GEPA Coding Agent

[prompt_learning]
task_app_url = "http://localhost:8020"
algorithm = "gepa"

[prompt_learning.policy]
model = "gpt-4.1-mini"
provider = "openai"
inference_mode = "synth_hosted"

[prompt_learning.policy.context_override]
system_prompt = """You are an expert Rust developer implementing Pokemon TCG cards.

Your task: Implement card effects by editing Rust files with stub functions.

Key patterns:
- Use `def_id_matches(&card.def_id, "DF", NUMBER)` to identify cards
- Implement attack modifiers in `attack_override`
- Use `game.queue_prompt()` for user choices

Output requirements:
1. ACTUALLY EDIT files - replace TODO stubs with working code
2. Make sure code compiles (`cargo check`)
3. Make sure tests pass (`cargo test`)"""

[prompt_learning.gepa]
env_name = "engine_bench"

[prompt_learning.gepa.evaluation]
seeds = [0, 2, 7, 8, 9, 16, 22, 29, 35, 42, 51, 68, 77, 88, 99]
validation_seeds = [0, 9, 22, 42, 77]

[prompt_learning.gepa.population]
initial_size = 8
num_generations = 5
children_per_generation = 8

[prompt_learning.gepa.rollout]
budget = 200
max_concurrent = 6

[prompt_learning.gepa.unified_optimization]
enable_task_app_context_overrides = true
optimization_target = "unified"
mutable_files = ["AGENTS.md", ".codex/skills.yaml"]

[prompt_learning.policy.context_override]
file_artifacts = { "AGENTS.md" = """# Agent Instructions

## Primary Objective
Implement Pokemon TCG card effects in Rust.

## Rules
1. Read existing code patterns first
2. Follow the engine architecture
3. Run tests after implementation
""" }