# Financial Data

AI Berkshire skill: 财务数据获取与交叉验证规范.

- Repository: https://github.com/xbtlin/ai-berkshire (16972 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add xbtlin/ai-berkshire --skill financial-data -g -a claude-code -y`
Codex: `npx skills add xbtlin/ai-berkshire --skill financial-data -g -a codex -y`
Cursor: `npx skills add xbtlin/ai-berkshire --skill financial-data -g -a cursor -y`
Gemini CLI: `npx skills add xbtlin/ai-berkshire --skill financial-data -g -a gemini-cli -y`

## Description

AI Berkshire skill: 财务数据获取与交叉验证规范. Source: skills/financial-data.md.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Codex adapter note

This skill is generated from `skills/financial-data.md` so Claude Code and Codex users share one canonical workflow.

- Treat `$ARGUMENTS` as 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 like `python3 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 `date` command 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。**

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## 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）

The rest: https://github.com/xbtlin/ai-berkshire/blob/HEAD/codex-skills/financial-data/SKILL.md