Edgar Risk Diff

Diff the SEC 10-K Risk Factors section (Item 1A) between two filings for any US-listed ticker. Surfaces new risks, removed risks, modified language, theme rollups (AI, cyber, geopolitics, regulation), and a churn percentage. Uses SEC EDGAR directly — no API key required. Premium tier adds embedding-based novelty scoring.

What is new in this company's risk factors vs. last year? This skill answers that question in seconds, for any US-listed ticker, by pulling the two most recent 10-Ks from SEC EDGAR and producing a structured diff of Item 1A (Risk Factors).

When to use this skill

Activate when the user asks any of:

• "What changed in {ticker}'s 10-K risk factors?"
• "What new risks did {ticker} disclose this year?"
• "Did {ticker} drop any risk factors compared to last year?"
• "Compare {ticker}'s risk factors between {year1} and {year2}."
• "Scan {tickers…} for new AI / cyber / China / regulatory risks."
• "Pull the latest risk factors for {ticker}."

Do NOT use this skill for:

• 10-Q diffs (this skill is 10-K only — Item 1A typically only changes annually).
• Live price / earnings / fundamentals (use a market-data skill instead).
• Legal advice. Output is informational only.

Quick start

# Diff the two most recent 10-Ks
python3 {baseDir}/scripts/risk_diff.py diff AAPL

# Diff specific years
python3 {baseDir}/scripts/risk_diff.py diff TSLA --years 2024 2022

# Print the latest Risk Factors section (no diff)
python3 {baseDir}/scripts/risk_diff.py latest NVDA

# One-line summary across many tickers (great for morning briefs)
python3 {baseDir}/scripts/risk_diff.py scan AAPL MSFT GOOGL NVDA META AMZN

# [premium] Embedding-based novelty score — ranks paragraphs by how new they
# actually are, not just whether the text differs.
python3 {baseDir}/scripts/risk_diff.py novelty AAPL --top 10

What the diff output contains

1. Churn percentage — fraction of paragraphs that were added, removed, or modified.
2. New themes — rollup of added paragraphs by topic (Cybersecurity, AI/ML, Geopolitics, Climate, Supply chain, Regulation, Litigation, Macro, Workforce, Crypto, Pandemic).
3. Added paragraphs — risk language that did not exist in the prior 10-K, each tagged with the themes it hits.
4. Removed paragraphs — language the company dropped (often as meaningful as what they added).
5. Modified paragraphs — old/new pairs with a similarity ratio, so the user can see how a known risk has been re-framed.

Free vs. premium

Capability	Free	Premium
Diff (added/removed/modified)	✅	✅
Theme rollup (keyword-based)	✅	✅
Multi-ticker scan	✅	✅
Semantic novelty score (embedding-based, catches paraphrased risks that the keyword diff misses)	❌	✅
Priority email support	❌	✅

Premium unlocks the novelty subcommand. Buy a license key at the listing and save it to ~/.edgar-risk-diff/license.txt, or export EDGAR_RISK_LICENSE=<key>.

Data source & rate limiting

• Reads from data.sec.gov and www.sec.gov/Archives/ (public, free, no auth).
• Throttles to under 10 req/s per SEC fair-access policy.
• Caches all responses in ~/.edgar-risk-diff/cache/ — subsequent runs on the same ticker are instant.
• Sends an EDGAR-compliant User-Agent header. Override with EDGAR_USER_AGENT="Your Name [e-mail]" if redistributing.

Security & permissions

No API keys. No credentials. No outbound writes.

• Makes HTTPS GET requests to data.sec.gov and www.sec.gov only.
• Writes only inside ~/.edgar-risk-diff/ (cache + optional license file).
• No telemetry. No analytics. No third-party calls.
• Review scripts/risk_diff.py (≈350 lines, single file, pure stdlib + requests) before first use.

Limitations

• Item 1A detection is regex-based and works on standard 10-K formatting. Filings that use highly non-standard HTML may fail to extract — re-run with latest to see what the parser sees.
• Paragraph matching is fuzzy (SequenceMatcher + hashed-bigram cosine). Very short paragraphs (<40 chars) are ignored to suppress noise.
• Premium novelty uses a deterministic hashed-bigram embedding — runs locally, no external API. Quality is below a transformer model but catches the paraphrase patterns this skill is designed for.