feat: 支持 skills 使用可自动恢复的 Cube sandbox runtime

examples/skills_with_cube/.env

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+# Set TRPC_AGENT_API_KEY、TRPC_AGENT_BASE_URL、TRPC_AGENT_MODEL_NAME
+TRPC_AGENT_API_KEY=your-api-key
+TRPC_AGENT_BASE_URL=your-base-url
+TRPC_AGENT_MODEL_NAME=your-model-name
+
+
+CUBE_TEMPLATE_ID=your-cube-template-id
+E2B_API_URL=your-e2b-api-url
+E2B_API_KEY=your-e2b-api-key

examples/skills_with_cube/README.md

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+# Skills Cube 与 stage_inputs 示例
+
+本示例演示在腾讯云 Agent 沙箱 Cube 工作区中执行 `skill_run`，并通过 `host://`、`workspace://`、`skill://` 等输入方案演示 `stage_inputs` 如何把本地输入上传/复制到远端沙箱。
+
+## 关键特性
+
+- `create_cube_sandbox_client(CubeClientConfig(auto_recover=True))`：创建远端 Cube 沙箱；底层 sandbox 过期/不存在时，`CubeSandboxClient` 会自动创建新 sandbox 并重试当前操作一次
+- `agent/tools.py` 中 `build_cube_skill_run_payload` 生成固定形态的 `skill_run` 负载供模型调用
+- `host://` 输入会从运行示例的本机路径上传到 Cube 沙箱，不依赖 Docker bind mount
+- `run_agent.py` 会准备示例 `/tmp/skillrun-inputs/sales.csv`，demo 结束后销毁 Cube 沙箱
+
+## Agent 层级结构说明
+
+- 根节点：`LlmAgent`，挂载 `SkillToolSet`（Cube 运行时 + 技能仓库）
+- 无子 Agent
+
+## 关键代码解释
+
+- `agent/tools.py`：通过 `CubeClientConfig(auto_recover=True)` 创建 `CubeSandboxClient`，再通过 `create_cube_workspace_runtime` 创建 workspace runtime
+- `agent/agent.py`：异步创建 agent，并把 workspace runtime 返回给 runner 做最终销毁
+- `run_agent.py`：组装含 `inputs` 数组的 JSON 提示词，驱动单次 `skill_run` 演示，并在 finally 中销毁沙箱
+
+## 环境与运行
+
+- Python 3.12；仓库根目录安装 Cube extra：`pip install -e '.[cube]'`
+- 配置 `TRPC_AGENT_API_KEY`、`TRPC_AGENT_BASE_URL`、`TRPC_AGENT_MODEL_NAME`
+- 配置 Cube 环境变量：`CUBE_TEMPLATE_ID`、`E2B_API_URL`、`E2B_API_KEY`
+- 可选：`SKILLS_ROOT`、`CUBE_EXECUTE_TIMEOUT`（默认 `30`）、`CUBE_IDLE_TIMEOUT`（默认 `600`）
+
+```bash
+cd examples/skills_with_cube
+python3 run_agent.py
+```
+
+### 验证 sandbox 重建后的 Skill runtime 恢复
+
+为了验证业务主动重建 sandbox 后，Skill 工具链仍能继续使用当前 workspace runtime，可以开启下面的环境变量：
+
+```bash
+SKILLS_WITH_CUBE_RECREATE_BETWEEN_RUNS=1 python3 run_agent.py
+```
+
+该模式会连续发起两次相同的 `skill_run` 请求：第一次正常运行；第二次请求前通过 workspace runtime 主动重建 Cube sandbox。只要第二次请求也能正常完成，就说明 Skill 工具链可以继续使用新的 runtime，而不是继续访问过期 sandbox。
+
