FAILURE MAP
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FA-86236 / Game economy crafting balance / Open access

Profession skill-up colors: Skill passes the hard cap · case 01

Crafting at 300 raises skill to 301.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The cap check is strict.

VERIFIED REPAIR

Restore `if skill >= 300:` at the skill cap step.

Unsuccessful approach: Adding ups double-counts gains already folded into skill.

Case contract

recipe_levels = [learn, yellow, green, gray]. Skill below learn cannot craft (error "too low"). Per roll (0..99), while skill < 300: chance 100 below yellow (orange), 75 below green, 25 below gray, else 0; the skill goes up when roll < chance and the color is re-evaluated each craft.

Why this case matters

Game economies leak or destroy currency when one crafting or pricing rule is off by one boundary, rounding stage or state update; the defect is observable in exact integer outcomes.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(skill, recipe_levels, rolls):
    learn, yellow, green, gray = recipe_levels
    if skill < learn:
        return {'skill': skill, 'ups': 0, 'error': 'too low'}
    ups = 0
    for r in rolls:
        if skill > 300:
            break
        if skill < yellow:
            chance = 100
        elif skill < green:
            chance = 75
        elif skill < gray:
            chance = 25
        else:
            chance = 0
        if r < chance:
            skill += 1
            ups += 1
    return {'skill': skill, 'ups': ups}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('fault site skill cap #1',
   [299, [277, 291, 302, 311], [74, 74, 74, 75, 14, 25, 25, 99]],
   {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [291, [288, 328, 330, 340], [50, 50, 0, 0, 0, 50, 0, 1, 50, 0, 50]],
   {'skill': 300, 'ups': 9}),
  ('regression skill cap #2',
   [291, [289, 309, 330, 340], [0, 0, 1, 1, 1, 50, 0, 1, 1, 1]],
   {'skill': 300, 'ups': 9}),
  ('partial repair boundary #1',
   [278, [271, 282, 330, 340], [0, 0, 50, 50, 50, 1, 0, 50, 1, 1, 50, 1]],
   {'skill': 290, 'ups': 12}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [291, [288, 328, 330, 340], [50, 50, 0, 0, 0, 50, 0, 1, 50, 0, 50]],
   {'skill': 300, 'ups': 9}),
  ('regression skill cap #2',
   [291, [289, 309, 330, 340], [0, 0, 1, 1, 1, 50, 0, 1, 1, 1]],
   {'skill': 300, 'ups': 9}),
  ('partial repair boundary #1',
   [278, [271, 282, 330, 340], [0, 0, 50, 50, 50, 1, 0, 50, 1, 1, 50, 1]],
   {'skill': 290, 'ups': 12}),
  ('regression skill cap #3',
   [296, [288, 306, 330, 340], [50, 1, 50, 1, 0, 50, 1]],
   {'skill': 300, 'ups': 4}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [296, [288, 306, 330, 340], [50, 1, 50, 1, 0, 50, 1]],
   {'skill': 300, 'ups': 4}),
  ('regression skill cap #2',
   [293, [292, 315, 330, 340], [0, 50, 1, 50, 50, 50, 1, 0, 50]],
   {'skill': 300, 'ups': 7}),
  ('partial repair boundary #1',
   [283, [277, 302, 330, 340], [1, 1, 1, 50, 0, 0, 0, 50, 1, 1]],
   {'skill': 293, 'ups': 10}),
  ('regression skill cap #3',
   [295, [291, 301, 330, 340], [50, 50, 1, 1, 1, 0, 50, 1, 0, 1, 0]],
   {'skill': 300, 'ups': 5}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [295, [291, 301, 330, 340], [50, 50, 1, 1, 1, 0, 50, 1, 0, 1, 0]],
   {'skill': 300, 'ups': 5}),
  ('fault site skill cap #1',
   [299, [292, 308, 330, 340], [50, 1, 1, 0, 1, 0, 1, 1]],
   {'skill': 300, 'ups': 1}),
  ('partial repair boundary #1',
   [287, [281, 296, 330, 340], [1, 1, 50, 0, 0, 0, 50, 1, 1, 50, 0]],
   {'skill': 298, 'ups': 11}),
  ('partial repair boundary #2',
   [293, [288, 298, 330, 340], [50, 0, 50, 0, 0, 50]],
   {'skill': 299, 'ups': 6}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [84, [64, 70, 80, 85], [0, 99, 75, 25, 25, 99, 75, 0]], {'skill': 85, 'ups': 1})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [292, [286, 325, 330, 340], [50, 1, 50, 0, 50, 0, 0, 50, 1]],
   {'skill': 300, 'ups': 8}),
  ('fault site skill cap #1',
   [299, [270, 285, 297, 302], [24, 24, 74, 24, 47, 0, 99]],
   {'skill': 300, 'ups': 1}),
  ('partial repair boundary #1',
   [293, [288, 298, 330, 340], [50, 0, 50, 0, 0, 50]],
   {'skill': 299, 'ups': 6}),
  ('partial repair boundary #2',
   [286, [281, 305, 330, 340], [50, 1, 1, 0, 50, 0, 50, 0, 0]],
   {'skill': 295, 'ups': 9}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [289, [288, 314, 330, 340], [50, 0, 50, 0, 0]], {'skill': 294, 'ups': 5})]]
for label, args, expected in cases[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
at cap #1{'skill': 301, 'ups': 2}{'skill': 300, 'ups': 1}Failed
fault site skill cap #1{'skill': 301, 'ups': 2}{'skill': 300, 'ups': 1}Failed
regression skill cap #1{'skill': 301, 'ups': 10}{'skill': 300, 'ups': 9}Failed
regression skill cap #2{'skill': 301, 'ups': 10}{'skill': 300, 'ups': 9}Failed
partial repair boundary #1{'skill': 290, 'ups': 12}{'skill': 290, 'ups': 12}Passed
exactly at learn level #1{'skill': 51, 'ups': 1}{'skill': 51, 'ups': 1}Passed
gray recipe roll zero #1{'skill': 90, 'ups': 0}{'skill': 90, 'ups': 0}Passed
control #1{'skill': 23, 'ups': 0}{'skill': 23, 'ups': 0}Passed

