diff --git a/.features/skill/MEMORY.md b/.features/skill/MEMORY.md
index 915fac2..5a2c15b 100644
--- a/.features/skill/MEMORY.md
+++ b/.features/skill/MEMORY.md
@@ -1,6 +1,7 @@
# Skill 功能
> 负责范围：技能包管理服务 - 核心实现
+> 最后更新：2026-06-02
> 最后更新：2026-05-26
> 最后更新：2026-05-23
> 最后更新：2026-04-20
@@ -28,9 +29,12 @@ MCP UI 类 skill 已按 MCP Apps 模式改造：工具返回数据，静态 HTML
- `skills_developing/` - 开发中 skills
- `agent/subagent_loader.py` - 扫描 skill `agents/*.md` 加载子 agent（2026-05 引入）
- `agent/mcp_trace_meta.py` - 对 `ClientSession.call_tool` 做 monkey-patch，向 `rag_retrieve` / `table_rag_retrieve` 的 MCP `_meta` 注入 `trace_id`（2026-05 引入）
+- `agent/tool_metrics_middleware.py` - `ToolMetricsMiddleware` 给每次 tool 调用 emit `catalog_agent.tool_call` 结构化指标（2026-05 引入）
## 最近重要事项
+- [2026-06-02](changelog/2026-Q2.md): 修复 deepagents 落盘 backend grep 扫描全盘问题——`create_custom_cli_agent` 中 `large_results_backend` / `conversation_history_backend` 的 `FilesystemBackend` 由 `virtual_mode=False` 改为 `True`，避免 `CompositeBackend` 剥前缀后把 `"/"` 转发给 grep 时扫到真实根目录，单次 grep 从 45–152s 回到毫秒级（`6bccd89`）
+- [2026-05-29](changelog/2026-Q2.md): 新增 `ToolMetricsMiddleware`——通过 `wrap_tool_call` / `awrap_tool_call` 对每次 tool 调用计时并 emit `catalog_agent.tool_call` 结构化指标（成功/失败/取消三态、含 `tool_name`/`trace_id`/`bot_id`/`duration_ms`/`error_type`）；插在 `init_agent` 中间件链的 `EmptyResponseRetryMiddleware` 之后、`ToolUseCleanupMiddleware` 之前（`9f0ae25`）
- [2026-05-26](changelog/2026-Q2.md): skill 引入 `category` 字段——`routes/skill_manager.py` 在 `SkillItem` / `SkillValidationResult` 增加 `category`，从 `plugin.json` 与 `SKILL.md` frontmatter 解析，official skill 默认 `"other"`、user skill 默认 `"custom"`；并通过 batch 给 common/developing/onprem/support 路径下大量 skill 元数据补 `category`，`data-dashboard` / `mcp-ui` 归类 `Interactive UI`（`203dcf4`, `3ada55a`, `9658588`）
- [2026-05-26](changelog/2026-Q2.md): developing 分支大合并新增多个 skill：`ai-ppt-generator`（百度 AI PPT）、`nfc-medicine-lookup`（NFC 药品检索）、`ppt-outline`（PPT 大纲 / HTML 演示文稿）、`z-card-image`（配图 / 卡片图），同时 `skills/linggan/*` 系列 skill 经合并回归（`3ada55a`）
- [2026-05-23](changelog/2026-Q2.md): 新增 MCP App 型 `skills/developing/ecommerce-storefront/`——含 `product-list` / `order-confirm` 两个 HTML App + 自带 `ecommerce_server.py` MCP server；同时落地 `docs/mcp-app-training.md`（约 1063 行）作为 MCP App 培训材料（`9d001c8`）
@@ -85,6 +89,8 @@ MCP UI 类 skill 已按 MCP Apps 模式改造：工具返回数据，静态 HTML
- ⚠️ **skill `category` 默认值**：API 返回的 `SkillItem.category`——official skill fallback 为 `"other"`、user skill fallback 为 `"custom"`；前端做分类视图时需要同时识别这两个 sentinel，不要假设官方/用户 skill 用同一套缺省值。
- ⚠️ **`category` 字段双入口**：同一 skill 可以同时在 `.claude-plugin/plugin.json` 和 `SKILL.md` frontmatter 写 `category`；`get_skill_metadata` 优先走 `parse_plugin_json`，若 skill 包没有 plugin.json 才回落到 `parse_skill_frontmatter`——两者写不一致时以 plugin.json 为准。
- ⚠️ **Daytona shell_env 是文件注入而非 process env**：`init_agent` 通过 `cat > $REMOTE_BASH_ENV_PATH` 写入 `export VAR=...` 行，沙箱内必须由 shell（bash）的 `BASH_ENV` 加载才能生效；非 daytona 模式或不走 bash 启动的脚本拿不到这些变量。扩展注入项需直接改 `init_agent` 里的 `_shell_env` 字典。
+- ⚠️ **`CompositeBackend` 路由下的落盘 backend 必须 `virtual_mode=True`**：`create_custom_cli_agent` 中 `large_results_backend` / `conversation_history_backend` 都用独立 `tempfile.mkdtemp()` 做根目录，但 `CompositeBackend` 在路由时会剥掉前缀、可能把 `"/"` 转发给 grep；`virtual_mode=False` 会把 `"/"` 解析为真实根目录并扫到 `/usr`、`/var`、其他会话的 tmp 目录（单次 45–152s）。`virtual_mode=True` 才会把所有路径锚定到 `root_dir` 并过滤越界结果。后续新增"只服务本次会话"的落盘 backend 一律走 `virtual_mode=True`，真实 workspace backend 仍保持 `False`。
+- ⚠️ **`ToolMetricsMiddleware` 必须在重试中间件之后、其他工具中间件之前**：`init_agent` 中顺序约定为 `EmptyResponseRetryMiddleware → ToolMetricsMiddleware → ToolUseCleanupMiddleware → ...`，这样统计到的 `duration_ms` 才包含全部后续 tool 处理开销并自然覆盖重试边界。指标 emit 自身的异常被吞掉只打 logger.exception，所以指标缺失不会触发 agent 报错，必须在指标后端做独立告警。
