Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add csmar432/finai-research --skill fin-data-acquisitiongit clone --depth 1 https://github.com/csmar432/finai-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/csmar432/finai-research/fin-data-acquisition)<a href="https://agentmods.dev/skills/csmar432/finai-research/fin-data-acquisition"><img src="https://agentmods.dev/badge/skills/csmar432/finai-research/fin-data-acquisition/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/csmar432/finai-research/fin-data-acquisition"><img src="https://agentmods.dev/badge/skills/csmar432/finai-research/fin-data-acquisition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.02810 |
| Opus 5 | $0.00018 | $0.01405 |
| Sonnet 5 | $0.00007 | $0.00562 |
| Haiku 4.5 | $0.00004 | $0.00281 |
Grade A, and why
fin-data-acquisition scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fin-data-acquisition
根据REFINED_DESIGN.md中的变量定义，自动获取所需数据并生成可执行的回归分析脚本（Python/Stata）。
触发条件
- 关键词:
获取数据数据获取data acquisition下载数据数据准备实证数据 - Skill语法:
Skill: fin-data-acquisition - 前置条件: 已完成
REFINED_DESIGN.md(研究设计文档)
核心原则
禁止行为 (未经用户明确授权不得执行)
❌ 静默回退到模拟数据
❌ 自动生成虚假回归结果
❌ 在用户未同意情况下继续流水线使用模拟数据
❌ 跳过数据源预检查直接获取数据
数据源预检查 (强制执行)
在任何数据获取前，必须先运行数据源检查：
from scripts.data_source_checker import DataSourceChecker, DataRequirement
# 第一步：定义数据需求
requirements = [
DataRequirement(
name="financial_data",
user_facing_name="A股财务数据",
description="ROA、资产负债率、企业规模、研发投入",
sources=["tushare", "wind", "csmar", "akshare"],
required=True,
),
DataRequirement(
name="esg_data",
user_facing_name="ESG评级",
sources=["msci", "商道融绿", "华证"],
required=False,
),
DataRequirement(
name="macro_data",
user_facing_name="宏观数据",
sources=["user-financial", "user-wb-data", "user-imf-data"],
required=True,
),
]
# 第二步：运行数据源检查
checker = DataSourceChecker()
results = checker.check(requirements)
# 第三步：展示可用性报告
checker.print_report(results)
数据源Fallback链
每个数据类型都有明确的降级路径：
A股财务数据
tushare (需TUSHARE_TOKEN)
↓ 失败/无Token
wind (需Wind账号)
↓ 失败/无账号
csmar (需机构账号)
↓ 失败/无账号
akshare (免费，备选)
↓ 失败
手动下载 -> 询问用户
宏观数据
user-financial (akshare, 免费)
↓ 失败
user-wb-data (World Bank, 免费)
↓ 失败
user-imf-data (IMF, 免费)
↓ 失败
手动下载 -> 询问用户
美股数据
user-yfinance (免费)
↓ 失败
user-eodhd (需EODHD_API_KEY)
↓ 失败/无Key
手动下载 -> 询问用户
学术文献数据
user-openalex (免费)
↓ 失败
user-arxiv (免费)
↓ 失败
user-nber-wp (免费)
↓ 失败
手动检索 -> 询问用户
DataFetcher API
from scripts.research_framework import DataFetcher, ProvenanceTracker
# 初始化 (带数据溯源)
tracker = ProvenanceTracker(output_dir="data/provenance/")
fetcher = DataFetcher(output_dir="data/", tracker=tracker, verbose=True)
# ============ 面板数据获取 ============
df = fetcher.fetch_panel(
tickers=["000001.SZ", "600000.SH"],
years=["2018", "2019", "2020", "2021", "2022"],
statements=["balance", "income", "cashflow"],
include_sustainability=True, # ESG数据
)
# ============ 财务报表获取 ============
fin = fetcher.fetch_financials("000001.SZ", "income") # 利润表
fin = fetcher.fetch_financials("000001.SZ", "balance") # 资产负债表
fin = fetcher.fetch_financials("000001.SZ", "cashflow") # 现金流量表
# ============ 公司信息获取 ============
info = fetcher.fetch_ticker_info("000001.SZ") # 股票基本信息
# ============ ESG/可持续发展数据 ============
sust = fetcher.fetch_sustainability("000001.SZ") # ESG评级等
# ============ 宏观数据获取 ============
macro = fetcher.fetch_macro(indicator="gdp", country="CHN")
# ============ 融资融券数据 ============
margin = fetcher.fetch_margin(ts_code="000001.SZ", start_date="20180101")
# ============ 陆股通/港股通 ============
hgt = fetcher.fetch_hsgt_top10(date="20240101")
# ============ 分析师预测 ============
forecast = fetcher.fetch_consensus("000001.SZ")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 370 lines · 36 tokens per session scan A 03bd0a8ffd18
fin-data-acquisition is a skill published in the GitHub repository csmar432/finai-research (100 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 2,810 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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