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#!/usr/bin/env python3
"""SkillOpt eval-only: run a single skill on a dataset without training.
Usage
-----
python scripts/eval_only.py \
--config configs/spreadsheetbench/default.yaml \
--skill skillopt/envs/spreadsheetbench/skills/initial.md \
--split_dir /path/to/split \
--out_root outputs/eval_skill0
All YAML keys can be overridden from the CLI, same as train.py.
"""
from __future__ import annotations
import argparse
import datetime
import json
import os
import sys
_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
_PROJECT_ROOT = os.path.dirname(_SCRIPT_DIR)
if _PROJECT_ROOT not in sys.path:
sys.path.insert(0, _PROJECT_ROOT)
from skillopt.model import (
configure_azure_openai,
configure_claude_code_exec,
configure_codex_exec,
configure_qwen_chat,
set_reasoning_effort,
set_target_backend,
set_target_deployment,
set_optimizer_backend,
set_optimizer_deployment,
)
from skillopt.model.common import default_model_for_backend, normalize_backend_name
_OPENAI_DEFAULT_MODEL_SENTINELS = {"gpt-5.4", "gpt-5.5"}
from skillopt.utils import compute_score
# ── Reuse registry from train.py ───────────────────────────────────────────
_ENV_REGISTRY: dict[str, type] = {}
def _register_builtins() -> None:
try:
from skillopt.envs.alfworld.adapter import ALFWorldAdapter
_ENV_REGISTRY["alfworld"] = ALFWorldAdapter
except ImportError:
pass
try:
from skillopt.envs.searchqa.adapter import SearchQAAdapter
_ENV_REGISTRY["searchqa"] = SearchQAAdapter
except ImportError:
pass
try:
from skillopt.envs.livemathematicianbench.adapter import LiveMathematicianBenchAdapter
_ENV_REGISTRY["livemathematicianbench"] = LiveMathematicianBenchAdapter
except ImportError:
pass
try:
from skillopt.envs.babyvision.adapter import BabyVisionAdapter
_ENV_REGISTRY["babyvision"] = BabyVisionAdapter
except ImportError:
pass
try:
from skillopt.envs.spreadsheetbench.adapter import SpreadsheetBenchAdapter
_ENV_REGISTRY["spreadsheetbench"] = SpreadsheetBenchAdapter
except ImportError:
pass
try:
from skillopt.envs.mmrb.adapter import MMRBAdapter
_ENV_REGISTRY["mmrb"] = MMRBAdapter
except ImportError:
pass
try:
from skillopt.envs.docvqa.adapter import DocVQAAdapter
_ENV_REGISTRY["docvqa"] = DocVQAAdapter
except ImportError:
pass
try:
from skillopt.envs.mathverse.adapter import MathVerseAdapter
_ENV_REGISTRY["mathverse"] = MathVerseAdapter
except ImportError:
pass
try:
from skillopt.envs.officeqa.adapter import OfficeQAAdapter
_ENV_REGISTRY["officeqa"] = OfficeQAAdapter
except ImportError:
pass
try:
from skillopt.envs.sealqa.adapter import SealQAAdapter
_ENV_REGISTRY["sealqa"] = SealQAAdapter
except ImportError:
pass
try:
from skillopt.envs.swebench.adapter import SWEBenchAdapter
_ENV_REGISTRY["swebench"] = SWEBenchAdapter
except ImportError:
pass
def get_adapter(cfg: dict):
_register_builtins()
env_name = cfg.get("env", "alfworld")
if env_name not in _ENV_REGISTRY:
raise ValueError(
f"Unknown environment '{env_name}'. "
f"Available: {list(_ENV_REGISTRY.keys())}"
)
adapter_cls = _ENV_REGISTRY[env_name]
import inspect
sig = inspect.signature(adapter_cls.__init__)
accepted = set(sig.parameters.keys()) - {"self"}
adapter_kwargs = {k: cfg[k] for k in accepted if k in cfg}
return adapter_cls(**adapter_kwargs)
# ── CLI ────────────────────────────────────────────────────────────────────
_BOOL = lambda x: str(x).lower() in ("true", "1", "yes") # noqa: E731
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="SkillOpt eval-only")
