ReddRadar
Agent skill
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.
Install this agent skill to your Project
npx add-skill https://github.com/daymade/claude-code-skills/tree/main/financial-data-collector
SKILL.md
Financial Data Collector
Collect 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.
Critical Constraints
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.
Workflow
Step 1: Collect Data
Run the collection script:
python scripts/collect_data.py TICKER [--years 5] [--output path/to/output.json]
The script collects in this priority:
- yfinance — market data, historical financials, beta, analyst estimates
- yfinance ^TNX — 10Y Treasury yield as risk-free rate proxy
- User supplement — for years where yfinance returns NaN (report to user, do not guess)
Step 2: Validate Data
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.
Step 3: Deliver JSON
Single file: {TICKER}_financial_data.json. Schema in references/output-schema.md.
Do NOT create: README, CSV, summary reports, or any auxiliary files.
Output Schema (Summary)
{
"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>
Handling Missing Years
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 Sign Preservation
capex = cash_flow.loc["Capital Expenditure", year_col] # -37256.0
result["capex"] = float(capex) # Preserve negative
Datetime Column Indexing
year_col = [c for c in financials.columns if c.year == target_year][0]
revenue = financials.loc["Total Revenue", year_col]
Field Name Guards
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>
Mistake 1: Default Values for Missing Data
# ❌ 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
Mistake 2: Assuming All Years Have Data
# ❌ 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
Mistake 3: Using yfinance FCF in DCF Models Directly
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.
Mistake 4: Flipping CapEx Sign
# ❌ 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>
Known yfinance Pitfalls
See references/yfinance-pitfalls.md for detailed field mapping and workarounds.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
daymade/claude-code-skills
excel-automation
Create, parse, and control Excel files on macOS. Professional formatting with openpyxl, complex xlsm parsing with stdlib zipfile+xml for investment bank financial models, and Excel window control via AppleScript. Use when creating formatted Excel reports, parsing financial models that openpyxl cannot handle, or automating Excel on macOS.
daymade/claude-code-skills
claude-code-history-files-finder
Finds and recovers content from Claude Code session history files. This skill should be used when searching for deleted files, tracking changes across sessions, analyzing conversation history, or recovering code from previous Claude interactions. Triggers include mentions of "session history", "recover deleted", "find in history", "previous conversation", or ".claude/projects".
daymade/claude-code-skills
claude-skills-troubleshooting
Diagnose and resolve Claude Code plugin and skill issues. This skill should be used when plugins are installed but not showing in available skills list, skills are not activating as expected, or when troubleshooting enabledPlugins configuration in settings.json. Triggers include "plugin not working", "skill not showing", "installed but disabled", or "enabledPlugins" issues.
daymade/claude-code-skills
fixing-claude-export-conversations
Fixes broken line wrapping in Claude Code exported conversation files (.txt), reconstructing tables, paragraphs, paths, and tool calls that were hard-wrapped at fixed column widths. Includes an automated validation suite (generic, file-agnostic checks). Triggers when the user has a Claude Code export file with broken formatting, mentions "fix export", "fix conversation", "exported conversation", "make export readable", references a file matching YYYY-MM-DD-HHMMSS-*.txt, or has a .txt file with broken tables, split paths, or mangled tool output from Claude Code.
daymade/claude-code-skills
continue-claude-work
Recover actionable context from local `.claude` session artifacts and continue interrupted work without running `claude --resume`. This skill should be used when the user provides a Claude session ID, asks to continue prior work from local history, or wants to inspect `.claude` files before resuming implementation.
daymade/claude-code-skills
promptfoo-evaluation
Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".
Didn't find tool you were looking for?