+### 验证 sandbox 失效后的自动恢复
+
+为了验证更接近真实场景的自动恢复路径，可以开启下面的临时测试开关：
+
+```bash
+SKILLS_WITH_CUBE_KILL_BETWEEN_RUNS=1 python3 run_agent.py
+```
+
+该模式同样会连续发起两次相同的 `skill_run` 请求。第一次请求成功后，示例会直接 kill 远端 Cube sandbox，但保留本地 `CubeSandboxClient` 中的旧句柄。第二次请求继续使用旧句柄访问远端 sandbox，此时 Cube 会返回类似 `Code.unknown: The requested resource does not exist` 的错误；如果 `auto_recover=True` 生效，日志中会出现：
+
+```txt
+Cube sandbox expired; recreating sandbox client: Code.unknown: The requested resource does not exist
+Cube sandbox client using sandbox: <new-sandbox-id>
+```
+
+随后第二次 `skill_run` 仍应返回 `exit_code=0`，说明旧 sandbox 被平台/外部销毁后，`CubeSandboxClient` 已自动恢复并继续执行当前操作。
+
+注意：`SKILLS_WITH_CUBE_KILL_BETWEEN_RUNS` 是为了验证恢复机制而加入的临时代码，会使用私有句柄直接 kill 远端 sandbox。正式提交示例或生产代码时可以删除该测试开关及对应 helper。
+
+## 期望运行结果
+
+```txt
+[START] skills_with_cube
+...
+created Cube sandbox ...
+...
+🔧 [Invoke Tool:: skill_run({... 'inputs': [
+  'host:///tmp/skillrun-inputs/sales.csv',
+  'workspace://skills/python-math/SKILL.md',
+  'skill://python-math/scripts/fib.py',
+], ...})
+📊 [Tool Result: {
+  'stdout': '', 'stderr': '', 'exit_code': 0,
+  'output_files': [
+    {'name': 'out/fib.txt', 'content': '0\n1\n1\n2\n3\n5\n8\n13\n21\n34\n', ...},
+    {'name': 'out/staged_inputs_tree.txt', 'content':
+      'work/inputs:\nsales.csv\n---\nwork/staged_inputs:\nfib.py\npython-math_skill.md\n', ...},
+  ],
+  ...
+}]
+...
+```
+
+## 结果分析（是否符合要求）
+
+符合本示例测试要求：Cube 沙箱成功创建并完成 `skill_run` 调用链；`host://` / `workspace://` / `skill://` 三种 input scheme 都成功落入远端工作区，输出文件 `out/fib.txt` 和 `out/staged_inputs_tree.txt` 正常产出，进程以 `exit_code=0` 结束。
+
+如果使用 `SKILLS_WITH_CUBE_KILL_BETWEEN_RUNS=1`，还需要确认第二次请求中出现自动恢复日志，并且第二次 `skill_run` 仍然成功。这表示真实的“旧 sandbox 不存在 -> client 自动重建 -> 当前操作重试”链路通过。
+
+## 适用场景建议
+
+- 需要在远端 Cube 沙箱内执行技能、并验证本地输入上传到沙箱时参考本示例
+- 调试 `workspace://` 时应确保源文件已存在于当前 workspace，再复制或链接到目标路径
+- 长生命周期 agent 建议开启 `CubeClientConfig(auto_recover=True)`，避免 Cube sandbox 因超时或平台清理后导致后续技能调用持续失败
+- 自动恢复会创建全新的 sandbox，远端 workspace 内容不会自动从旧 sandbox 迁移；Skill staging 和 workspace 创建逻辑需要能在新 sandbox 上重新执行

examples/skills_with_cube/agent/__init__.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.