p.add_argument("--config", type=str, required=True)
p.add_argument("--skill", type=str, required=True,
help="Path to skill .md file to evaluate")
p.add_argument("--split", type=str, default="all",
help="Which split to eval: train/valid_seen/valid_unseen/all (default: all)")
p.add_argument("--cfg-options", nargs="+", default=[],
help="Override config: section.key=value")
# Legacy flat overrides
p.add_argument("--env", type=str)
p.add_argument("--backend", type=str,
choices=["azure_openai", "codex", "codex_exec", "claude", "claude_chat", "claude_code_exec"])
p.add_argument("--optimizer_model", type=str)
p.add_argument("--target_model", type=str)
p.add_argument("--optimizer_backend", type=str)
p.add_argument("--target_backend", type=str)
p.add_argument("--reasoning_effort", type=str,
choices=["", "low", "medium", "high", "xhigh", "max"])
p.add_argument("--azure_endpoint", type=str)
p.add_argument("--azure_api_version", type=str)
p.add_argument("--azure_api_key", type=str)
p.add_argument("--azure_openai_endpoint", type=str)
p.add_argument("--azure_openai_api_version", type=str)
p.add_argument("--azure_openai_api_key", type=str)
p.add_argument("--azure_openai_auth_mode", type=str)
p.add_argument("--azure_openai_ad_scope", type=str)
p.add_argument("--azure_openai_managed_identity_client_id", type=str)
p.add_argument("--optimizer_azure_openai_endpoint", type=str)
p.add_argument("--optimizer_azure_openai_api_version", type=str)
p.add_argument("--optimizer_azure_openai_api_key", type=str)
p.add_argument("--optimizer_azure_openai_auth_mode", type=str)
p.add_argument("--optimizer_azure_openai_ad_scope", type=str)
p.add_argument("--optimizer_azure_openai_managed_identity_client_id", type=str)
p.add_argument("--target_azure_openai_endpoint", type=str)
p.add_argument("--target_azure_openai_api_version", type=str)
p.add_argument("--target_azure_openai_api_key", type=str)
p.add_argument("--target_azure_openai_auth_mode", type=str)
p.add_argument("--target_azure_openai_ad_scope", type=str)
p.add_argument("--target_azure_openai_managed_identity_client_id", type=str)
p.add_argument("--codex_exec_path", type=str)
p.add_argument("--codex_exec_sandbox", type=str)
p.add_argument("--codex_exec_profile", type=str)
p.add_argument("--codex_exec_full_auto", type=_BOOL)
p.add_argument("--codex_exec_reasoning_effort", type=str)
p.add_argument("--codex_exec_use_sdk", type=str)
p.add_argument("--codex_exec_network_access", type=_BOOL)
p.add_argument("--codex_exec_web_search", type=_BOOL)
p.add_argument("--codex_exec_approval_policy", type=str)
p.add_argument("--claude_code_exec_path", type=str)
p.add_argument("--claude_code_exec_profile", type=str)
p.add_argument("--claude_code_exec_use_sdk", type=str)
p.add_argument("--claude_code_exec_effort", type=str)
p.add_argument("--claude_code_exec_max_thinking_tokens", type=int)
p.add_argument("--out_root", type=str)
p.add_argument("--data_path", type=str)
p.add_argument("--split_mode", type=str,
choices=["ratio", "split_dir"])
p.add_argument("--split_ratio", type=str)
p.add_argument("--split_seed", type=int)
p.add_argument("--split_dir", type=str)
p.add_argument("--split_output_dir", type=str)
p.add_argument("--data_root", type=str)
p.add_argument("--max_turns", type=int)
p.add_argument("--workers", type=int)
p.add_argument("--max_api_workers", type=int)
p.add_argument("--seed", type=int)
p.add_argument("--test_env_num", type=int)
p.add_argument("--mode", type=str,
help="SpreadsheetBench: single/multi/react (default comes from config)")