## Skill 目录结构
diff --git a/.features/skill/changelog/2026-Q2.md b/.features/skill/changelog/2026-Q2.md
index ca34c8e..86b46f2 100644
--- a/.features/skill/changelog/2026-Q2.md
+++ b/.features/skill/changelog/2026-Q2.md
@@ -4,6 +4,57 @@
---
+## 2026-06-02: 修复 deepagents 落盘 backend grep 扫描全盘问题（virtual_mode=True）
+
+**类型**：Bug 修复
+
+**背景**：`create_custom_cli_agent` 给 `large_results` / `conversation_history` 两个路由创建了独立 `FilesystemBackend(tempfile.mkdtemp(...), virtual_mode=False)`，配合 `CompositeBackend` 做前缀路由。线上出现 grep 调用耗时 45–152s 的异常。
+
+**改动**：将 `large_results_backend` 与 `conversation_history_backend` 的 `virtual_mode` 由 `False` 改为 `True`，并在调用处补 NOTE 注释说明 virtual_mode 的语义。
+
+**根因**：`CompositeBackend` 会先剥掉路由前缀，再把剩余路径（极端情况下就是 `"/"`）转发给被路由 backend 的 grep。当 backend 的 `virtual_mode=False` 时，`"/"` 解析为真实根目录而不是 `root_dir`，于是 grep 在沙箱 / 容器内对 `/usr`、`/var`、其他会话的 tmp 目录全盘扫描，单次耗时达数十秒到分钟级。`virtual_mode=True` 会把所有路径锚定到 `root_dir`，并过滤掉根目录之外的结果，把扫描限制在 backend 自己的 tmp 子目录内。
+
+**影响**：
+- 这两个 backend 的 grep 调用回落到毫秒级，整个 deep agent 的 tool 调用 P99 大幅下降。
+- 任何新增的"落盘但只服务于本次会话"的 `FilesystemBackend` 走 `CompositeBackend` 路由时，**必须**使用 `virtual_mode=True`，否则容易复现同样的全盘扫描问题。
+- 真实 workspace backend（用户文件根目录）仍然是 `virtual_mode=False`，因为它需要看到沙箱真实路径上的产物。
+
+**相关文件**：
+- `agent/deep_assistant.py`
+
+**Commit/PR**：`6bccd89`
+
+---
+
+## 2026-05-29: 新增 ToolMetricsMiddleware（tool 调用埋点）
+
+**类型**：新功能
+
+**背景**：deep agent 内部的工具调用（含 MCP tool / skill script / 文件系统操作等）此前缺少统一的耗时与成功率埋点；问题排查只能依赖日志，无法对接 `emit_question_metric` 的结构化指标体系。
+
+**改动**：
+- 新增 `agent/tool_metrics_middleware.py`，定义 `ToolMetricsMiddleware(AgentMiddleware)`：
+ - 同时实现 `wrap_tool_call`（同步）与 `awrap_tool_call`（异步）。
+ - 对每次 tool 调用计时（`time.monotonic()`），通过 `emit_question_metric(stage="catalog_agent.tool_call", ...)` 上报：`status`（`success` / `error` / `cancel`）、`duration_ms`、`error_type`、`tool_name`、`tool_call_id`、`trace_id`、`bot_id`、`session_id`、`model`、`stream`、`tool_response`、`enable_thinking`。
+ - 异步分支特别捕获 `asyncio.CancelledError` 上报 `status="cancel"` 后再 re-raise。
+ - 指标 emit 自身的异常被 try/except 兜住，绝不影响 tool 调用本体。
+- `agent/deep_assistant.py::init_agent` 中间件链中，将 `ToolMetricsMiddleware(config)` 插在 `EmptyResponseRetryMiddleware` 之后、`ToolUseCleanupMiddleware` 之前。
+
+**根因**：N/A（新功能）
+
+**影响**：
+- 所有走 deep agent / sub agent 的 tool 调用现在都会自动出 `catalog_agent.tool_call` 结构化指标，可在指标后端按 `tool_name` / `bot_id` / `status` 聚合做 P50 / P99 / 错误率分析。
+- 中间件顺序硬约束扩展：`EmptyResponseRetryMiddleware → ToolMetricsMiddleware → ToolUseCleanupMiddleware → CustomFilesystemMiddleware → SubAgentMiddleware → AnthropicPromptCachingMiddleware`，调整 `init_agent` 中间件顺序时需保持 `ToolMetricsMiddleware` 在最外层（仅次于重试），否则统计到的耗时不包含其他中间件开销。
+- emit 失败只打 logger.exception，不会回传给 tool handler；指标缺失需在指标后端层面单独告警，不要依赖 agent 端口的报错。
+
+**相关文件**：
+- `agent/tool_metrics_middleware.py`（新增）
+- `agent/deep_assistant.py`
+
+**Commit/PR**：`9f0ae25`
+
+---
+
## 2026-05-26: skill `category` 字段全面接入
**类型**：新功能
diff --git a/poetry.lock b/poetry.lock
index 7c6806d..aa53671 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand.
[[package]]
name = "agent-client-protocol"
@@ -1091,18 +1091,6 @@ all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>
standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
standard-no-fastapi-cloud-cli = ["email-validator (>=2.0.0)", "fastapi-cli[standard-no-fastapi-cloud-cli] (>=0.0.8)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
-[[package]]
-name = "filelock"
-version = "3.20.0"
-description = "A platform independent file lock."
