Product identity + widen books for DISPLAY, model input byte-identical

IDENTITY (CLAUDE.md top + MASTER-PLAN header). VYNDR is a PREDICTIVE MODEL:
it projects what a player will DO and picks accurately. Market edge is a
BYPRODUCT of a good prediction, never the success criterion. Success =
the forecast is honest about its own confidence AND still ranks --
calibration and resolution, both. No edge/CLV term belongs in a pass/fail
gate; they are diagnostics we report, not thresholds a model must clear.
A model tuned to beat a closing line has been fitted to the market instead
of to the game.

Per-sport doctrine (Phillips 2022, classify by what players DO not by
position): each sport is its own model -- own variables, archetypes,
conditions, calibration, honest ceiling. Shared across sports: ONLY the
Bayesian inference math.

Truth Law: no fabricated data; honest-absent over invented; label
limitations in-band; provisional stays provisional until re-run;
documented is not verified.

PHASE 2 -- AGGREGATOR WIDENING (live). normalizeProps now emits every
DISPLAY book instead of 5 of 18. Before this we discarded 13 books of our
own accord and 64.8% of the MLB slate was invisible to users. Every prop
carries book_role (both/takeable/reference/dfs/offshore) so the display
layer can say WHAT a price is -- a fixed-payout DFS number and a two-way
sportsbook price are not interchangeable objects. Unknown books are still
dropped.

PHASE 3 -- MODEL GATE (the model does not move). bookRoles splits
MODEL_BOOKS (the legacy allow-list, character for character) from
DISPLAY_BOOKS. Both model paths re-filter before they pick a line:
gradeSlateService.dedupeProps (before first-row-wins AND before the limit)
and intradayRefreshService.indexOddsProps (which RE-GRADES at the current
line -- without the gate, widening would have silently moved locked lines
onto books the model has never been calibrated against). A test asserts
the graded set is byte-identical through the widening.

CURRENT_RULER_VERSION stays v1_first_book. The gate lifts only when the
MLB calibration is re-run on the consensus ruler and v2 is promoted.

HONEST FRAMING, recorded in the plan: this is an AGGREGATOR win and it
does NOT fix the model. WNBA still abstains -- a model problem, not a
coverage problem; it is better covered than MLB. MLB isotonic still
provisional. The consensus is MARKET, not SHARP: pinnacle, matchbook and
polymarket are 0% on both sports, so no sharp anchor exists in our feed.

Two superseded tests updated to stronger properties rather than deleted:
roleOf now names the KIND of book, and the normalizer test asserts the
display set widens WHILE the model set does not.

Gates: 4,027 tests / 322 suites green; next build exit 0.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc

CLAUDE.md

@@ -1,5 +1,49 @@
 # VYNDR — Claude Code Project Context
 
+---
+
+# 🔷 PRODUCT IDENTITY — READ FIRST, EVERY SESSION
+
+**VYNDR IS A PREDICTIVE MODEL.** It projects what a player will **DO**, and picks
+accurately. It reads and pulls the market apart — a student of the game that is
+also an aggregator.
+
+**Market edge is a BYPRODUCT of a good prediction. It is NEVER the success
+criterion.**
+
+> **SUCCESS = the forecast is honest about its own confidence AND still ranks.**
+> Calibration (does 60% mean 60%?) *and* resolution (do higher forecasts actually
+> hit more often?). Both, or it isn't working.
+
+**No edge or CLV term belongs in a pass/fail gate.** CLV and market-relative edge
+are *diagnostics we report*, never thresholds a model must clear to ship. A model
+that forecasts honestly and ranks correctly is working even in a week the market
+moved against it; a model tuned to beat a closing line has been fitted to the
+market instead of to the game.
+
+## PER-SPORT DOCTRINE
+*(Rashad Phillips, "Basketball Position Metric," 2022 — classify players by **what
+they do**, not by position labels.)*
+
+**Each sport is its OWN model** — its own variables, archetypes, conditions,
+calibration and honest ceiling. The **only** thing shared across sports is the
+**Bayesian inference math**. Never one model fit to all sports; never a sport
+stubbed in on another sport's template and counted as covered.
+
+## TRUTH LAW
+- **No fabricated data anywhere.** If it renders a number, it comes from the
+  database or it doesn't render. `Number(null) === 0` is the classic breach.
+- **Honest-absent beats invented.** An empty state is a valid answer.
+- **Label limitations in-band** — e.g. "market consensus, **not sharp**",
+  "RECORD BUILDING", "MODEL · LEARNING".
+- **Provisional results stay provisional until re-run.** A measurement taken
+  against an instrument that has since changed is not a result; it is a result
+  *pending*.
+- **Verify by inducing the real code path on demand** — never wait on a cron slot
+  to find out whether something works. Documented ≠ verified.
+
+---
+
 ## What This Is
 Sports betting intelligence SaaS. Real software product.
 Three tiers: Free (5 scans), Analyst ($19.99 / $14.99 founder), Desk ($49.99 / $34.99 founder).