chore: import upstream snapshot with attribution

2026-07-13 12:39:17 +08:00
commit 4ed4e9ff99
1368 changed files with 334957 additions and 0 deletions
@@ -0,0 +1,78 @@
+import argparse
+import asyncio
+from pathlib import Path
+
+from agents import Agent, Runner, ShellTool, ShellToolLocalSkill, trace
+from examples.tools.shell import ShellExecutor
+
+SKILL_NAME = "csv-workbench"
+SKILL_DIR = Path(__file__).resolve().parent / "skills" / SKILL_NAME
+
+
+def build_local_skill() -> ShellToolLocalSkill:
+    return {
+        "name": SKILL_NAME,
+        "description": "Analyze CSV files and return concise numeric summaries.",
+        "path": str(SKILL_DIR),
+    }
+
+
+async def main(model: str) -> None:
+    local_skill = build_local_skill()
+
+    with trace("local_shell_skill_example"):
+        agent1 = Agent(
+            name="Local Shell Agent (Local Skill)",
+            model=model,
+            instructions="Use the available local skill to answer user requests.",
+            tools=[
+                ShellTool(
+                    environment={
+                        "type": "local",
+                        "skills": [local_skill],
+                    },
+                    executor=ShellExecutor(),
+                )
+            ],
+        )
+
+        result1 = await Runner.run(
+            agent1,
+            (
+                "Use the csv-workbench skill. Create /tmp/test_orders.csv with columns "
+                "id,region,amount,status and at least 6 rows. Then report total amount by "
+                "region and count failed orders."
+            ),
+        )
+        print(f"Agent: {result1.final_output}")
+
+        agent2 = Agent(
+            name="Local Shell Agent (Reuse)",
+            model=model,
+            instructions="Reuse the existing local shell and answer concisely.",
+            tools=[
+                ShellTool(
+                    environment={
+                        "type": "local",
+                    },
+                    executor=ShellExecutor(),
+                )
+            ],
+        )
+
+        result2 = await Runner.run(
+            agent2,
+            "Run `ls -la /tmp/test_orders.csv`, then summarize in one sentence.",
+        )
+        print(f"Agent (reuse): {result2.final_output}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        default="gpt-5.6-sol",
+        help="Model name to use.",
+    )
+    args = parser.parse_args()
+    asyncio.run(main(args.model))