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
- {file = "filelock-3.20.0-py3-none-any.whl", hash = "sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2"},
- {file = "filelock-3.20.0.tar.gz", hash = "sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4"},
-]
-
[[package]]
name = "filetype"
version = "1.2.0"
@@ -1267,46 +1255,6 @@ files = [
{file = "frozenlist-1.8.0.tar.gz", hash = "sha256:3ede829ed8d842f6cd48fc7081d7a41001a56f1f38603f9d49bf3020d59a31ad"},
]
-[[package]]
-name = "fsspec"
-version = "2025.9.0"
-description = "File-system specification"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
- {file = "fsspec-2025.9.0-py3-none-any.whl", hash = "sha256:530dc2a2af60a414a832059574df4a6e10cce927f6f4a78209390fe38955cfb7"},
- {file = "fsspec-2025.9.0.tar.gz", hash = "sha256:19fd429483d25d28b65ec68f9f4adc16c17ea2c7c7bf54ec61360d478fb19c19"},
-]
-
-[package.extras]
-abfs = ["adlfs"]
-adl = ["adlfs"]
-arrow = ["pyarrow (>=1)"]
-dask = ["dask", "distributed"]
-dev = ["pre-commit", "ruff (>=0.5)"]
-doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
-dropbox = ["dropbox", "dropboxdrivefs", "requests"]
-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
-fuse = ["fusepy"]
-gcs = ["gcsfs"]
-git = ["pygit2"]
-github = ["requests"]
-gs = ["gcsfs"]
-gui = ["panel"]
-hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
-libarchive = ["libarchive-c"]
-oci = ["ocifs"]
-s3 = ["s3fs"]
-sftp = ["paramiko"]
-smb = ["smbprotocol"]
-ssh = ["paramiko"]
-test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
-test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
-test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard ; python_version < \"3.14\""]
-tqdm = ["tqdm"]
-
[[package]]
name = "google-auth"
version = "2.48.0"
@@ -1642,28 +1590,6 @@ files = [
hpack = ">=4.1,<5"
hyperframe = ">=6.1,<7"
-[[package]]
-name = "hf-xet"
-version = "1.1.10"
-description = "Fast transfer of large files with the Hugging Face Hub."
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
-files = [
- {file = "hf_xet-1.1.10-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:686083aca1a6669bc85c21c0563551cbcdaa5cf7876a91f3d074a030b577231d"},
- {file = "hf_xet-1.1.10-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:71081925383b66b24eedff3013f8e6bbd41215c3338be4b94ba75fd75b21513b"},
- {file = "hf_xet-1.1.10-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6b6bceb6361c80c1cc42b5a7b4e3efd90e64630bcf11224dcac50ef30a47e435"},
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- {file = "hf_xet-1.1.10-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0a0005fd08f002180f7a12d4e13b22be277725bc23ed0529f8add5c7a6309c06"},
- {file = "hf_xet-1.1.10-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f900481cf6e362a6c549c61ff77468bd59d6dd082f3170a36acfef2eb6a6793f"},
- {file = "hf_xet-1.1.10-cp37-abi3-win_amd64.whl", hash = "sha256:5f54b19cc347c13235ae7ee98b330c26dd65ef1df47e5316ffb1e87713ca7045"},
- {file = "hf_xet-1.1.10.tar.gz", hash = "sha256:408aef343800a2102374a883f283ff29068055c111f003ff840733d3b715bb97"},
-]
-
-[package.extras]
-tests = ["pytest"]
-
[[package]]
name = "hpack"
version = "4.1.0"
@@ -1790,45 +1716,6 @@ files = [
{file = "httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d"},
]
-[[package]]
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-version = "0.35.3"
-description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
-optional = false
-python-versions = ">=3.8.0"
-groups = ["main"]
-files = [
- {file = "huggingface_hub-0.35.3-py3-none-any.whl", hash = "sha256:0e3a01829c19d86d03793e4577816fe3bdfc1602ac62c7fb220d593d351224ba"},
- {file = "huggingface_hub-0.35.3.tar.gz", hash = "sha256:350932eaa5cc6a4747efae85126ee220e4ef1b54e29d31c3b45c5612ddf0b32a"},
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-
-[package.dependencies]
-filelock = "*"
-fsspec = ">=2023.5.0"
-hf-xet = {version = ">=1.1.3,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
-packaging = ">=20.9"
-pyyaml = ">=5.1"
-requests = "*"
-tqdm = ">=4.42.1"
-typing-extensions = ">=3.7.4.3"
-
-[package.extras]
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-fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
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-tensorflow = ["graphviz", "pydot", "tensorflow"]
-tensorflow-testing = ["keras (<3.0)", "tensorflow"]
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-dev = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "jinja2 (>=3.1.0)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<=0.9)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "openai (>=1.98.0)", "optax (>=0.0.8,<=0.1.4)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torch (>=2.2)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)", "uvicorn"]
-dev-tensorflow = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "onnxconverter-common", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "openai (>=1.98.0)", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "tf2onnx", "timeout-decorator", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "urllib3 (<2.0.0)", "uvicorn"]
-dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<=0.9)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "openai (>=1.98.0)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torch (>=2.2)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)", "uvicorn"]
-flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
-flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-ftfy = ["ftfy"]
-hf-xet = ["hf_xet"]
-hub-kernels = ["kernels (>=0.6.1,<=0.9)"]
-integrations = ["kernels (>=0.6.1,<=0.9)", "optuna", "ray[tune] (>=2.7.0)"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)"]
-mistral-common = ["mistral-common[opencv] (>=1.6.3)"]
-modelcreation = ["cookiecutter (==1.7.3)"]
-natten = ["natten (>=0.14.6,<0.15.0)"]
