An AI risk desk for what the Nov 3, 2026 US elections could do to a company's profit.
Name a company. Your coding agent staffs a desk: analysts map how the company makes its profit, what each candidate would do, and every Kalshi and Polymarket market that can move it. A Risk Officer sizes each risk with a small engine, a Red Team flags the weak numbers, and every run ends in two verdicts, with sources. Works in Claude Code, Codex, Cursor, Gemini and any agent that reads Agent Skills.
See it without installing: live reports for Ford, PG&E, Coinbase and DraftKings.
Install it (one line, below), then ask your agent:
What does Nov 3 mean for Ford?
The desk board, in the chat and at the top of a local web report (Ford's, prices of Oct 28):
| Analyst | Signal | Why |
|---|---|---|
| Exposure | EXPOSED | biggest if it happens: −$1.30B a year (−19.2% of profit) |
| Policy | WEAK LINK | for results of 1% of profit or more, who wins moves the chance by 5 points at most |
| Market | PRICED | 3 markets on Kalshi and Polymarket |
| Red Team | CAUTION | 8 flags to read |
Risk Officer: Election risk LOW · Policy risk MEDIUM
- Election risk LOW: nothing passes the bar. The biggest is +$1.00B a year if Congress repeals the Section 232 auto and metals tariffs, but only 5% even if Democrats win.
- Policy risk MEDIUM: Kalshi puts 6% on the US giving notice to leave USMCA before 2027. For Ford: −$1.30B a year (−19.2% of profit).
- Red Team: the USMCA impact is an estimate, and at the high end of its range (−$2.50B) policy risk would be HIGH.
And a card to share (the report saves it as PNG):
Eight household names, run end to end (Ford on Oct 28 prices, the rest on Oct 27). Each links to its full summary, profit structure and sources.
| Company | Election risk | Policy risk | Biggest risk | Market | Profit, per year |
|---|---|---|---|---|---|
| PG&E | High | none | Becerra wins (96%) → 30% chance of a rate freeze, vs 0% under Hilton | Kalshi | −$600M (−12.6%) |
| DraftKings | Medium | High | Congress bans sports contracts on prediction markets in 2026 | Polymarket 18% | +$150M (+24.2%) |
| Coinbase | Low | High | Clarity Act (crypto market structure) becomes law before 2028 | Kalshi 21% | +$370M (+25.8%) |
| Ford | Low | Medium | US gives notice to leave USMCA before 2027 | Kalshi 6% | −$1.3B (−19.2%) |
| GM | Low | Medium | Same USMCA notice | Kalshi 8% | −$1.7B (−13.3%) |
| Disney | none | Medium | Federal film and TV production tax credit becomes law | Kalshi 26% | +$400M (+2.3%) |
| Uber | Low | none | California AG suit ends in a Lyft-sized settlement: 70%, and who wins moves it at most 11 points | Kalshi | −$170M (−3.1%) |
| Starbucks | Low | Low | New York City enacts a $30 minimum wage by 2030 | Kalshi 8% | −$60M (−2.0%) |
"none" means no market was linked to the company on that side.
The surprise: who wins on Nov 3 moves these odds less than the headlines suggest. Only one of the eight gets a high election risk. The bigger risks come from markets that price the policy directly and barely depend on the vote: the Clarity Act and sports contracts (high), USMCA for Ford and GM (medium). Where the election does matter, the Red Team keeps it honest: research finds a new utility commission moves allowed returns by about 0.15 points, and attorneys general of both parties sue companies, so PG&E and Uber carry those flags.
Not investment advice. Analyst drafts for human review. No recommendation to buy, sell or hold any security or to trade any contract. Markets are shown as information only.
Four skills, one per desk role. Each works on its own; election-risk calls the other three by name.
| Role | Skill | Ask it on its own |
|---|---|---|
| Exposure analyst | profit-structure | "Which of Ford's profit lines can a government move?" |
| Policy analyst | candidate-positions | "What would California's next governor do on electricity rates?" |
| Market analyst | prediction-markets | "What are the odds on a data-center moratorium?" |
| Risk Officer + Red Team | election-risk | "What does Nov 3 mean for Uber?" |
- The agent researches, the engine calculates. Every number comes from a small Python engine (standard library only), not from the model's arithmetic.
- Two kinds of market.* Direct* : the market resolving Yes is the policy, so its price is the chance.Race : the market prices who wins, times the chance the policy follows each winner.
- Two verdicts.* Election risk* = share of profit × how much who wins moves the chance.Policy risk = share of profit × the market's chance. High at 2% of profit or more, Medium at 0.5%. Only results that pass the bar count (at least 1% of profit, volume 10,000, chance 5%); when none does, the board says what came closest and why it falls short.
- Every impact shows how it was built : the formula with each number and its source, and a low-high range.
- Red Team. It checks whether each verdict survives its own range: "Policy risk HIGH ... would be LOW at the low end" means the verdict rests on one estimate. It also flags positions with no source link (counted as 0), thin markets and estimated impacts.
Ask your agent (recommended):
Install election-risk from https://github.com/juwonyee/election-risk
Or install it yourself #
claude plugin marketplace add juwonyee/election-risk
claude plugin install election-risk@election-risk
codex plugin marketplace add juwonyee/election-risk
codex plugin add election-risk@election-risk
gemini extensions install https://github.com/juwonyee/election-risk
git clone --depth 1 https://github.com/juwonyee/election-risk ~/.cursor/plugins/local/election-risk
npx skills add juwonyee/election-risk
Install the plugin or the skills, not both (you would see every skill twice).
Needs Python 3.10+. No packages, no API keys.
Race results need to know what each candidate would actually do. That lives in a policy book: one file per Nov 3 contest, every position with a source link, and the chance it happens if they win, from a research-based probability table (veto override rates, governors' proposal success, ballot measure pass rates and more).
The book is not in this repo, on purpose: candidate positions in a public repo could be edited by anyone. On the first run your agent asks you to pick:
- Build it yourself : your agent researches every contest with sources. It takes a while, and it's reused for every company afterwards.
- Download ours : ready in a minute, built by us on the date shown, possibly out of date. Your agent tells
you that and asks before running
erisk book download --accept-risk.
Every report prints which book it used (built or downloaded, date, fingerprint) and when prices were fetched.
- Chances are upper bounds. The probability table comes from historical rates over a full term, used here for about two years.
- Impacts are often estimates. Each one is labeled verified or estimated and shows its formula, so you can check it.
- The book can be wrong or out of date. Positions with no source count as 0, and the agent lists rows it doubts as weak links.
- Only priced risks. A risk with no Kalshi or Polymarket market is left out. Unreachable sources are listed, never worked around.
- Prices move. The examples use Oct 27 prices. Raw price snapshots are not kept in this repo (venue terms).
It won't recommend trades, size positions, help open market accounts, or work around a venue's regional restrictions. It names no small businesses (franchised chains are discussed as "a typical store").
python3 engine/erisk.py markets pull # every open Nov 3 + policy market
python3 engine/erisk.py book status # policy book coverage
python3 engine/erisk.py book find --tag utilities --state CA
python3 engine/erisk.py markets show GOVPARTYGA-26 --venue kalshi
ERISK_OUT=examples python3 engine/erisk.py compute --company SO --no-open # test fixture, synthetic prices
See AGENTS.md. Edit engine/, then python3 scripts/sync_engine.py; check with
python3 scripts/validate_skills.py and python3 -m unittest discover tests.
MIT.