocks | aastocks.com/tc/stocks/analysis/company-fundamental | 直接访问 |
| 2（副） | macrotrends（ADR代码） | 腾讯用TCEHY，网易用NTES | 直接访问 |
| 原始一手 | HKEX披露易 | hkexnews.hk | 年报PDF |
A股（三七互娱、吉比特等）
| 优先级 | 来源 | URL | 获取方式 |
|---|---|---|---|
| 1（主） | 东方财富 | eastmoney.com → 搜股票代码 → 财务报表 | 直接访问 |
| 2（副） | 巨潮资讯 | cninfo.com.cn | 原始年报/季报PDF |
Show full SKILL.md (172 more words)Show less
台股（台积电2330、联发科2454、大立光3008等）
| 优先级 | 来源 | URL | 获取方式 |
|---|---|---|---|
| 1（主） | FinMind API | api.finmindtrade.com | tools/twstock_data.py（零依赖脚本，见下） |
| 2（副） | Goodinfo台湾股市资讯网 | goodinfo.tw/tw/StockDetail.asp?STOCK_ID={代码} | 直接访问 |
| 原始一手 | 公开资讯观测站（MOPS） | mops.twse.com.tw | 财报原文/月营收公告 |
FinMind 取数工具（分析台股时优先调用，输出自带市值验算）：
python3 tools/twstock_data.py quote 2330 # 最新行情 + PER/PBR/殖利率 + 市值验算
python3 tools/twstock_data.py valuation 2330 # 估值指标 + PER一年区间 + 52周高低
python3 tools/twstock_data.py financials 2330 # 近5年年度核心财务（营收/毛利率/归母净利/EPS/ROE）
python3 tools/twstock_data.py revenue 2330 # 近13个月月营收及同比
python3 tools/twstock_data.py dividend 2330 # 近年股利政策（现金/股票股利、除息日）
python3 tools/twstock_data.py search 台積 # 搜索股票代码（注意台股名称为繁体）台股特别注意：
- 货币单位是新台币（TWD），与港币/人民币/美元混排时必须显式标注，跨市场对比先统一换算
- 月营收是台股独有优势：上市柜公司每月10日前强制披露上月营收，是跟踪基本面拐点最快的公开信号，earnings-review/thesis-tracker 类分析应优先利用（
revenue子命令） - FinMind 损益表为单季值，工具已自动加总为年度值；不足4季的年份会标注"仅前N季累计"
- FinMind 未注册可直接用（有小时级限额）。注册后的 API token 只存本机、严禁提交到 git，工具按优先级自动读取：①环境变量
FINMIND_TOKEN；②本地文件local/finmind_token.txt（local/已被.gitignore永久排除，把 token 单独一行写入该文件即可）。token 不得出现在报告、skill、commit 中 - 交叉验证：FinMind 数值与 Goodinfo（或 macrotrends 上的 ADR，如 TSM）对照，误差规则同下；台积电等有 ADR 的公司注意 ADR 与台股原股的汇率/存托比率差异（1 TSM ADR = 5 股 2330）
执行规范
第一步：获取数据
对每个财务指标（收入、净利润、毛利率、经营现金流、资产负债率等），分别从来源1和来源2取数。
第二步：误差计算与标记
误差率 = |来源1数值 - 来源2数值| / 来源1数值 × 100%| 误差 | 处理方式 |
|---|---|
| ≤ 1% | ✅ 一致，取来源1数值，标注两个来源 |
| 1% ~ 5% | ⚠️ 标记"数据存在差异"，注明两个数值，说明可能原因（汇率/会计口径） |
| > 5% | ❌ 标记"数据存在重大差异"，必须查原始财报核实，不得直接使用 |
第三步：数据呈现格式
每个关键数据必须按以下格式标注：
收入：1,239亿元 ✅
- macrotrends: 1,241亿元
- stockanalysis: 1,237亿元
- 误差: 0.3%差异示例：
净利润：245亿元 ⚠️ 数据存在差异
- macrotrends: 245亿元（GAAP）
- stockanalysis: 278亿元（Non-GAAP）
- 误差: 13.5% — 原因：会计口径不同（GAAP vs Non-GAAP）常见差异原因（不一定是数据错误）
| 原因 | 说明 |
|---|---|
| GAAP vs Non-GAAP | 最常见，尤其是利润类数据 |
| 汇率换算 | 港币/人民币/美元换算时间点不同 |
| 财年定义 | 自然年 vs 财年（如苹果财年10月结束） |
| 合并口径 | 是否含少数股东权益 |
| 数据更新滞后 | 某平台尚未更新最新一期财报 |
特别规则
- 未上市公司（米哈游、莉莉丝等）：只有一手数据来源时，数据前标记
[估计]，不执行交叉验证 - 季度数据 vs 年度数据：优先使用年度数据做交叉验证，季度数据部分来源可能有滞后
- 原始财报优先：若两个来源均与原始财报（10-K/年报PDF）不符，以原始财报为准，标记来源错误
股价与复权（历史序列必读）
价格有三种口径，混用会让历史股价位置、长期涨幅、历史估值分位全部失真：
| 口径 | 含义 | 用途 |
|---|---|---|
| 不复权 | 实际成交价，除权除息日跳空 | 仅用于"当前时点"快照 |
| 前复权 | 以最新价为基准回调历史价 | 历史股价对比、N年涨幅、历史PE band 一律用它 |
| 后复权 | 以上市首日为基准前推 | 计算历史总回报/年化收益 |
规则：
- 涉及历史价格的分析统一用前复权，且同一分析内不得混用复权与不复权来源。
- 当前市值/当前PE 用当前实际股价 × 当前总股本即可，与复权无关——复权只影响历史序列。
- 跨越拆股/大比例送转的每股指标（历史EPS、历史股价），必须复权还原后再同比。
- 总回报/年化收益需计入分红（后复权已含），只看价格涨幅会低估。
- 增发/回购后市值验算以最新总股本为准（
financial_rigor.py verify-market-cap偏差>5% 会提示核对）。
快速索引
| 场景 | 主要来源 | 备用来源 |
|---|---|---|
| PDD / 拼多多 | macrotrends.net/stocks/charts/PDD | stockanalysis.com/stocks/pdd |
| 腾讯 | macrotrends.net/stocks/charts/TCEHY | aastocks（0700.HK） |
| 网易 | macrotrends.net/stocks/charts/NTES | aastocks（9999.HK） |
| 三七互娱 | eastmoney.com（002555） | cninfo.com.cn |
| 吉比特 | eastmoney.com（603444） | cninfo.com.cn |
| Nintendo | macrotrends.net/stocks/charts/NTDOY | stockanalysis.com/stocks/ntdoy |
| Capcom | macrotrends（CCOEY） | stockanalysis（CCOEY） |
| 台积电 | tools/twstock_data.py（2330） | goodinfo.tw / macrotrends（TSM，注意1 ADR=5股） |
| 联发科 | tools/twstock_data.py（2454） | goodinfo.tw |
© xbtlin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in codex-skills/financial-data of xbtlin/ai-berkshire.
Open the folder on GitHubat commit efa220f
Compare with similar skills
Financial Data Cross-Validation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Financial Data Cross-Validation this skillxbtlin/ai-berkshire | 17k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Eastmoney Market DataHKUDS/Vibe-Trading | 35k | — | ~1k | Automated safety check: Pass | MIT | |