return p.parse_args()
def main() -> None:
args = parse_args()
from skillopt.config import load_config as _load, flatten_config, is_structured
cfg = _load(args.config, overrides=args.cfg_options)
structured = is_structured(cfg)
# Apply legacy --key value overrides
cli = {k: v for k, v in vars(args).items()
if v is not None and k not in ("config", "skill", "split", "cfg_options")}
if cli:
if structured:
from skillopt.config import apply_overrides
_MAP = {
"backend": "model.backend",
"optimizer_model": "model.optimizer",
"target_model": "model.target",
"optimizer_backend": "model.optimizer_backend",
"target_backend": "model.target_backend",
"reasoning_effort": "model.reasoning_effort",
"azure_endpoint": "model.azure_endpoint",
"azure_api_version": "model.azure_api_version",
"azure_api_key": "model.azure_api_key",
"azure_openai_endpoint": "model.azure_openai_endpoint",
"azure_openai_api_version": "model.azure_openai_api_version",
"azure_openai_api_key": "model.azure_openai_api_key",
"azure_openai_auth_mode": "model.azure_openai_auth_mode",
"azure_openai_ad_scope": "model.azure_openai_ad_scope",
"azure_openai_managed_identity_client_id": "model.azure_openai_managed_identity_client_id",
"optimizer_azure_openai_endpoint": "model.optimizer_azure_openai_endpoint",
"optimizer_azure_openai_api_version": "model.optimizer_azure_openai_api_version",
"optimizer_azure_openai_api_key": "model.optimizer_azure_openai_api_key",
"optimizer_azure_openai_auth_mode": "model.optimizer_azure_openai_auth_mode",
"optimizer_azure_openai_ad_scope": "model.optimizer_azure_openai_ad_scope",
"optimizer_azure_openai_managed_identity_client_id": "model.optimizer_azure_openai_managed_identity_client_id",
"target_azure_openai_endpoint": "model.target_azure_openai_endpoint",
"target_azure_openai_api_version": "model.target_azure_openai_api_version",
"target_azure_openai_api_key": "model.target_azure_openai_api_key",
"target_azure_openai_auth_mode": "model.target_azure_openai_auth_mode",
"target_azure_openai_ad_scope": "model.target_azure_openai_ad_scope",
"target_azure_openai_managed_identity_client_id": "model.target_azure_openai_managed_identity_client_id",
"codex_exec_path": "model.codex_exec_path",
"codex_exec_sandbox": "model.codex_exec_sandbox",
"codex_exec_profile": "model.codex_exec_profile",
"codex_exec_full_auto": "model.codex_exec_full_auto",
"codex_exec_reasoning_effort": "model.codex_exec_reasoning_effort",
"codex_exec_use_sdk": "model.codex_exec_use_sdk",
"codex_exec_network_access": "model.codex_exec_network_access",
"codex_exec_web_search": "model.codex_exec_web_search",
"codex_exec_approval_policy": "model.codex_exec_approval_policy",
"claude_code_exec_path": "model.claude_code_exec_path",
"claude_code_exec_profile": "model.claude_code_exec_profile",
"claude_code_exec_use_sdk": "model.claude_code_exec_use_sdk",
"claude_code_exec_effort": "model.claude_code_exec_effort",
"claude_code_exec_max_thinking_tokens": "model.claude_code_exec_max_thinking_tokens",
"seed": "train.seed",
"test_env_num": "evaluation.test_env_num",
"env": "env.name",
"out_root": "env.out_root",
}
mapped = []
for k, v in cli.items():
dotted = _MAP.get(k)
if dotted:
mapped.append(f"{dotted}={v}")
else:
mapped.append(f"env.{k}={v}")
apply_overrides(cfg, mapped)
else:
cfg.update(cli)
cfg = flatten_config(cfg) if structured else cfg
for new_key, old_key in (
("azure_openai_endpoint", "azure_endpoint"),
("azure_openai_api_version", "azure_api_version"),