examples/skills_with_cube/agent/agent.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""Agent for Cube-backed skill runs."""
+
+from trpc_agent_sdk.agents import LlmAgent
+from trpc_agent_sdk.models import LLMModel
+from trpc_agent_sdk.models import OpenAIModel
+
+from .config import get_model_config
+from .prompts import INSTRUCTION
+from .tools import create_skill_tool_set
+
+
+def _create_model() -> LLMModel:
+    """ Create a model"""
+    api_key, url, model_name = get_model_config()
+    model = OpenAIModel(model_name=model_name, api_key=api_key, base_url=url)
+    return model
+
+
+async def create_agent():
+    """Create a Cube-backed skill run agent and its workspace runtime."""
+
+    # Create tools
+    skill_tool_set, skill_repository, workspace_runtime = await create_skill_tool_set()
+    agent = LlmAgent(
+        name="skill_run_agent_with_cube",
+        description="A professional skill run assistant that can use Agent Skills.",
+        model=_create_model(),
+        # Use state variables for template replacement - Demonstration of the {var} syntax
+        instruction=INSTRUCTION,
+        tools=[skill_tool_set],
+        skill_repository=skill_repository,
+    )
+    return agent, workspace_runtime
+
+
+root_agent = None

examples/skills_with_cube/agent/config.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+""" Agent config module"""
+
+import os
+
+
+def get_model_config() -> tuple[str, str, str]:
+    """Get model config from environment variables"""
+    api_key = os.getenv('TRPC_AGENT_API_KEY', '')
+    url = os.getenv('TRPC_AGENT_BASE_URL', '')
+    model_name = os.getenv('TRPC_AGENT_MODEL_NAME', '')
+    if not api_key or not url or not model_name:
+        raise ValueError('''TRPC_AGENT_API_KEY, TRPC_AGENT_BASE_URL,
+                         and TRPC_AGENT_MODEL_NAME must be set in environment variables''')
+    return api_key, url, model_name

examples/skills_with_cube/agent/prompts.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+""" prompts for agent"""
+
+INSTRUCTION = """
+Be a concise, helpful assistant that can use Agent Skills.
+
+When a task may need tools, first ask to list skills or suggest one.
+Load a skill only when needed, then run commands from its docs exactly.
+Prefer safe defaults; ask clarifying questions if anything is ambiguous.
+When running, include output_files patterns if files are expected.
+Summarize results, note saved files, and propose next steps briefly.
+
+Inside a Cube skill workspace, inputs staged from host:// are uploaded
+copies in the remote sandbox. Treat inputs/ and work/inputs/ as input
+data and write new results under out/ or $OUTPUT_DIR.
+
+When chaining multiple skills, read previous results directly from
+out/ (or $OUTPUT_DIR) and write new files back to out/. Prefer using
+skill_run inputs/outputs fields to map files instead of shell commands
+like cp or mv where possible.
+
+When using a skill, follow this workflow:
+1. First call skill_load to load the skill documentation
+2. Always call skill_list_docs immediately after skill_load to verify what documents have been loaded,
+   including documents from subdirectories (e.g., references/ folder)
+3. If needed, use skill_select_docs to add additional documents
+4. Call skill_list_docs again after skill_select_docs to confirm the final set of loaded documents
+5. Finally use skill_run to execute commands
+
+This ensures you can verify that all relevant documentation files, including those in subdirectories,
+are properly loaded before executing commands.
+"""