SHA-256 / f97f057dfd83687c78b5687006316833cb6c656cdcd78caa1f7d2ea9c95db6e0

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(skill, recipe_levels, rolls):
    learn, yellow, green, gray = recipe_levels
    if skill < learn:
        return {'skill': skill, 'ups': 0, 'error': 'too low'}
    ups = 0
    for r in rolls:
        if skill + ups >= 300:
            break
        if skill < yellow:
            chance = 100
        elif skill < green:
            chance = 75
        elif skill < gray:
            chance = 25
        else:
            chance = 0
        if r < chance:
            skill += 1
            ups += 1
    return {'skill': skill, 'ups': ups}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('fault site skill cap #1',
   [299, [277, 291, 302, 311], [74, 74, 74, 75, 14, 25, 25, 99]],
   {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [291, [288, 328, 330, 340], [50, 50, 0, 0, 0, 50, 0, 1, 50, 0, 50]],
   {'skill': 300, 'ups': 9}),
  ('regression skill cap #2',
   [291, [289, 309, 330, 340], [0, 0, 1, 1, 1, 50, 0, 1, 1, 1]],
   {'skill': 300, 'ups': 9}),
  ('partial repair boundary #1',
   [278, [271, 282, 330, 340], [0, 0, 50, 50, 50, 1, 0, 50, 1, 1, 50, 1]],
   {'skill': 290, 'ups': 12}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [291, [288, 328, 330, 340], [50, 50, 0, 0, 0, 50, 0, 1, 50, 0, 50]],
   {'skill': 300, 'ups': 9}),
  ('regression skill cap #2',
   [291, [289, 309, 330, 340], [0, 0, 1, 1, 1, 50, 0, 1, 1, 1]],
   {'skill': 300, 'ups': 9}),
  ('partial repair boundary #1',
   [278, [271, 282, 330, 340], [0, 0, 50, 50, 50, 1, 0, 50, 1, 1, 50, 1]],
   {'skill': 290, 'ups': 12}),
  ('regression skill cap #3',
   [296, [288, 306, 330, 340], [50, 1, 50, 1, 0, 50, 1]],
   {'skill': 300, 'ups': 4}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [296, [288, 306, 330, 340], [50, 1, 50, 1, 0, 50, 1]],
   {'skill': 300, 'ups': 4}),
  ('regression skill cap #2',
   [293, [292, 315, 330, 340], [0, 50, 1, 50, 50, 50, 1, 0, 50]],
   {'skill': 300, 'ups': 7}),
  ('partial repair boundary #1',
   [283, [277, 302, 330, 340], [1, 1, 1, 50, 0, 0, 0, 50, 1, 1]],
   {'skill': 293, 'ups': 10}),
  ('regression skill cap #3',
   [295, [291, 301, 330, 340], [50, 50, 1, 1, 1, 0, 50, 1, 0, 1, 0]],
   {'skill': 300, 'ups': 5}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [295, [291, 301, 330, 340], [50, 50, 1, 1, 1, 0, 50, 1, 0, 1, 0]],
   {'skill': 300, 'ups': 5}),
  ('fault site skill cap #1',
   [299, [292, 308, 330, 340], [50, 1, 1, 0, 1, 0, 1, 1]],
   {'skill': 300, 'ups': 1}),
  ('partial repair boundary #1',
   [287, [281, 296, 330, 340], [1, 1, 50, 0, 0, 0, 50, 1, 1, 50, 0]],
   {'skill': 298, 'ups': 11}),
  ('partial repair boundary #2',
   [293, [288, 298, 330, 340], [50, 0, 50, 0, 0, 50]],
   {'skill': 299, 'ups': 6}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [84, [64, 70, 80, 85], [0, 99, 75, 25, 25, 99, 75, 0]], {'skill': 85, 'ups': 1})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [292, [286, 325, 330, 340], [50, 1, 50, 0, 50, 0, 0, 50, 1]],
   {'skill': 300, 'ups': 8}),
  ('fault site skill cap #1',
   [299, [270, 285, 297, 302], [24, 24, 74, 24, 47, 0, 99]],
   {'skill': 300, 'ups': 1}),
  ('partial repair boundary #1',
   [293, [288, 298, 330, 340], [50, 0, 50, 0, 0, 50]],
   {'skill': 299, 'ups': 6}),
  ('partial repair boundary #2',
   [286, [281, 305, 330, 340], [50, 1, 1, 0, 50, 0, 50, 0, 0]],
   {'skill': 295, 'ups': 9}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [289, [288, 314, 330, 340], [50, 0, 50, 0, 0]], {'skill': 294, 'ups': 5})]]
for label, args, expected in cases[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
at cap #1{'skill': 300, 'ups': 1}{'skill': 300, 'ups': 1}Passed
fault site skill cap #1{'skill': 300, 'ups': 1}{'skill': 300, 'ups': 1}Passed
regression skill cap #1{'skill': 296, 'ups': 5}{'skill': 300, 'ups': 9}Failed
regression skill cap #2{'skill': 296, 'ups': 5}{'skill': 300, 'ups': 9}Failed
partial repair boundary #1{'skill': 289, 'ups': 11}{'skill': 290, 'ups': 12}Failed
exactly at learn level #1{'skill': 51, 'ups': 1}{'skill': 51, 'ups': 1}Passed
gray recipe roll zero #1{'skill': 90, 'ups': 0}{'skill': 90, 'ups': 0}Passed
control #1{'skill': 23, 'ups': 0}{'skill': 23, 'ups': 0}Passed