-num2words = ["num2words"]
-onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
-onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
-open-telemetry = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"]
-optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (>=2.15.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.13.1)", "urllib3 (<2.0.0)"]
-ray = ["ray[tune] (>=2.7.0)"]
-retrieval = ["datasets (>=2.15.0)", "faiss-cpu"]
-ruff = ["ruff (==0.13.1)"]
-sagemaker = ["sagemaker (>=2.31.0)"]
-sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["accelerate (>=0.26.0)", "fastapi", "openai (>=1.98.0)", "pydantic (>=2)", "starlette", "torch (>=2.2)", "uvicorn"]
-sigopt = ["sigopt"]
-sklearn = ["scikit-learn"]
-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "openai (>=1.98.0)", "parameterized (>=0.9)", "psutil", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "timeout-decorator", "torch (>=2.2)", "uvicorn"]
-tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-tiktoken = ["blobfile", "tiktoken"]
-timm = ["timm (!=1.0.18,<=1.0.19)"]
-tokenizers = ["tokenizers (>=0.22.0,<=0.23.0)"]
-torch = ["accelerate (>=0.26.0)", "torch (>=2.2)"]
-torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.34.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "tqdm (>=4.27)"]
-video = ["av"]
-vision = ["Pillow (>=10.0.1,<=15.0)"]
-
-[[package]]
-name = "triton"
-version = "2.2.0"
-description = "A language and compiler for custom Deep Learning operations"
-optional = false
-python-versions = "*"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
-files = [
- {file = "triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5"},
- {file = "triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0"},
- {file = "triton-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5"},
- {file = "triton-2.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab"},
- {file = "triton-2.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace"},
- {file = "triton-2.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df"},
-]
-
-[package.dependencies]
-filelock = "*"
-
-[package.extras]
-build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
-tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
-
[[package]]
name = "truststore"
version = "0.10.4"
@@ -7129,9 +6374,9 @@ files = [
]
[package.extras]
-cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and python_version < \"3.14\"", "cffi (>=2.0.0b) ; platform_python_implementation != \"PyPy\" and python_version >= \"3.14\""]
+cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and python_version < \"3.14\"", "cffi (>=2.0.0b0) ; platform_python_implementation != \"PyPy\" and python_version >= \"3.14\""]
[metadata]
lock-version = "2.1"
python-versions = ">=3.12,<3.15"
-content-hash = "f5b01d5a1e60672741f2c5e8cc6e2ec55534963a9a3791fd1cdf67d3c2fbd70b"
+content-hash = "af874fe9bf96a0a2468027b645438e4f4d955e965d4945083024fb33d79b5d7e"
diff --git a/pyproject.toml b/pyproject.toml
index d5163fd..594a6af 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,9 +13,6 @@ dependencies = [
"requests==2.32.5",
"pydantic==2.10.5",
"python-dateutil==2.8.2",
- "torch==2.2.0",
- "transformers",
- "sentence-transformers",
"numpy<2",
"aiohttp",
"aiofiles",
diff --git a/requirements.txt b/requirements.txt
index 4237427..a07f0b5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -38,11 +38,9 @@ distro==1.9.0 ; python_version >= "3.12" and python_version < "3.15"
docstring-parser==0.17.0 ; python_version >= "3.12" and python_version < "3.15"
et-xmlfile==2.0.0 ; python_version >= "3.12" and python_version < "3.15"
fastapi==0.116.1 ; python_version >= "3.12" and python_version < "3.15"
-filelock==3.20.0 ; python_version >= "3.12" and python_version < "3.15"
filetype==1.2.0 ; python_version >= "3.12" and python_version < "3.15"
forbiddenfruit==0.1.4 ; python_version >= "3.12" and python_version < "3.15" and implementation_name == "cpython"
frozenlist==1.8.0 ; python_version >= "3.12" and python_version < "3.15"
-fsspec==2025.9.0 ; python_version >= "3.12" and python_version < "3.15"
google-auth==2.48.0 ; python_version >= "3.12" and python_version < "3.15"
google-genai==1.65.0 ; python_version >= "3.12" and python_version < "3.15"
googleapis-common-protos==1.74.0 ; python_version >= "3.12" and python_version < "3.15"
@@ -52,19 +50,16 @@ grpcio-tools==1.78.0 ; python_version >= "3.12" and python_version < "3.15"
grpcio==1.78.0 ; python_version >= "3.12" and python_version < "3.15"
h11==0.16.0 ; python_version >= "3.12" and python_version < "3.15"
h2==4.3.0 ; python_version >= "3.12" and python_version < "3.15"
-hf-xet==1.1.10 ; python_version >= "3.12" and python_version < "3.15" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64")
hpack==4.1.0 ; python_version >= "3.12" and python_version < "3.15"
httpcore==1.0.9 ; python_version >= "3.12" and python_version < "3.15"
httptools==0.7.1 ; python_version >= "3.12" and python_version < "3.15" and platform_system != "Windows"
httpx-sse==0.4.3 ; python_version >= "3.12" and python_version < "3.15"
httpx==0.28.1 ; python_version >= "3.12" and python_version < "3.15"
-huggingface-hub==0.35.3 ; python_version >= "3.12" and python_version < "3.15"
hyperframe==6.1.0 ; python_version >= "3.12" and python_version < "3.15"
idna==3.11 ; python_version >= "3.12" and python_version < "3.15"
importlib-metadata==8.7.1 ; python_version >= "3.12" and python_version < "3.15"
jinja2==3.1.6 ; python_version >= "3.12" and python_version < "3.15"
jiter==0.11.1 ; python_version >= "3.12" and python_version < "3.15"
-joblib==1.5.2 ; python_version >= "3.12" and python_version < "3.15"
json-repair==0.29.10 ; python_version >= "3.12" and python_version < "3.15"
json5==0.13.0 ; python_version >= "3.12" and python_version < "3.15"
