codexopt.example.yaml

version: 1
targets:
  agents_files:
    - AGENTS.md
    - "**/AGENTS.md"
    - "**/AGENTS.override.md"
  skills_globs:
    - ".codex/skills/**/SKILL.md"
    - "**/.codex/skills/**/SKILL.md"
    - ".agents/skills/**/SKILL.md"
    - "**/.agents/skills/**/SKILL.md"
  exclude_globs:
    - ".git/**"
    - ".codexopt/**"
    - ".venv/**"
    - "node_modules/**"
    - "reference/**"
output:
  root_dir: ".codexopt"
evidence:
  task_files: []
  issue_files: []
optimization:
  engine: "heuristic"
  min_apply_delta: 0.01
  max_metric_calls: 60
  reflection_model: null
  skillopt_train_ratio: 0.67
  skillopt_edit_budget: 24
  skillopt_validation_delta: 0.01
# Settings for the GEPA-concept-aligned reflective engine (`--engine reflective`,
# also used by `codexopt improve`). It evolves a document against real Codex
# rollouts scored by a tiered reward (verifier -> LLM-judge -> static).
reflective:
  # Model specs: "codex" (uses `codex exec`, no API key), "openai/<model>",
  # "<model>", or null to disable that role. Defaults stay offline; use
  # `codexopt improve --live` to opt into Codex-backed rollouts and mutation.
  optimizer_model: null        # reflective-mutation LLM; null -> heuristic proposer (weak signal)
  judge_model: null            # LLM-judge for trajectories; null disables the judge tier
  reward_mode: "tiered"        # tiered | verifier | judge | static
  minibatch_size: 3
  max_iterations: 6            # reflect -> mutate -> gate iterations (optimization budget)
  edit_budget: 12              # max line-edit operations per mutation (gradient clipping)
  valset_ratio: 0.34           # held-out fraction used by the validation gate
  max_rollouts: 60             # hard cap on Codex/verifier executions per run (cost guard)
  seed: 0
  codex_binary: "codex"