sec-10k-company-analysis

name	sec-10k-company-analysis
description	Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with filings/financial_facts tables.

SEC 10-K Company Analysis

Use this skill to analyze one company from a SQLite SEC filings database and produce distinct, data-grounded QA pairs.

Inputs you need

• Company identifier: CIK preferred (or ticker/name if unavailable).
• Database connection or path.
• Target output count if specified; otherwise produce 12–20 distinct QA pairs.

Required workflow

Step 1: Schema discovery

Always inspect tables first before querying. Confirm exact column names — never assume aliases.

Key schema facts:

• filings table: columns are cik, form, filing_date, report_date, accession_number (NOT form_type)
• financial_facts table: columns include fact_name, fact_value, unit, fiscal_year, fiscal_period, end_date, accession_number, form_type, dimension_segment, dimension_geography
• If a query fails with "no such column", inspect the table schema and correct immediately — do not retry the same failing query.

Step 2: Company identity

SELECT * FROM companies WHERE cik = '<CIK>'
SELECT cik, ticker, exchange FROM company_tickers WHERE cik = '<CIK>'

Step 3: Filing context

SELECT cik, form, filing_date, report_date, accession_number
FROM filings WHERE cik = '<CIK>' AND form = '10-K'
ORDER BY filing_date DESC LIMIT 10

Identify the 3–5 most recent annual 10-K accession numbers for trend queries.

Step 4: Metric discovery (do this before bulk queries)

-- All available fact names for this company
SELECT DISTINCT fact_name FROM financial_facts
WHERE cik = '<CIK>' AND form_type = '10-K'
ORDER BY fact_name LIMIT 300

-- Revenue alias search
SELECT DISTINCT fact_name FROM financial_facts
WHERE cik = '<CIK>' AND form_type = '10-K'
AND (fact_name LIKE '%Revenue%' OR fact_name LIKE '%Sales%'
     OR fact_name LIKE '%ContractWithCustomer%')

Revenue/income labels vary by company — discover actuals first, then use them.

Step 5: Pull evidence in two rounds

Round A — Core multi-year trends (query with form_type = '10-K', ordered by end_date):

• Revenue, net income, operating income, gross profit
• Total assets, liabilities, stockholders' equity
• Operating cash flow, investing cash flow, financing cash flow
• Long-term debt, shares outstanding, diluted EPS
• Dividends per share, interest expense, income tax expense

Round B — Detail and niche metrics (pull what's available; skip silently if absent):

• Comprehensive income, accumulated OCI
• Working capital components: accounts receivable, inventory, accounts payable
• Debt carrying amount, weighted average interest rate, debt fair value
• Operating lease right-of-use assets, operating lease income
• Depreciation and amortization (separate from D&A combined if available)
• Impairment charges, restructuring charges
• Share-based compensation, deferred revenue, deferred tax
• Segment or geography data (dimension_segment, dimension_geography filters)
• Industry-specific metrics: R&D expense (pharma/tech), benefits/claims expense (insurance), lease revenue (REITs), investment income (financial), capex intensity

Step 6: Generate QA pairs from evidence

Submit a QA pair immediately when you have multi-datapoint support for a non-trivial conclusion. Keep exploring after each submission — aim for 12–20 distinct pairs covering different angles.

QA angle checklist

Work through as many distinct angles as the data supports:

1. Revenue growth drivers and volatility
2. Profitability trajectory (operating income, net income, margins)
3. Earnings quality: cash flow vs accounting income (OCF vs net income gap)
4. Capital allocation: dividends, buybacks, capex balance
5. Balance sheet evolution: leverage, equity growth, asset mix
6. Debt profile: level, interest rate, maturity, fair vs carrying value
7. Liquidity: cash position, working capital components (AR, inventory, AP)
8. Per-share trends: EPS, dividend per share, share count trajectory
9. Comprehensive income vs net income (OCI items, forex, hedging)
10. Cost structure shifts: COGS, SG&A, R&D as % of revenue
11. D&A and capex as signals of asset intensity and growth investment
12. Impairment and restructuring as transformation/risk signals
13. Tax rate dynamics: effective rate, deferred taxes, tax benefits
14. Segment or geographic concentration (if data present)
15. Industry-specific metrics (claims ratio, R&D intensity, lease income, etc.)
16. Lease obligations and right-of-use assets
17. Pension / post-retirement benefit obligations (if material)
18. Deferred revenue and contract liability trends

Do not repeat the same thesis with different wording. Each QA should occupy a distinct analytical position.

QA style

Question form: "How has X evolved from Y to Z?" or "What does [metric trend] reveal about [business quality]?" Questions should be specific enough to be graded against retrieved data, but broad enough to require synthesis.

Answer form: 1–2 sentences. Lead with a concrete trend or comparison (include specific values and period references), then state the implication. Do not include more than 3–4 numbers per answer — prefer qualitative synthesis over numeric recaps.

Good example:

q: How does AvalonBay's operating cash flow compare to its dividend obligations, and what does this indicate about sustainability? a: Operating cash flow of $1.61B in 2024 comfortably exceeds dividend payments of $969M (~1.65× coverage), and the pattern has held consistently from 2022–2024, indicating strong and sustainable dividend coverage.

Poor (too numeric, no synthesis):

a: OCF was $1.61B in 2024, $1.52B in 2023, $1.42B in 2022. Dividends were $969M, $935M, $891M.

Edge-case handling

• Missing expected metrics: search for alternate fact_name values; never invent absent fields.
• Empty results: relax one filter at a time (remove accession constraint, widen date range, try alternate tag names).
• Mixed annual/quarterly facts: keep 10-K trend analysis annual-focused; filter by form_type = '10-K' and use fiscal_period if needed to isolate FY facts.
• Duplicate facts for same period: prefer the latest accession number; document only stable comparisons.
• Query errors: read the error, correct schema usage, and continue — do not retry the identical failing query.

Output format

For each QA pair:

• q: one analytical question with clear scope and period.
• a: concise answer grounded in retrieved facts (values, direction, period, implication).

Quality bar:

• Evidence-grounded, non-redundant, specific.
• No unsupported claims or speculation.
• Answers interpretable without extra context.
• Covers enough distinct angles that a reader gains a comprehensive financial picture of the company.