jsonpatch==1.33 ; python_version >= "3.12" and python_version < "3.15"
@@ -98,22 +93,8 @@ mcp==1.12.4 ; python_version >= "3.12" and python_version < "3.15"
mdit-py-plugins==0.5.0 ; python_version >= "3.12" and python_version < "3.15"
mdurl==0.1.2 ; python_version >= "3.12" and python_version < "3.15"
mem0ai==0.1.115 ; python_version >= "3.12" and python_version < "3.15"
-mpmath==1.3.0 ; python_version >= "3.12" and python_version < "3.15"
multidict==6.7.0 ; python_version >= "3.12" and python_version < "3.15"
-networkx==3.5 ; python_version >= "3.12" and python_version < "3.15"
numpy==1.26.4 ; python_version >= "3.12" and python_version < "3.15"
-nvidia-cublas-cu12==12.1.3.1 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-cuda-cupti-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-cuda-nvrtc-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-cuda-runtime-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-cudnn-cu12==8.9.2.26 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-cufft-cu12==11.0.2.54 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-curand-cu12==10.3.2.106 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-cusolver-cu12==11.4.5.107 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-cusparse-cu12==12.1.0.106 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-nccl-cu12==2.19.3 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-nvjitlink-cu12==12.9.86 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
-nvidia-nvtx-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
obstore==0.8.2 ; python_version >= "3.12" and python_version < "3.15"
openai==2.31.0 ; python_version >= "3.12" and python_version < "3.15"
openpyxl==3.1.5 ; python_version >= "3.12" and python_version < "3.15"
@@ -168,10 +149,6 @@ requests==2.32.5 ; python_version >= "3.12" and python_version < "3.15"
rich==14.2.0 ; python_version >= "3.12" and python_version < "3.15"
rpds-py==0.27.1 ; python_version >= "3.12" and python_version < "3.15"
rsa==4.9.1 ; python_version >= "3.12" and python_version < "3.15"
-safetensors==0.6.2 ; python_version >= "3.12" and python_version < "3.15"
-scikit-learn==1.7.2 ; python_version >= "3.12" and python_version < "3.15"
-scipy==1.16.2 ; python_version >= "3.12" and python_version < "3.15"
-sentence-transformers==5.1.1 ; python_version >= "3.12" and python_version < "3.15"
setuptools==80.9.0 ; python_version >= "3.12" and python_version < "3.15"
six==1.17.0 ; python_version >= "3.12" and python_version < "3.15"
sniffio==1.3.1 ; python_version >= "3.12" and python_version < "3.15"
@@ -181,21 +158,15 @@ sqlite-vec==0.1.6 ; python_version >= "3.12" and python_version < "3.15"
sse-starlette==3.0.2 ; python_version >= "3.12" and python_version < "3.15"
starlette==0.47.3 ; python_version >= "3.12" and python_version < "3.15"
structlog==25.5.0 ; python_version >= "3.12" and python_version < "3.15"
-sympy==1.14.0 ; python_version >= "3.12" and python_version < "3.15"
tavily-python==0.7.22 ; python_version >= "3.12" and python_version < "3.15"
tenacity==9.1.2 ; python_version >= "3.12" and python_version < "3.15"
textual-autocomplete==4.0.6 ; python_version >= "3.12" and python_version < "3.15"
textual-speedups==0.2.1 ; python_version >= "3.12" and python_version < "3.15"
textual==8.0.0 ; python_version >= "3.12" and python_version < "3.15"
-threadpoolctl==3.6.0 ; python_version >= "3.12" and python_version < "3.15"
tiktoken==0.12.0 ; python_version >= "3.12" and python_version < "3.15"
-tokenizers==0.22.1 ; python_version >= "3.12" and python_version < "3.15"
toml==0.10.2 ; python_version >= "3.12" and python_version < "3.15"
tomli-w==1.2.0 ; python_version >= "3.12" and python_version < "3.15"
-torch==2.2.0 ; python_version >= "3.12" and python_version < "3.15"
tqdm==4.67.1 ; python_version >= "3.12" and python_version < "3.15"
-transformers==4.57.1 ; python_version >= "3.12" and python_version < "3.15"
-triton==2.2.0 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64"
truststore==0.10.4 ; python_version >= "3.12" and python_version < "3.15"
typing-extensions==4.15.0 ; python_version >= "3.12" and python_version < "3.15"
typing-inspection==0.4.2 ; python_version >= "3.12" and python_version < "3.15"
diff --git a/skills/developing/table-query/SKILL.md b/skills/developing/table-query/SKILL.md
new file mode 100644
index 0000000..9a9156a
--- /dev/null
+++ b/skills/developing/table-query/SKILL.md
@@ -0,0 +1,137 @@
+---
+name: table-query
+description: Query structured spreadsheet/table data (Excel/CSV) to answer questions about values, prices, quantities, inventory, specifications, rankings, comparisons, summaries, aggregations, lists, or any numeric/tabular lookup. Use this skill whenever the answer likely comes from uploaded tables. You locate tables, read their schema, author SQLite SQL yourself, and run it — the backend does no LLM work, so it is fast.
+category: Data & Retrieval
+---
+
+# Table Query
+
+Answer table/spreadsheet questions by authoring and running SQLite SQL against the
+bot's uploaded Excel data. The backend is a thin, fast SQL executor — **you** do the
+thinking (rewrite the question, pick tables, write SQL). Row-level citations
+(`__src`) are produced for you.
+
+## When to use
+
+Use `table-query` for: values, prices, quantities, inventory, specifications,
+rankings, comparisons, summaries, aggregations (sum/avg/count), lists, person /
+project / product lookups, monthly/period totals, or any question whose answer
+comes from structured tables. For pure concept / definition / policy / explanation
+questions, use the `rag_retrieve` document tool instead.
+
+## Workflow (do this in order, once)
+
+1. **search-tables** — rewrite the user's question into a retrieval query (core
+ entity + attributes + synonyms), then locate candidate tables. Call this **once**.
