chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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from __future__ import annotations
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import argparse
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import asyncio
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import sys
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from pathlib import Path
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from typing import cast
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from openai.types.responses import ResponseTextDeltaEvent
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from agents import Runner
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from agents.items import TResponseInputItem
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from agents.run import RunConfig
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from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
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from agents.sandbox.capabilities import Capabilities, Skills
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from agents.sandbox.entries import Dir, GitRepo, LocalFile
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if __package__ is None or __package__ == "":
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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DATA_PATH = Path(__file__).resolve().parent / "data"
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W2_PATH = DATA_PATH / "sample_w2.pdf"
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FORM_1040_PATH = DATA_PATH / "f1040.pdf"
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DEFAULT_IMAGE = "tax-prep:latest"
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DEFAULT_SKILLS_REPO = "sdcoffey/tax-prep-skills"
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DEFAULT_SKILLS_REF = "main"
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DEFAULT_QUESTION = "Please generate a 1040 for filing year 2025."
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INSTRUCTIONS = """
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You are a federal tax filing agent. Your job is to compute year-end taxes and
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produce a filled-out Form 1040 for the specified tax year using the user's
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provided documents. Use only the information in the supplied files. If required
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data is missing or unclear, ask follow-up questions or note explicit
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assumptions. Save the finalized, filled PDF in the `output/` directory and
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provide a short summary of key amounts such as income, deductions, tax, and
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refund or amount due.
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This is a demo, so assume the following unless the workspace says otherwise:
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1. Filing status is single.
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2. SSN is 123-45-6789.
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3. Date of birth is 1991-01-01.
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4. There are no other income documents.
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5. If a minor data point is still needed, make up a clearly synthetic test value.
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Use the `federal-tax-prep` skill to accomplish this task.
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""".strip()
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def _require_docker_dependency():
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try:
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from docker import from_env as docker_from_env # type: ignore[import-untyped]
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except Exception as exc: # pragma: no cover - import path depends on local Docker setup
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raise SystemExit(
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"Docker-backed runs require the Docker SDK.\n"
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"Install the repo dependencies with: make sync"
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) from exc
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from agents.sandbox.sandboxes.docker import DockerSandboxClient, DockerSandboxClientOptions
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return docker_from_env, DockerSandboxClient, DockerSandboxClientOptions
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def _build_manifest() -> Manifest:
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return Manifest(
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entries={
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"taxpayer_data": Dir(
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children={"sample_w2.pdf": LocalFile(src=W2_PATH)},
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description="Taxpayer income documents such as W-2s and 1099s.",
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),
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"reference_forms": Dir(
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children={"f1040.pdf": LocalFile(src=FORM_1040_PATH)},
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description="Blank tax forms the agent can use as templates.",
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),
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"output": Dir(description="Write finalized tax documents here."),
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}
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)
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def _build_agent(*, model: str, skills_repo: str, skills_ref: str) -> SandboxAgent:
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return SandboxAgent(
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name="Tax Prep Assistant",
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model=model,
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instructions=(
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INSTRUCTIONS + "\n\n"
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"Inspect the workspace before answering. Keep final explanations concise, and make "
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"sure the final filled files are actually written into `output/`."
