Create core/config.py

core/config.py

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+from dataclasses import dataclass
+from typing import Dict
+
+@dataclass
+class ModelRate:
+    # Price per 1M tokens (USD). Keep provider-agnostic.
+    input_per_1m: float
+    output_per_1m: float
+    cached_input_per_1m: float = 0.0
+    reasoning_per_1m: float = 0.0  # optional
+
+@dataclass
+class SimConfig:
+    # Time model
+    minutes_per_tick: int = 30
+
+    # Budgeting
+    budget_usd_soft: float = 5.00
+    budget_usd_hard: float = 10.00
+
+    # Task system
+    initial_tasks: int = 24
+    new_tasks_per_day: int = 6
+    rework_probability_base: float = 0.08  # baseline chance a "completed" task needs rework
+
+    # Incident injection
+    incident_probability_per_day: float = 0.12  # creates urgent tasks
+    outage_probability_per_day: float = 0.05    # increases latency + failure chance
+
+    # Token simulation knobs (when not calling real LLMs)
+    est_prompt_tokens_per_task: int = 900
+    est_completion_tokens_per_task: int = 250
+    est_reasoning_tokens_per_task: int = 0
+
+DEFAULT_MODEL_RATES: Dict[str, ModelRate] = {
+    # These are placeholders. You can swap with your exact rate cards.
+    "economy": ModelRate(input_per_1m=0.20, output_per_1m=0.80, cached_input_per_1m=0.05),
+    "balanced": ModelRate(input_per_1m=1.50, output_per_1m=6.00, cached_input_per_1m=0.50),
+    "premium": ModelRate(input_per_1m=5.00, output_per_1m=15.00, cached_input_per_1m=1.25),
+}