+2. **get-schemas** — for the relevant subset of returned tables, fetch their
+ `CREATE TABLE` schema and sample rows. Never write SQL without seeing the schema.
+3. **author SQL** — write a SQLite query plan as JSON (see below).
+4. **run-sql** — execute the plan. It returns CSV with an `__src` column and a
+ `file_ref_table` mapping plus citation instructions.
+5. **answer + cite** — write the answer and add `` tags built from
+ `__src` + `file_ref_table`. Never print the `__src` column to the user.
+
+### Anti-waste rules
+
+- Call **search-tables at most once** per question. Do not re-locate tables you
+ already have schemas for.
+- If `run-sql` returns an error, fix the SQL and call **run-sql** again (at most ~2
+ tries). Do **NOT** restart from search-tables.
+- If `search-tables` finds nothing, fall back to the `rag_retrieve` document tool.
+
+## Commands
+
+```bash
+# 1. locate tables
+python {SKILL_DIR}/scripts/table_query.py search-tables --query "2025 April May June sales total" --top-k 20
+
+# 2. read schema + sample rows for the tables you picked
+python {SKILL_DIR}/scripts/table_query.py get-schemas --tables "sales_2025,customers"
+
+# 3. run your authored plan — pipe the JSON plan via stdin (no temp file needed)
+python {SKILL_DIR}/scripts/table_query.py run-sql <<'PLAN'
+{"queries":[{"step":1,"sql":"CREATE TEMP TABLE \"final_table_step1\" AS SELECT \"month\", SUM(\"amount\") AS \"total\" FROM \"sales_2025\" GROUP BY \"month\"","source_table_names":["sales_2025"],"destine_table_name":"final_table_step1","destine_table_type":"final","destine_table_description":"Monthly totals"}]}
+PLAN
+```
+
+## Authoring the SQL plan
+
+The plan is a JSON object `{ "queries": [ ... ] }` that you pass to `run-sql` **on
+stdin via a quoted heredoc** (`<<'PLAN' ... PLAN`). The quoted delimiter keeps all
+the double quotes, single quotes and `$` in your SQL intact — no shell escaping.
+(You may instead write it to a file and use `--plan-file path.json` if a plan is very
+large, but stdin is the default and needs no extra step.)
+
+Each query is one SQL step:
+
+```json
+{
+ "queries": [
+ {
+ "step": 1,
+ "sql": "CREATE TEMP TABLE \"final_table_step1\" AS SELECT \"month\", SUM(\"amount\") AS \"total\" FROM \"sales_2025\" WHERE \"month\" IN ('2025-04','2025-05','2025-06') GROUP BY \"month\"",
+ "source_table_names": ["sales_2025"],
+ "destine_table_name": "final_table_step1",
+ "destine_table_type": "final",
+ "destine_table_description": "Monthly sales totals for Apr-Jun 2025"
+ }
+ ]
+}
+```
+
+Field meaning:
+- `step`: 1-based execution order.
+- `sql`: a SQLite statement, normally `CREATE TEMP TABLE "..." AS SELECT ...`.
+- `source_table_names`: tables this step reads (original tables, or earlier steps'
+ `destine_table_name` for multi-step plans).
+- `destine_table_name`: the temp table this step creates. Convention:
+ `intermediate_table_stepN` or `final_table_stepN`.
+- `destine_table_type`: `"final"` for results the user should see, `"intermediate"`
+ for helper steps. **At least one `final` is required.**
+- `destine_table_description`: short human description of the result.
+
+### SQL rules (important)
+
+- **Quote every identifier** with double quotes: `"column name"`, `"table name"`.
+- String literals use single quotes; escape `'` as `''`.
+- Prefer **one logical result per `final` table**. For multiple separate results,
+ emit multiple `final` tables (e.g. step1, step2) — do **NOT** `UNION` unrelated results.
+- For row-level citations to be precise, keep `final` steps as simple single-table
+ `SELECT`s (no `JOIN` / `GROUP BY` / aggregation). Aggregations still work but the
+ citation degrades to file+sheet level (`F1S2`) instead of an exact row (`F1S2R5`).
+- Multi-step plans run in `step` order: build `intermediate_table_stepN` first, then
+ read it in a later step. Don't reference a temp table before it is created.
+- **Sample rows are a format hint only** — never assume they represent the full data
+ or the row count. Your SQL must scan the whole table. Use `LIKE '%value%'` for free
+ text and `=` for enums/codes.
+
+## Result handling & citations
+
+- `run-sql` output begins with citation instructions, then `file_ref_table`, then the
+ result CSV (with `__src`).
+- Parse `__src` (`F1S2R5` = file_ref F1, sheet 2, row 5) and `file_ref_table` to build
+ ``.
+- Put citations on their own line **after** the list/table that uses the data; combine
+ same-(file,sheet) rows into one citation.
+- If the result hint says rows were truncated (`Only the first N rows ...; the
+ remaining M ...`), tell the user the total (`N+M`), shown (`N`), and omitted (`M`).
+- Never expose the `__src` column itself to the user.
+
+### Controlling truncation
+
+`run-sql` truncates results by default (total rows and per-cell characters) to keep
+the context manageable. If a result comes back truncated and you genuinely need more,
+re-run with higher limits — do **not** re-run search-tables:
+
+```bash
+python {SKILL_DIR}/scripts/table_query.py run-sql --max-rows 500 --cell-max 4000 <<'PLAN'
+{"queries":[ ... ]}
+PLAN
+```
+
+- `--max-rows`: max total rows across all `final` tables (default from backend config,
+ hard ceiling 2000). Prefer writing an aggregate query (SUM/COUNT/GROUP BY) over
+ pulling thousands of detail rows.