("azure_openai_api_key", "azure_api_key"),
):
if cfg.get(new_key) in (None, "") and cfg.get(old_key) not in (None, ""):
cfg[new_key] = cfg[old_key]
explicit_backend = getattr(args, "backend", None)
if explicit_backend is None:
for option in args.cfg_options or []:
key = str(option).split("=", 1)[0].strip()
if key == "model.backend":
explicit_backend = str(option).split("=", 1)[1].strip()
break
backend = normalize_backend_name(cfg.get("model_backend") or cfg.get("target_backend") or "azure_openai")
def _has_model_override(dotted_key: str, legacy_key: str) -> bool:
if getattr(args, legacy_key, None) is not None:
return True
for option in args.cfg_options or []:
key = str(option).split("=", 1)[0].strip()
if key == dotted_key:
return True
return False
if explicit_backend is not None:
backend = normalize_backend_name(explicit_backend)
cfg["model_backend"] = backend
if backend in {"claude", "claude_chat"}:
cfg.setdefault("optimizer_backend", "claude_chat")
cfg.setdefault("target_backend", "claude_chat")
elif backend in {"codex", "codex_exec"}:
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "codex_exec")
elif backend == "claude_code_exec":
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "claude_code_exec")
else:
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "openai_chat")
else:
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "openai_chat")
if cfg.get("optimizer_backend") == "claude_chat":
if (
str(cfg.get("optimizer_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS
and not _has_model_override("model.optimizer", "optimizer_model")
):
cfg["optimizer_model"] = default_model_for_backend("claude_chat")
if cfg.get("target_backend") == "claude_chat":
if (
str(cfg.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS
and not _has_model_override("model.target", "target_model")
):
cfg["target_model"] = default_model_for_backend("claude_chat")
if cfg.get("target_backend") == "claude_code_exec":
if (
str(cfg.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS
and not _has_model_override("model.target", "target_model")
):
cfg["target_model"] = default_model_for_backend("claude_chat")
if not cfg.get("out_root"):
env = cfg.get("env", "unknown")
model = cfg.get("target_model", "unknown").replace("/", "-")
ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
cfg["out_root"] = os.path.join("outputs", f"eval_{env}_{model}_{ts}")
cfg["out_root"] = os.path.abspath(cfg["out_root"])
out_root = cfg["out_root"]
os.makedirs(out_root, exist_ok=True)
# Load skill
skill_path = os.path.abspath(args.skill)
with open(skill_path) as f:
skill_content = f.read()
print(f" [skill] {skill_path} ({len(skill_content)} chars)")
# Configure models
configure_azure_openai(
endpoint=(cfg.get("azure_openai_endpoint") or cfg.get("azure_endpoint") or None),
api_version=(cfg.get("azure_openai_api_version") or cfg.get("azure_api_version") or None),
api_key=(cfg.get("azure_openai_api_key") or cfg.get("azure_api_key") or None),
auth_mode=cfg.get("azure_openai_auth_mode") or None,
ad_scope=cfg.get("azure_openai_ad_scope") or None,
managed_identity_client_id=cfg.get("azure_openai_managed_identity_client_id") or None,
optimizer_endpoint=cfg.get("optimizer_azure_openai_endpoint") or None,
optimizer_api_version=cfg.get("optimizer_azure_openai_api_version") or None,
optimizer_api_key=cfg.get("optimizer_azure_openai_api_key") or None,
optimizer_auth_mode=cfg.get("optimizer_azure_openai_auth_mode") or None,
optimizer_ad_scope=cfg.get("optimizer_azure_openai_ad_scope") or None,