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),
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default_manifest=_build_manifest(),
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capabilities=Capabilities.default()
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+ [
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Skills(
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from_=GitRepo(repo=skills_repo, ref=skills_ref),
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),
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],
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)
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async def _copy_output_dir(
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*,
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session,
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destination_root: Path,
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) -> list[Path]:
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destination_root.mkdir(parents=True, exist_ok=True)
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remote_output_root = session.normalize_path("output")
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pending_dirs = [remote_output_root]
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copied_files: list[Path] = []
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while pending_dirs:
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current_dir = pending_dirs.pop()
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for entry in await session.ls(current_dir):
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entry_path = Path(entry.path)
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if entry.is_dir():
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pending_dirs.append(entry_path)
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continue
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relative_path = entry_path.relative_to(remote_output_root)
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local_path = destination_root / relative_path
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local_path.parent.mkdir(parents=True, exist_ok=True)
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handle = await session.read(entry_path)
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try:
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payload = handle.read()
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finally:
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handle.close()
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if isinstance(payload, str):
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local_path.write_text(payload, encoding="utf-8")
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else:
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local_path.write_bytes(bytes(payload))
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copied_files.append(local_path)
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return copied_files
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async def _run_turn(
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*,
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agent: SandboxAgent,
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input_items: list[TResponseInputItem],
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run_config: RunConfig,
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) -> list[TResponseInputItem]:
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stream_result = Runner.run_streamed(agent, input_items, run_config=run_config)
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saw_text_delta = False
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async for event in stream_result.stream_events():
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if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent):
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if not saw_text_delta:
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print("assistant> ", end="", flush=True)
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saw_text_delta = True
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print(event.data.delta, end="", flush=True)
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continue
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if event.type == "run_item_stream_event" and event.name == "tool_called":
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raw_item = getattr(event.item, "raw_item", None)
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tool_name = ""
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if isinstance(raw_item, dict):
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tool_name = cast(str, raw_item.get("name") or raw_item.get("type") or "")
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else:
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tool_name = cast(
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str,
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getattr(raw_item, "name", None) or getattr(raw_item, "type", None) or "",
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)
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if tool_name:
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if saw_text_delta:
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print()
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saw_text_delta = False
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print(f"[tool call] {tool_name}")
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if saw_text_delta:
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print()
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return stream_result.to_input_list()
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async def main(
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*,
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model: str,
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image: str,
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question: str,
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output_dir: Path,
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skills_repo: str,
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skills_ref: str,
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) -> None:
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docker_from_env, DockerSandboxClient, DockerSandboxClientOptions = _require_docker_dependency()
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agent = _build_agent(model=model, skills_repo=skills_repo, skills_ref=skills_ref)
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client = DockerSandboxClient(docker_from_env())
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sandbox = await client.create(
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manifest=agent.default_manifest,
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options=DockerSandboxClientOptions(image=image),
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)
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run_config = RunConfig(
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sandbox=SandboxRunConfig(session=sandbox),
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workflow_name="Sandbox tax prep demo",
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)
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conversation: list[TResponseInputItem] = [{"role": "user", "content": question}]
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try:
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async with sandbox:
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conversation = await _run_turn(
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agent=agent,
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input_items=conversation,
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run_config=run_config,
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)
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while True:
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try:
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additional_input = input("> ")
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except (EOFError, KeyboardInterrupt):
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break
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conversation.append({"role": "user", "content": additional_input})
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conversation = await _run_turn(
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agent=agent,
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input_items=conversation,
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run_config=run_config,
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)
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copied_files = await _copy_output_dir(session=sandbox, destination_root=output_dir)
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finally:
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await client.delete(sandbox)
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print(f"\nCopied {len(copied_files)} file(s) to {output_dir}")
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for copied_file in copied_files:
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print(copied_file)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", default="gpt-5.6-sol", help="Model name to use.")
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parser.add_argument("--image", default=DEFAULT_IMAGE, help="Docker image for the sandbox.")
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parser.add_argument("--question", default=DEFAULT_QUESTION, help="Prompt to send to the agent.")
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parser.add_argument(
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"--output-dir",
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default="tax-prep-results",
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help="Local directory where files from sandbox output/ will be copied.",
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)
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parser.add_argument(
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"--skills-repo",
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default=DEFAULT_SKILLS_REPO,
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help="GitHub repo in owner/name form for the skills bundle.",
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)
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parser.add_argument(
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"--skills-ref",
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default=DEFAULT_SKILLS_REF,
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help="Git ref for the skills bundle.",
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)
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args = parser.parse_args()
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asyncio.run(
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main(
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model=args.model,
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image=args.image,
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question=args.question,
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output_dir=Path(args.output_dir).resolve(),
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skills_repo=args.skills_repo,
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skills_ref=args.skills_ref,
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)
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)
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