SHA-256 / ada66968ca0b5087e5c5ecf6d3549b33ba0dfd7e83b79b11112ab04a8cfdb791

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(skill, recipe_levels, rolls):
    learn, yellow, green, gray = recipe_levels
    if skill < learn:
        return {'skill': skill, 'ups': 0, 'error': 'too low'}
    ups = 0
    for r in rolls:
        if skill >= 300:
            break
        if skill < yellow:
            chance = 100
        elif skill < green:
            chance = 75
        elif skill < gray:
            chance = 25
        else:
            chance = 0
        if r < chance:
            skill += 1
            ups += 1
    return {'skill': skill, 'ups': ups}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('fault site skill cap #1',
   [299, [277, 291, 302, 311], [74, 74, 74, 75, 14, 25, 25, 99]],
   {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [291, [288, 328, 330, 340], [50, 50, 0, 0, 0, 50, 0, 1, 50, 0, 50]],
   {'skill': 300, 'ups': 9}),
  ('regression skill cap #2',
   [291, [289, 309, 330, 340], [0, 0, 1, 1, 1, 50, 0, 1, 1, 1]],
   {'skill': 300, 'ups': 9}),
  ('partial repair boundary #1',
   [278, [271, 282, 330, 340], [0, 0, 50, 50, 50, 1, 0, 50, 1, 1, 50, 1]],
   {'skill': 290, 'ups': 12}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [291, [288, 328, 330, 340], [50, 50, 0, 0, 0, 50, 0, 1, 50, 0, 50]],
   {'skill': 300, 'ups': 9}),
  ('regression skill cap #2',
   [291, [289, 309, 330, 340], [0, 0, 1, 1, 1, 50, 0, 1, 1, 1]],
   {'skill': 300, 'ups': 9}),
  ('partial repair boundary #1',
   [278, [271, 282, 330, 340], [0, 0, 50, 50, 50, 1, 0, 50, 1, 1, 50, 1]],
   {'skill': 290, 'ups': 12}),
  ('regression skill cap #3',
   [296, [288, 306, 330, 340], [50, 1, 50, 1, 0, 50, 1]],
   {'skill': 300, 'ups': 4}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [296, [288, 306, 330, 340], [50, 1, 50, 1, 0, 50, 1]],
   {'skill': 300, 'ups': 4}),
  ('regression skill cap #2',
   [293, [292, 315, 330, 340], [0, 50, 1, 50, 50, 50, 1, 0, 50]],
   {'skill': 300, 'ups': 7}),
  ('partial repair boundary #1',
   [283, [277, 302, 330, 340], [1, 1, 1, 50, 0, 0, 0, 50, 1, 1]],
   {'skill': 293, 'ups': 10}),
  ('regression skill cap #3',
   [295, [291, 301, 330, 340], [50, 50, 1, 1, 1, 0, 50, 1, 0, 1, 0]],
   {'skill': 300, 'ups': 5}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [23, [8, 14, 23, 38], [75]], {'skill': 23, 'ups': 0})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [295, [291, 301, 330, 340], [50, 50, 1, 1, 1, 0, 50, 1, 0, 1, 0]],