examples/skills_with_cube/agent/tools.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""Tools for the Cube-backed skill agent."""
+import os
+from pathlib import Path
+from typing import Any
+
+from trpc_agent_sdk.code_executors import WorkspaceInputSpec
+from trpc_agent_sdk.code_executors.cube import CubeClientConfig
+from trpc_agent_sdk.code_executors.cube import CubeWorkspaceRuntime
+from trpc_agent_sdk.code_executors.cube import CubeWorkspaceRuntimeConfig
+from trpc_agent_sdk.code_executors.cube import create_cube_sandbox_client
+from trpc_agent_sdk.code_executors.cube import create_cube_workspace_runtime
+from trpc_agent_sdk.skills import ENV_SKILLS_ROOT
+from trpc_agent_sdk.skills import SkillToolSet
+from trpc_agent_sdk.skills import create_default_skill_repository
+
+
+def _get_skill_paths() -> str:
+    """Get the skill paths."""
+    skills_root = os.getenv(ENV_SKILLS_ROOT)
+    if skills_root:
+        return skills_root
+    current_path = Path(__file__).parent
+    path = str(current_path.parent / "skills")
+    # convert to file URL
+    # path = "file://" + path
+    # "http://{host}:{port}/{path}/{filename}.{extension}"
+    # path = "http://localhost:8000/skills/skills.tar.gz"
+    return path
+
+
+def _cube_client_config() -> CubeClientConfig:
+    """Build Cube executor config from environment variables."""
+    return CubeClientConfig(
+        execute_timeout=float(os.getenv("CUBE_EXECUTE_TIMEOUT", "30")),
+        idle_timeout=int(os.getenv("CUBE_IDLE_TIMEOUT", "600")),
+        auto_recover=True,
+    )
+
+
+async def create_skill_tool_set() -> tuple[SkillToolSet, Any, CubeWorkspaceRuntime]:
+    """Create a Cube-backed skill tool set and its Cube runtime."""
+    tool_kwargs = {
+        "save_as_artifacts": True,
+        "omit_inline_content": False,
+    }
+
+    cfg = _cube_client_config()
+    sandbox_client = await create_cube_sandbox_client(cfg)
+    workspace_runtime = create_cube_workspace_runtime(
+        sandbox_client=sandbox_client,
+        execute_timeout=cfg.execute_timeout,
+        workspace_cfg=CubeWorkspaceRuntimeConfig(),
+    )
+    print(f"[skills_with_cube] using Cube sandbox: {workspace_runtime.sandbox_id}", flush=True)
+    skill_paths = _get_skill_paths()
+    repository = create_default_skill_repository(skill_paths, workspace_runtime=workspace_runtime)
+    toolset = SkillToolSet(repository=repository, run_tool_kwargs=tool_kwargs)
+    return toolset, repository, workspace_runtime
+
+
+def build_cube_stage_inputs_specs(inputs_host: str = "/tmp/skillrun-inputs") -> list[WorkspaceInputSpec]:
+    """Build example input specs for Cube runtime.
+
+    The returned specs demonstrate the supported input schemes used by
+    ``CubeWorkspaceFS.stage_inputs``:
+
+    - ``host://``     : upload from a host path into the remote Cube sandbox
+    - ``workspace://``: reuse a file already present in current workspace
+    - ``skill://``    : reference a file under workspace ``skills/``
+    """
+    return [
+        WorkspaceInputSpec(
+            src=f"host://{inputs_host}/sales.csv",
+            dst="work/inputs/sales.csv",
+            mode="link",
+        ),
+        WorkspaceInputSpec(
+            # This file exists after skill staging, so the workspace:// demo is stable.
+            src="workspace://skills/python-math/SKILL.md",
+            dst="work/staged_inputs/python-math_skill.md",
+            mode="copy",
+        ),
+        WorkspaceInputSpec(
+            src="skill://python-math/scripts/fib.py",
+            dst="work/staged_inputs/fib.py",
+            mode="copy",
+        ),
+    ]
+
+
+def build_cube_skill_run_payload(skill_name: str = "python-math",
+                                 inputs_host: str = "/tmp/skillrun-inputs") -> dict[str, Any]:
+    """Build a full ``skill_run`` payload for Cube mode demonstration.
+
+    This payload can be used directly when invoking the ``skill_run`` tool:
+    it stages input schemes into the remote Cube workspace and writes outputs
+    under ``out/``.
+    """
+    return {
+        "skill":
+        skill_name,
+        "cwd":
+        f"$SKILLS_DIR/{skill_name}",
+        "command": ("python scripts/fib.py 10 > out/fib.txt && "
+                    "(ls -R work/inputs; echo '---'; ls -R work/staged_inputs) > out/staged_inputs_tree.txt"),
+        "inputs": [spec.model_dump() for spec in build_cube_stage_inputs_specs(inputs_host=inputs_host)],
+        "output_files": [
+            "out/fib.txt",
+            "out/staged_inputs_tree.txt",
+        ],
+    }