daymade/financial-data-collector
Collect real financial data for any US publicly traded company from free public sources (yfinance). Output structured JSON consumable by downstream financial skills (DCF modeling, comps analysis, earnings review). Handles market data (price, shares, beta), historical financials (income statement, cash flow, balance sheet), WACC inputs, and analyst estimates. Use when users request collect data for ticker, get financials for company, pull market data, gather DCF inputs, or any task requiring structured financial data before analysis. Also triggers on financial data, company data, stock data.
npx skills add https://github.com/daymade/claude-code-skills --skill financial-data-collectorCollect and validate real financial data for US public companies using free data sources.
Output is a standardized JSON file ready for consumption by other financial skills.
NO FALLBACK values. If a field cannot be retrieved, set it to null with _source: "missing".
Never substitute defaults (e.g., beta or 1.0). The downstream skill decides how to handle missing data.
Data source attribution is mandatory. Every data section must have a _source field.
CapEx sign convention: yfinance returns CapEx as negative (cash outflow). Preserve the original sign. Document the convention in output metadata. Do NOT flip signs.
yfinance FCF ≠ Investment bank FCF. yfinance FCF = Operating CF + CapEx (no SBC deduction). Flag this in output metadata so downstream DCF skills don't overstate FCF.
Run the collection script:
python scripts/collect_data.py TICKER [--years 5] [--output path/to/output.json]
The script collects in this priority:
python scripts/validate_data.py path/to/output.json
Checks: field completeness, cross-field consistency (Market Cap = Price × Shares), range sanity (WACC 5-20%, beta 0.3-3.0), sign conventions.
Single file: {TICKER}_financial_data.json. Schema in references/output-schema.md.
Do NOT create: README, CSV, summary reports, or any auxiliary files.
{
"ticker": "META",
"company_name": "Meta Platforms, Inc.",
"data_date": "2026-03-02",
"currency": "USD",
"unit": "millions_usd",
"data_sources": { "market_data": "...", "2022_to_2024": "..." },
"market_data": { "current_price": 648.18, "shares_outstanding_millions": 2187, "market_cap_millions": 1639607, "beta_5y_monthly": 1.284 },
"income_statement": { "2024": { "revenue": 164501, "ebit": 69380, "tax_expense": ..., "net_income": ..., "_source": "yfinance" } },
"cash_flow": { "2024": { "operating_cash_flow": ..., "capex": -37256, "depreciation_amortization": 15498, "free_cash_flow": ..., "change_in_nwc": ..., "_source": "yfinance" } },
"balance_sheet": { "2024": { "total_debt": 30768, "cash_and_equivalents": 77815, "net_debt": -47047, "current_assets": ..., "current_liabilities": ..., "_source": "yfinance" } },
"wacc_inputs": { "risk_free_rate": 0.0396, "beta": 1.284, "credit_rating": null, "_source": "yfinance + ^TNX" },
"analyst_estimates": { "revenue_next_fy": 251113, "revenue_fy_after": 295558, "eps_next_fy": 29.59, "_source": "yfinance" },
"metadata": { "_capex_convention": "negative = cash outflow", "_fcf_note": "yfinance FCF = OperatingCF + CapEx. Does NOT deduct SBC." }
}
Full schema with all field definitions: references/output-schema.md
<correct_patterns>
if pd.isna(revenue):
result[year] = {"revenue": None, "_source": "yfinance returned NaN — supplement from 10-K"}
# Report missing years to the user. Do NOT skip or fill with estimates.
capex = cash_flow.loc["Capital Expenditure", year_col] # -37256.0
result["capex"] = float(capex) # Preserve negative
year_col = [c for c in financials.columns if c.year == target_year][0]
revenue = financials.loc["Total Revenue", year_col]
if "Total Revenue" in financials.index:
revenue = financials.loc["Total Revenue", year_col]
elif "Revenue" in financials.index:
revenue = financials.loc["Revenue", year_col]
else:
revenue = None
</correct_patterns>
<common_mistakes>
# ❌ WRONG
beta = info.get("beta", 1.0)
growth = data.get("growth") or 0.02
# ✅ RIGHT
beta = info.get("beta") # May be None — that's OK
# ❌ WRONG — 2020-2021 may be NaN
revenue = float(financials.loc["Total Revenue", year_col])
# ✅ RIGHT
value = financials.loc["Total Revenue", year_col]
revenue = float(value) if pd.notna(value) else None
yfinance FCF does NOT deduct SBC. For mega-caps like META, SBC can be $20-30B/yr, making yfinance FCF ~30% higher than investment-bank FCF. Always flag this in output.
# ❌ WRONG — double-negation risk downstream
capex = abs(cash_flow.loc["Capital Expenditure", year_col])
# ✅ RIGHT — preserve original, document convention
capex = float(cash_flow.loc["Capital Expenditure", year_col]) # -37256.0
</common_mistakes>
See references/yfinance-pitfalls.md for detailed field mapping and workarounds.
Take daymade/financial-data-collector from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.