# Risk Factor Delta > Diff Item 1A Risk Factors between two 10-K filings for a name using Massive's pre-parsed and taxonomy-classified risk-factor endpoint. Reports categories added, categories removed, and categories where the supporting text materially changed (>= 25% length delta) year-over-year. Groups by primary category so the reader sees the shape of what changed, not a flat diff. Use when a PM, credit analyst, or fundamental researcher asks "what did management add to Item 1A this year?" Requires Stocks Basic. Runs on the free tier. - Skill: `rgourley/risk-factor-delta` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add rgourley/risk-factor-delta` - Raw SKILL.md: https://api.skillmd.com/api/skills/rgourley/risk-factor-delta/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: rgourley (https://skillmd.com/u/rgourley) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/rgourley/risk-factor-delta --- # risk-factor-delta You hand over a ticker. The skill pulls the most recent 10-K risk-factor disclosures and the one before it, diffs the standardized category taxonomy, and reports what management added, dropped, and materially rewrote year-over-year. This is the "what changed in Item 1A?" read that fundamental analysts do by hand. It works because Massive already parses and categorizes risk factors from every 10-K into a three-tier taxonomy (primary, secondary, tertiary). No NLP on our end. No EDGAR text scraping. ## When to invoke - A fundamental analyst asks "did AAPL add anything new to Item 1A?" - A credit analyst wants a heads-up on new balance-sheet or liquidity risks flagged for the first time this cycle - A macro-driven investor scanning for regulatory / tariff / geopolitical risk-factor additions across a basket - The user says "risk factor delta", "10-K diff", "what's new in Item 1A", "compare risk factors YoY" Not for: single-filing risk catalog (fine as a fallback but the primary value is the delta). Not for prose-level word-for-word diff (this is a category-level diff with supporting text quoted for confirmation). ## What you need - A ticker (`--ticker`, required) - `MASSIVE_API_KEY` exported in the environment - Stocks Basic plan minimum. The `/stocks/filings/vX/risk-factors` endpoint is included on every Stocks plan. Optional: - `--current-filing-date` (YYYY-MM-DD): pin a specific "current" filing. Defaults to the most recent on record. - `--prior-filing-date` (YYYY-MM-DD): pin a specific "prior" filing. Defaults to the second-most-recent on record. ## What you get back Two output layers from one run. **Layer 1: canonical JSON** matching [`output-schema.json`](./output-schema.json). Top-level: `filings.current`, `filings.prior`, and `summary` counts (added, removed, materially changed, retained unchanged). `changes.added[]`, `changes.removed[]`, `changes.materially_changed[]` each carry per-entry `{primary_category, secondary_category, tertiary_category, supporting_text}`. Materially-changed entries also include `prior_supporting_text`, both lengths, and `length_delta_pct`. **Layer 2: rendered narrative**. Header with the delta counts, three sections (NEW / DROPPED / MATERIALLY CHANGED) grouped by primary category, each with the supporting-text quote so the reader can confirm the taxonomy call, followed by a one-line Take. See [`references/rendering.md`](./references/rendering.md). ## How it works 1. **Pull risk factors** for the ticker via `GET /stocks/filings/vX/risk-factors?ticker={T}&limit=50000&sort=filing_date.desc`. Massive returns one row per unique (primary, secondary, tertiary) category per filing, with a supporting-text snippet. 2. **Group by filing_date.** Each 10-K filing produces N rows all sharing the same `filing_date`. Sort dates descending; pick the two most recent as `current` and `prior` (or use the caller-supplied dates). 3. **Diff by category tuple.** For every (primary, secondary, tertiary): - In current only → added - In prior only → removed - In both → check supporting_text length delta. `>= 25%` flip → materially changed. Otherwise retained unchanged. 4. **Group results by primary category.** The primary axis is the headline shape ("all the new categories are financial_and_market"). Secondary/tertiary render as bullets under it. 5. **Take.** One sentence summarizing counts and the concentration of new categories. Massive's taxonomy comes from a published research paper linked in the endpoint docs; see [`references/methodology.md`](./references/methodology.md). ## Foundations used - [`massive-api-patterns`](../massive-api-patterns) for REST auth, retry, and pagination on the filings endpoint. ## Output mode: note Narrative note. This is a category-level diff on a small number of rows (10-K risk factors typically 15-40 per filing); a wide table would waste space. The rendered format optimizes for a fundamental analyst reading the delta once, then quoting the supporting text into a note or a call. ## Endpoints used - `GET /stocks/filings/vX/risk-factors?ticker={T}&limit=50000&sort=filing_date.desc` All categorized risk factors for the ticker across every 10-K on record. One paginated call. ## Doesn't handle (yet) - **Sentence-level text diff.** The skill reports a length delta as a "materially changed" proxy and quotes the current supporting text. A proper word-level diff (highlighting added/removed phrases) would be a clean PR extension. - **Cross-ticker roll-ups.** A watchlist mode ("scan my 30 names for new regulatory risk factors YoY") would compose this skill and aggregate by primary_category. Queued. - **10-Q updates.** Item 1A can be amended in a 10-Q. The endpoint covers annual 10-K disclosures only for the diff. 10-Q updates are a separate lane. - **Historical trends.** Only diffs two filings. A "risk factor trajectory over N years" view would surface which categories are chronic vs newly-appearing; queued. - **Peer comparison.** No "what risks does AAPL cite that MSFT doesn't?" yet. The taxonomy makes this trivially composable; queued as a separate `peer-risk-comparison` skill. These are clean PR extensions. The output schema is forward-compatible.