| SEC EDGAR Filings FetcherHKUDS/Vibe-Trading | 35k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Financial Researchfirecrawl/web-agent | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| US Market Data ToolkitGeeksfino/finskills | 283 | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Financial Statement Deep DiveGeeksfino/finskills | 283 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 |
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Questions about Financial Data Cross-Validation
What does Financial Data Cross-Validation do?
A research rule set for pulling company financials from prioritized sources by market and cross-checking every key figure against two independent sources. The core rule is that every key figure must come from two independent sources, and a gap above 1% must be flagged. The skill lists primary, secondary and original-filing sources by market: for US stocks macrotrends and stockanalysis with SEC EDGAR filings, for Hong Kong stocks aastocks, macrotrends through ADR codes and HKEX filings, for A-shares East Money and CNINFO, and for Taiwan stocks FinMind through a bundled script, Goodinfo and MOPS.
When should I use Financial Data Cross-Validation?
Financial Data Cross-Validation fits situations like: collecting financial statements for a US, Hong Kong, A-share or Taiwan stock; cross-checking revenue and profit figures from two sources; stating a data cutoff date in an investment research report.
How do I install Financial Data Cross-Validation in Claude Code?
Run `npx skills add xbtlin/ai-berkshire --skill financial-data -a claude-code`. Or copy the skill folder (codex-skills/financial-data in xbtlin/ai-berkshire) into .claude/skills/financial-data in your project. Claude Code loads it when a task matches its description.
How do I install Financial Data Cross-Validation in Codex?
Run `npx skills add xbtlin/ai-berkshire --skill financial-data -a codex`. Or copy the skill folder (codex-skills/financial-data in xbtlin/ai-berkshire) into .agents/skills/financial-data in your project. Codex loads it when a task matches its description.
Can I use Financial Data Cross-Validation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xbtlin/ai-berkshire --skill financial-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/financial-data, .gemini/skills/financial-data, .github/skills/financial-data and .opencode/skills/financial-data in your project.
What does Financial Data Cross-Validation need to run?
Going by SKILL.md and its folder, Financial Data Cross-Validation needs the command-line tools its instructions call (python3) and credentials named FINMIND_TOKEN. Our summary lists: Python 3 and the repository's `tools/` scripts such as `financial_rigor.py`; Web access to the listed data sources.
Does Financial Data Cross-Validation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Financial Data Cross-Validation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Financial Data Cross-Validation use?
Financial Data Cross-Validation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Financial Data Cross-Validation use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Financial Data Cross-Validation?
Skills that share tags, products or a category with Financial Data Cross-Validation: Eastmoney Market Data (HKUDS/Vibe-Trading, 35k stars), SEC EDGAR Filings Fetcher (HKUDS/Vibe-Trading, 35k stars), Financial Research (firecrawl/web-agent, 1.2k stars) and US Market Data Toolkit (Geeksfino/finskills, 283 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Financial Data Cross-Validation?
xbtlin (a GitHub user) maintains it in xbtlin/ai-berkshire, which has 16,652 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 6, 2026.
Source: xbtlin/ai-berkshire on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.