   {'skill': 300, 'ups': 5}),
  ('fault site skill cap #1',
   [299, [292, 308, 330, 340], [50, 1, 1, 0, 1, 0, 1, 1]],
   {'skill': 300, 'ups': 1}),
  ('partial repair boundary #1',
   [287, [281, 296, 330, 340], [1, 1, 50, 0, 0, 0, 50, 1, 1, 50, 0]],
   {'skill': 298, 'ups': 11}),
  ('partial repair boundary #2',
   [293, [288, 298, 330, 340], [50, 0, 50, 0, 0, 50]],
   {'skill': 299, 'ups': 6}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [84, [64, 70, 80, 85], [0, 99, 75, 25, 25, 99, 75, 0]], {'skill': 85, 'ups': 1})],
 [('at cap #1', [299, [200, 250, 275, 320], [0, 0]], {'skill': 300, 'ups': 1}),
  ('regression skill cap #1',
   [292, [286, 325, 330, 340], [50, 1, 50, 0, 50, 0, 0, 50, 1]],
   {'skill': 300, 'ups': 8}),
  ('fault site skill cap #1',
   [299, [270, 285, 297, 302], [24, 24, 74, 24, 47, 0, 99]],
   {'skill': 300, 'ups': 1}),
  ('partial repair boundary #1',
   [293, [288, 298, 330, 340], [50, 0, 50, 0, 0, 50]],
   {'skill': 299, 'ups': 6}),
  ('partial repair boundary #2',
   [286, [281, 305, 330, 340], [50, 1, 1, 0, 50, 0, 50, 0, 0]],
   {'skill': 295, 'ups': 9}),
  ('exactly at learn level #1', [50, [50, 60, 70, 80], [0]], {'skill': 51, 'ups': 1}),
  ('gray recipe roll zero #1', [90, [50, 60, 70, 80], [0, 0]], {'skill': 90, 'ups': 0}),
  ('control #1', [289, [288, 314, 330, 340], [50, 0, 50, 0, 0]], {'skill': 294, 'ups': 5})]]
for label, args, expected in cases[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
at cap #1{'skill': 300, 'ups': 1}{'skill': 300, 'ups': 1}Passed
fault site skill cap #1{'skill': 300, 'ups': 1}{'skill': 300, 'ups': 1}Passed
regression skill cap #1{'skill': 300, 'ups': 9}{'skill': 300, 'ups': 9}Passed
regression skill cap #2{'skill': 300, 'ups': 9}{'skill': 300, 'ups': 9}Passed
partial repair boundary #1{'skill': 290, 'ups': 12}{'skill': 290, 'ups': 12}Passed
exactly at learn level #1{'skill': 51, 'ups': 1}{'skill': 51, 'ups': 1}Passed
gray recipe roll zero #1{'skill': 90, 'ups': 0}{'skill': 90, 'ups': 0}Passed
control #1{'skill': 23, 'ups': 0}{'skill': 23, 'ups': 0}Passed

SHA-256 / 8a7d36d2a8811cc6ba05c68d69e0135c391bf213fb96cce305a8841736ec8409

Verification & scope

Deterministic toy contract stipulated for this model; integer or exact arithmetic only, not a reproduction of any specific game engine. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:50:47.635223+00:00.

Case digest / 0fb7a414c978a6f838bb1d4a7fdf06e30b042b880aa04e1654d66415249806b9