optimizer_managed_identity_client_id=(
cfg.get("optimizer_azure_openai_managed_identity_client_id") or None
),
target_endpoint=cfg.get("target_azure_openai_endpoint") or None,
target_api_version=cfg.get("target_azure_openai_api_version") or None,
target_api_key=cfg.get("target_azure_openai_api_key") or None,
target_auth_mode=cfg.get("target_azure_openai_auth_mode") or None,
target_ad_scope=cfg.get("target_azure_openai_ad_scope") or None,
target_managed_identity_client_id=(
cfg.get("target_azure_openai_managed_identity_client_id") or None
),
)
set_optimizer_backend(cfg.get("optimizer_backend", "openai_chat"))
set_target_backend(cfg.get("target_backend", "openai_chat"))
set_optimizer_deployment(cfg.get("optimizer_model", default_model_for_backend(backend)))
set_target_deployment(cfg.get("target_model", default_model_for_backend(backend)))
configure_codex_exec(
path=cfg.get("codex_exec_path", "codex"),
sandbox=cfg.get("codex_exec_sandbox", "workspace-write"),
profile=cfg.get("codex_exec_profile", ""),
full_auto=cfg.get("codex_exec_full_auto", False),
reasoning_effort=cfg.get("codex_exec_reasoning_effort", "none"),
use_sdk=cfg.get("codex_exec_use_sdk", None),
network_access=cfg.get("codex_exec_network_access", False),
web_search=cfg.get("codex_exec_web_search", False),
approval_policy=cfg.get("codex_exec_approval_policy", "never"),
)
configure_claude_code_exec(
path=cfg.get("claude_code_exec_path", "claude"),
profile=cfg.get("claude_code_exec_profile", ""),
use_sdk=cfg.get("claude_code_exec_use_sdk", None),
effort=cfg.get("claude_code_exec_effort", cfg.get("reasoning_effort", "medium")),
max_thinking_tokens=cfg.get("claude_code_exec_max_thinking_tokens", 16384),
)
configure_qwen_chat(
base_url=cfg.get("qwen_chat_base_url") or None,
api_key=cfg.get("qwen_chat_api_key") or None,
temperature=cfg.get("qwen_chat_temperature"),
timeout_seconds=cfg.get("qwen_chat_timeout_seconds"),
max_tokens=cfg.get("qwen_chat_max_tokens"),
enable_thinking=cfg.get("qwen_chat_enable_thinking"),
target_base_url=cfg.get("target_qwen_chat_base_url") or None,
target_api_key=cfg.get("target_qwen_chat_api_key") or None,
target_temperature=cfg.get("target_qwen_chat_temperature"),
target_timeout_seconds=cfg.get("target_qwen_chat_timeout_seconds"),
target_max_tokens=cfg.get("target_qwen_chat_max_tokens"),
target_enable_thinking=cfg.get("target_qwen_chat_enable_thinking"),
)
set_reasoning_effort(cfg.get("reasoning_effort", "") or None)
# Build adapter
adapter = get_adapter(cfg)
adapter.setup(cfg)
seed = cfg.get("seed", 42)
split = args.split or "all"
if split == "all":
items = (
adapter.build_eval_env(0, "train", seed)
+ adapter.build_eval_env(0, "valid_seen", seed)
+ adapter.build_eval_env(0, "valid_unseen", seed)
)
else:
env_num = cfg.get("test_env_num", 0)
items = adapter.build_eval_env(env_num, split, seed)
print(f"\n [eval] split={split} items={len(items)}")
print(f" [eval] out_root={out_root}")
print(f"{'='*60}")
# Run rollout
results = adapter.rollout(items, skill_content, out_root)
# Score
hard, soft = compute_score(results)
print(f"\n{'='*60}")
print(f" Results: hard={hard:.4f} soft={soft:.4f} (n={len(results)})")
print(f"{'='*60}")
# Save summary
summary = {
"skill": skill_path,
"split": split,
"n_items": len(results),
"hard": hard,
"soft": soft,
}
with open(os.path.join(out_root, "eval_summary.json"), "w") as f:
json.dump(summary, f, indent=2, ensure_ascii=False)
print(f" Saved to: {out_root}")
if __name__ == "__main__":
main()