financial-data-collector
- Repo stars 1,187
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- Author updated Jun 14, 2026, 10:01 AM
- Author repo claude-code-skills
- Domain
- Engineering
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @daymade · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: financial-data-collector
description: Collect real financial data for any US publicly traded company from free public sources (yfinanc…
category: engineering
runtime: Python
---
# financial-data-collector output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Critical Constraints / Workflow / Step 1: Collect Data” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “Critical Constraints / Workflow / Step 1: Collect Data” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Critical Constraints / Workflow / Step 1: Collect Data”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: financial-data-collector
description: Collect real financial data for any US publicly traded company from free public sources (yfinanc…
category: engineering
source: daymade/claude-code-skills
---
# financial-data-collector
## When to use
- Collect real financial data for any US publicly traded company from free public sources (yfinance). Output structured…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Critical Constraints / Workflow / Step 1: Collect Data” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "financial-data-collector" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Critical Constraints / Workflow / Step 1: Collect Data
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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.jsonChecks: 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
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 negativeDatetime 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 = NoneMistake 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 OKMistake 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 NoneMistake 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]) # -37256.0Known yfinance Pitfalls
See references/yfinance-pitfalls.md for detailed field mapping and workarounds.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review