+- `--cell-max`: max characters per cell before it is truncated with `..` (default from
+ backend config, hard ceiling 10000). Raise this when a long-text column (e.g. a
+ description/spec field) is getting cut off.
diff --git a/skills/developing/table-query/scripts/table_query.py b/skills/developing/table-query/scripts/table_query.py
new file mode 100755
index 0000000..b45a121
--- /dev/null
+++ b/skills/developing/table-query/scripts/table_query.py
@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+"""
+table-query CLI.
+
+Fast, LLM-free table querying. Talks to the felo-mygpt table_query endpoints:
+ - search-tables : POST /v1/table_query/search_tables/{bot_id}
+ - get-schemas : POST /v1/table_query/get_schemas/{bot_id}
+ - run-sql : POST /v1/table_query/run_sql/{bot_id}
+
+The agent drives the orchestration (rewrite -> locate -> author SQL -> run);
+the backend only does cheap work, so each call returns in seconds.
+"""
+
+import argparse
+import hashlib
+import json
+import os
+import sys
+
+try:
+ import requests
+except ImportError:
+ print("Error: requests module is required. Please install it with: pip install requests")
+ sys.exit(1)
+
+DEFAULT_BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
+DEFAULT_MASTERKEY = os.getenv("MASTERKEY", "master")
+
+# Same citation contract the legacy table_rag_retrieve used, so the agent's
+# behaviour is unchanged.
+TABLE_CITATION_INSTRUCTIONS = """
+When using the retrieved table knowledge below, you MUST add XML citation tags for factual claims.
+
+Format: ``
+- Parse `__src`: `F1S2R5` = file_ref F1, sheet 2, row 5
+- Look up file_id in `file_ref_table`
+- Combine same-sheet rows into one citation: `rows=[2, 4, 6]`
+- MANDATORY: Create SEPARATE citation for EACH (file, sheet) combination
+- NEVER put on the same line as a bullet point or table row
+- Citations MUST be on separate lines AFTER the complete list/table
+- NEVER include the `__src` column in your response - it is internal metadata only
+- Citations MUST appear IMMEDIATELY AFTER the paragraph or bullet list that uses the knowledge
+- NEVER collect all citations and place them at the end of your response
+
+"""
+
+
+def load_config() -> dict:
+ """Load robot_config.json from the robot project root (3 levels up from scripts/)."""
+ config_path = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'robot_config.json')
+ if os.path.exists(config_path):
+ try:
+ with open(config_path, 'r', encoding='utf-8') as f:
+ return json.load(f)
+ except (json.JSONDecodeError, IOError) as e:
+ print(f"Warning: failed to load robot_config.json: {e}", file=sys.stderr)
+ return {}
+
+
+def _resolve_bot_id(cli_bot_id: str) -> str:
+ if cli_bot_id:
+ return cli_bot_id
+ return load_config().get('bot_id') or os.getenv("BOT_ID") or os.getenv("ASSISTANT_ID")
+
+
+def _post(path: str, bot_id: str, payload: dict) -> dict:
+ url = f"{DEFAULT_BACKEND_HOST}/v1/table_query/{path}/{bot_id}"
+ auth_token = hashlib.md5(f"{DEFAULT_MASTERKEY}:{bot_id}".encode()).hexdigest()
+ headers = {
+ "content-type": "application/json",
+ "authorization": f"Bearer {auth_token}",
+ }
+ trace_id = os.getenv("TRACE_ID") or os.getenv("X_REQUEST_ID")
+ if trace_id:
+ headers["X-Request-ID"] = trace_id
+ resp = requests.post(url, json=payload, headers=headers, timeout=30)
+ if resp.status_code != 200:
+ raise RuntimeError(f"API {path} returned {resp.status_code}: {resp.text}")
+ return resp.json()
+
+
+def cmd_search_tables(args, bot_id: str) -> str:
+ res = _post("search_tables", bot_id, {"query": args.query, "top_k": args.top_k})
+ tables = res.get("tables", [])
+ if not tables:
+ return ("No matching tables found. If the question may be answered from documents "
+ "instead of spreadsheets, fall back to the rag_retrieve document tool.")
+ lines = [f"Found {len(tables)} candidate table(s). Pick the relevant ones and call "
+ f"`get-schemas` for them next.\n"]
+ for t in tables:
+ lines.append(
+ f"- table_name: {t['table_name']}\n"
+ f" file: {t.get('file_name','')} | sheet: {t.get('sheet_name','')} "
+ f"| score: {round(t.get('score', 0), 3)}\n"
+ f" description: {t.get('table_description','')}"
+ )
+ return "\n".join(lines)
+
+
+def cmd_get_schemas(args, bot_id: str) -> str:
+ table_names = [t.strip() for t in args.tables.split(',') if t.strip()]
+ res = _post("get_schemas", bot_id,
+ {"table_names": table_names, "sample_rows": args.sample_rows})
+ schemas = res.get("schemas", [])
+ missing = res.get("missing_tables", [])
+ if not schemas:
+ return f"No schemas resolved. Missing tables: {missing}"
+ blocks = []
+ for s in schemas:
+ block = [f"### Table: {s['table_name']}",
+ f"File: {s.get('file_name','')} | Sheet: {s.get('sheet_name','')}",
+ "```sql", s.get('sql_create', ''), "```"]
+ sample = s.get('sample_rows') or []
+ if sample:
+ block.append("Sample rows (format hint only, NOT the row count):")
+ block.append("```csv")
+ for row in sample:
+ block.append(",".join('"' + str(c).replace('"', '""') + '"' for c in row))
+ block.append("```")
+ blocks.append("\n".join(block))
+ out = "\n\n".join(blocks)
+ if missing:
+ out += f"\n\nNote: these requested tables were not found: {missing}"
+ out += ("\n\nNow author a SQLite plan and run it by piping the JSON to run-sql on stdin:\n"
+ " run-sql <<'PLAN'\n"
+ " {\"queries\": [{\"step\": 1, \"sql\": \"CREATE TEMP TABLE \\\"final_table_step1\\\" "
+ "AS SELECT ...\", \"source_table_names\": [\"...\"], "
+ "\"destine_table_name\": \"final_table_step1\", \"destine_table_type\": \"final\"}]}\n"
+ " PLAN\n"
+ "Quote all identifiers with double quotes.")
