feat: add Laravel Fortify skill, condense API data rules, add homepage mockup

.claude/rules/scoring.md

@@ -8,52 +8,30 @@ Never guess — stay silent (no_signal) when signals conflict or data is insuffi
 
 ## The 5 signals (in priority order)
 
-### Signal 1 — Local price trend (HIGHEST WEIGHT)
-- Query `station_prices` for user's nearest 5 stations (within 5km of user lat/lng)
-- Use last 14 days of history for `e10` (or user's preferred fuel type)
-- **Use linear regression, not rolling averages:**
-    - Run least-squares regression on `(recorded_at, price_pence)` pairs
-    - Calculate slope (pence/day) and R² (goodness of fit, 0–1)
-    - Only use the regression result if R² ≥ 0.5 — below that, data is too noisy
-    - Use adaptive lookback: try 5 days first (best signal on sharp moves), fall back to 14 days if R² < 0.5
-- **Falling**: slope ≤ -0.3p/day AND R² ≥ 0.5 → wait signal, points scale with slope magnitude
-- **Rising**: slope ≥ +0.3p/day AND R² ≥ 0.5 → fill_up signal
-- **Flat / noisy**: |slope| < 0.3 OR R² < 0.5 → no signal from this source
-- Store slope, R², lookback_days, and data_points in signal output
-- Weight: 40 points max
+### Signal 1 — Local price trend (40 pts max)
+- Nearest 5 stations within 5km; user's preferred fuel type
+- Least-squares regression on `(recorded_at, price_pence)`; adaptive lookback: 5 days first, fall back to 14 if R² < 0.5
+- Slope ≤ -0.3p/day AND R² ≥ 0.5 → wait; slope ≥ +0.3p/day AND R² ≥ 0.5 → fill_up; otherwise no signal
+- Store: slope, R², lookback_days, data_points
 
-### Signal 2 — Supermarket anchor effect (HIGH WEIGHT)
-- Find nearest supermarket station (is_supermarket = 1) within 10km
-- Check if supermarket cut price in last 48 hours (> 1p drop)
-- Check if nearest non-supermarket stations have NOT yet followed
-- If supermarket cut AND independents haven't moved → strong wait signal
-- Also check the inverse: if supermarket RAISED and independents haven't → mild fill_up
-- Weight: 35 points max
+### Signal 2 — Supermarket anchor effect (35 pts max)
+- Nearest supermarket (is_supermarket = 1) within 10km
+- Supermarket cut > 1p in last 48h AND independents haven't followed → wait
+- Inverse (supermarket raised, independents haven't) → mild fill_up
 
-### Signal 3 — Day-of-week pattern (MEDIUM WEIGHT — needs 8+ weeks data)
-- Per station: average price by day-of-week over last 90 days
-- Only activate if station has 56+ days of history
-- If today is statistically 1.5p+ cheaper than weekly average → mild fill_up
-- If today is statistically 1.5p+ more expensive → mild wait
-- Weight: 15 points max
+### Signal 3 — Day-of-week pattern (15 pts max)
+- Requires 56+ days of station history; average price by day-of-week over last 90 days
+- Today 1.5p+ below weekly average → mild fill_up; 1.5p+ above → mild wait
 
-### Signal 4 — Brent crude direction (LOW WEIGHT)
-- Read from `price_predictions` table — never query `brent_prices` directly in scoring
-- `OilPriceService::generatePrediction()` runs daily at 7am and writes the prediction
-- LLM (`source = 'llm'`) is preferred; EWMA (`source = 'ewma'`) is the fallback
-- Direction `rising` → mild fill_up pressure; `falling` → mild wait; `flat` → no signal
-- Points awarded proportionally to confidence: `(confidence / 100) * 10`
-- Weight: 10 points max
+### Signal 4 — Brent crude direction (10 pts max)
+- Read from `price_predictions` table only (never query `brent_prices` in scoring)
+- LLM (`source='llm'`) preferred; EWMA fallback. Points = `(confidence / 100) * 10`
+- `rising` → fill_up; `falling` → wait; `flat` → no signal
 
-### Signal 5 — Price stickiness (CONFIDENCE MODIFIER)
-- Per station: calculate average hold duration (days between price changes) from history
-- Requires 30+ days of history to activate
-- Use as a confidence modifier, not a directional signal:
-    - avg hold < 2 days → reduce overall confidence by 5 points (volatile, hard to predict)
-    - avg hold 2–4 days → neutral, no adjustment
-    - avg hold > 5 days → increase overall confidence by 5 points (predictable, sticky)
-- Store avg_hold_days and data_points in signal output
-- Applied after all other signals are summed (±5 points)
+### Signal 5 — Price stickiness (confidence modifier, ±5 pts)
+- Requires 30+ days history. Applied after all signals are summed.
+- avg hold < 2 days → -5 pts; 2–4 days → 0; > 5 days → +5 pts
+- Store: avg_hold_days, data_points
 
 ## Confidence thresholds
 
@@ -99,18 +77,9 @@ Reason strings are stored in `scoring_results.signals` JSON and shown in the UI
 
 ## Data quality — anomaly rejection
 
-The Fuel Finder API contains dirty data (live example: 1369.0p/litre in national index).
-Reject a price record before storing or scoring if:
-- `price_pence > 25000` (over 250p/litre — physically implausible for UK pump prices)
-- `price_pence < 10000` (under 100p/litre — almost certainly a decimal entry error)
-- Price changed by more than 20p in a single update from the same station
-  (flag for review, do not use in scoring)
-
-Log rejected records to an `anomalous_prices` table for monitoring.
-Never let a dirty data point skew the regression slope or collapse R².
+Reject before storing or scoring: `price_pence > 25000` or `< 10000`, or single-update change > 20p (flag for review).
+Log to `anomalous_prices` table. Never let dirty data skew regression slope or collapse R².
 
 ## Accuracy self-tracking
 
-After 3 days, check if `wait` recommendation was correct (prices did fall further).
-Store outcome in `scoring_results` for future display:
-"This signal has been right X% of the time in your area."
+After 3 days, check if `wait` was correct (prices fell further). Store outcome in `scoring_results` for display.