LevelField

Know who you're really betting against.

LevelField assesses the structural information-asymmetry risk of prediction-market event contracts — before you bet, from the contract text alone, with no trading data and no LLM API dependency.

It does not predict outcomes, track smart money, or detect anomalies after the fact. It answers one question: does the structure of this event allow a group of people to know the answer before you do — and is anything stopping them from trading on it?

Built for the Somnia × DreamDEX Event Contracts Hackathon (2026).

Judge in three commands (Node ≥ 20.9, no API key, no wallet):

npm install && npm test        # 70 software tests
npm run demo:agent             # an agent asks the real MCP server before acting: PROCEED at 3/100, DECLINE at 95/100
npm run validate               # 16-contract validation: category order + Spearman rho = 0.930

• ScoreRegistry on Somnia Shannon (source-verified): 0xb8e11dea346f2c961880879606a269db3165bbc7

• Demo video: demo-video/levelfield-demo.mp4 (2:55, burned captions + SRT)

• SDK feedback report (hackathon deliverable): docs/sdk-feedback-report.md

• Unlike LLM-scored risk tools, no model ever writes the number here — classification is anchor-matched with verbatim-verified evidence; the score is deterministic, unit-tested code.

How it works

Two-stage pipeline; no model ever produces a number.

Stage A — classification. A contract is matched against a fixed, public anchor library on five dimensions:

• D1 Outcome Control (30%) — what produces the outcome, from natural process to one person's will

• D3 Insider Tradability (25%) — whether the people who know early can trade on it

• D2 Knowledge Circle (20%) — how many people know before disclosure

• D4 Disclosure Synchronicity (15%) — whether everyone learns the outcome at once

• D5 Outcome Manufacturability (10%) — whether someone could cause the outcome to win a bet

Every classification must quote the contract's own text verbatim (mechanically verified as a substring), and a code-level scanner — independent of any model's judgment — detects instruction-like content addressed at automated assessors, flags it, and disqualifies any evidence quote drawn from those sentences. A market creator can neither talk a model into a low score nor have the attack text itself pass as evidence.

Stage A has three providers, none of which calls a paid API:

Stage B — scoring (deterministic code). Levels → weighted score 0–100 → band (low / moderate / elevated / high), plus two circuit breakers:

• CB-1: outcome decided by one person not clearly barred from trading it → graduated floor 80/90/95 (by D3 level 3/4/5)

• CB-2: outcome manufacturable unilaterally by such a party → graduated floor 75/85/90; also trips at D2=1 with D5=5 (a manufacturer needs no disclosure lag)

Two cross-dimension rules are enforced deterministically in the engine (not just requested of the classifier), and every adjustment is reported in the output's caveats.

Ambiguity never scores low: a dimension that can't be determined from the text defaults conservatively to level 4 and is flagged.

The taxonomy follows the outcome-maker classification the Anti-Corruption Data Collective validated against 435,000+ settled Polymarket markets ($54B volume, 2021–2026). Running the full pipeline over live testnet markets plus the curated set reproduces that risk gradient end-to-end with zero API calls: 3 (price binaries) → 19–21 (statistics, elections) → 49 (FOMC) → 65 (layoffs) → 78 (military) → 80–95 (individual-will, circuit-breaker floors).

Repository layout

packages/scoring/       two-stage pipeline: anchors, classifiers, voting, engine, DreamDEX fetcher
packages/mcp/           MCP server: assessment protocol + verification + scoring, zero LLM deps
apps/web/               Next.js UI: markets, per-market detail, methodology (reads the score cache)
data/anchors/           the anchor library (single source of truth, YAML)
data/curated/           curated contracts spanning the risk spectrum + injection test
data/classifications/   reference classifications (open protocol, quotes mechanically verified)
data/scores/            score cache written by score:all (in git for reproducible demos)
docs/design/no-api.md   architecture decision record
docs/research-dreamdex.md  verified integration reference (field mapping, gotchas, live findings)
FEEDBACK.md             SDK & docs feedback journal (hackathon deliverable, 11 evidence-backed entries)

Quick start (no API key needed)

Requires Node.js 20.9 or newer (the current Next.js 16 runtime floor).

npm install
npm test                      # 70 unit/integration tests
npm run demo:agent            # agent → MCP server pre-trade check (PROCEED 3/100, DECLINE 95/100)
npm run validate              # 16-contract validation: ordering + Spearman rho (docs/validation.md)
npx tsx scripts/agreement.ts  # 3 blind runs vs reference: band agreement 16/16 (docs/agreement.md)
npm run score:all             # score live testnet markets + curated set -> data/scores/
npm run dev -w @levelfield/web    # UI at localhost:3000
rm -rf apps/web/.next && npm run build -w @levelfield/web  # production build — the rm -rf is
                               # required if a `dev` session ran in this directory first, else
                               # `next build` can report success but leave a `.next/` with no
                               # BUILD_ID, which `next start` then refuses to serve
npm run mcp                   # stdio MCP server (see packages/mcp/README.md)
npx tsx scripts/probe-dreamdex.ts          # indexer field-coverage probe
npx tsx scripts/verify-classifications.ts  # re-verify all evidence quotes
# after setting the public repo/immutable commit and republishing provenance-complete attestations:
GITHUB_REPO=OWNER/REPO GITHUB_REF="$(git rev-parse HEAD)" npm run verify:onchain

Status

• Anchor library v1 (5 dimensions × 5 levels, reference cases)

• Deterministic scoring engine + circuit breakers (unit-tested)

• Rule classifier for live price binaries (typed fields only)

• Reference classifications for the curated spectrum (80 quotes verified)

• Batch scorer + score cache (live + curated, ACDC gradient reproduced)

• MCP server (protocol / score / anchors), verified over stdio

• Web UI (auditable snapshot, market evidence, methodology, local assessment workspace)

• ScoreRegistry deployed on Somnia Shannon: 0xb8e11dea346f2c961880879606a269db3165bbc7 — 28 legacy attestations published. Before final release, set GITHUB_REPO=owner/repo and GITHUB_REF to the immutable submission commit SHA, republish the current score index to replace legacy placeholder URIs, then run npm run verify:onchain for a complete field-by-field read-back snapshot.

• Validation harness: 16 contracts, category medians strictly ordered, Spearman ρ = 0.93 (docs/validation.md)

• Inter-run agreement: 3 independent blind classifiers, majority-vote band matches the reference 16/16 (docs/agreement.md)

• Final SDK feedback report (docs/sdk-feedback-report.md)

• Required 2–3 minute demo video — master complete (demo-video/levelfield-demo.mp4, 2:55, QA chain in demo-video/film/README.md); YouTube upload pending

• Optional presentation deck

What this is not

• Not a prediction of any outcome, and not trading advice.

• Not live insider detection: the score reflects the structural risk of the event type, not activity in a specific market right now.

• Not an accusation against any person or account.