+ return out
+
+
+def cmd_run_sql(args, bot_id: str) -> str:
+ # Read the plan from --plan-file if given, otherwise from stdin (heredoc).
+ try:
+ if args.plan_file:
+ with open(args.plan_file, 'r', encoding='utf-8') as f:
+ raw = f.read()
+ else:
+ raw = sys.stdin.read()
+ if not raw.strip():
+ return ("Error: no plan provided. Pipe the JSON plan via stdin, e.g.\n"
+ " python scripts/table_query.py run-sql <<'PLAN'\n"
+ " {\"queries\": [...]}\n"
+ " PLAN")
+ plan = json.loads(raw)
+ except (json.JSONDecodeError, IOError) as e:
+ return f"Error: failed to read SQL plan: {e}"
+ # accept either {"queries": [...]} or a bare [...] list
+ queries = plan.get("queries") if isinstance(plan, dict) else plan
+ if not queries:
+ return "Error: the plan must contain a non-empty `queries` list."
+ payload = {"queries": queries}
+ if args.max_rows is not None:
+ payload["max_rows"] = args.max_rows
+ if args.cell_max is not None:
+ payload["cell_max"] = args.cell_max
+ res = _post("run_sql", bot_id, payload)
+ if not res.get("success"):
+ return (f"SQL execution failed: {res.get('error')}\n"
+ "Fix your SQL and call run-sql again. Do NOT restart from search-tables.")
+ parts = [TABLE_CITATION_INSTRUCTIONS]
+ if res.get("instruction"):
+ parts.append(res["instruction"])
+ if res.get("knowledge"):
+ parts.append(res["knowledge"])
+ if res.get("extra_goal"):
+ parts.append(res["extra_goal"])
+ return "\n".join(parts)
+
+
+def main():
+ parser = argparse.ArgumentParser(description="table-query: fast LLM-free table querying")
+ parser.add_argument("--bot-id", default=None, help="Bot id (defaults to robot_config.json)")
+ sub = parser.add_subparsers(dest="command", required=True)
+
+ p_search = sub.add_parser("search-tables", help="Vector-locate relevant tables")
+ p_search.add_argument("--query", "-q", required=True, help="Rewritten retrieval query")
+ p_search.add_argument("--top-k", "-k", type=int, default=20)
+
+ p_schemas = sub.add_parser("get-schemas", help="Fetch CREATE TABLE schema + sample rows")
+ p_schemas.add_argument("--tables", "-t", required=True, help="Comma-separated table names")
+ p_schemas.add_argument("--sample-rows", type=int, default=3)
+
+ p_run = sub.add_parser("run-sql", help="Execute an authored SQL plan (JSON via stdin or file)")
+ p_run.add_argument("--plan-file", "-f", default=None,
+ help="Path to plan JSON file (optional; defaults to reading stdin)")
+ p_run.add_argument("--max-rows", type=int, default=None,
+ help="Max total result rows (raise if a result came back truncated)")
+ p_run.add_argument("--cell-max", type=int, default=None,
+ help="Max characters per cell before truncation")
+
+ args = parser.parse_args()
+ bot_id = _resolve_bot_id(args.bot_id)
+ if not bot_id:
+ print("Error: bot_id is required (robot_config.json / --bot-id / BOT_ID env)")
+ sys.exit(1)
+
+ try:
+ if args.command == "search-tables":
+ print(cmd_search_tables(args, bot_id))
+ elif args.command == "get-schemas":
+ print(cmd_get_schemas(args, bot_id))
+ elif args.command == "run-sql":
+ print(cmd_run_sql(args, bot_id))
+ except Exception as e:
+ print(f"Error: {str(e)}")
+ sys.exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/skills/developing/table-query/skill.yaml b/skills/developing/table-query/skill.yaml
new file mode 100644
index 0000000..839dda9
--- /dev/null
+++ b/skills/developing/table-query/skill.yaml
@@ -0,0 +1,25 @@
+name: table-query
+version: 1.0.0
+description: Fast LLM-free table querying. Locate tables, fetch schema, author SQLite SQL, and run it with row-level citations.
+author:
+ name: sparticle
+ email: support@gbase.ai
+license: MIT
+tags:
+ - table
+ - sql
+ - excel
+ - retrieval
+ - citation
+runtime:
+ python: ">=3.7"
+ dependencies:
+ - requests
+entry_point: scripts/table_query.py
+commands:
+ search-tables:
+ description: Vector-locate relevant tables for a query
+ get-schemas:
+ description: Fetch CREATE TABLE schema + sample rows for given tables
+ run-sql:
+ description: Execute an authored SQLite plan and return CSV with __src citations
diff --git a/skills/developing/table-query/verify_table_query.sh b/skills/developing/table-query/verify_table_query.sh
new file mode 100755
index 0000000..f6de962
--- /dev/null
+++ b/skills/developing/table-query/verify_table_query.sh
@@ -0,0 +1,67 @@
+#!/usr/bin/env bash
+#
+# Manual verification for the new table_query endpoints.
+# Run this against an environment where the feature/table-query-split branch is
+# deployed (e.g. dev). It checks the 3 fast endpoints and diffs run_sql output
+# against the legacy table_rag_retrieve for parity.
+#
+# Usage:
+# HOST=https://api-dev.gptbase.ai BOT_ID= MASTERKEY=master ./verify_table_query.sh
+#
+set -euo pipefail
+
+HOST="${HOST:-https://api-dev.gptbase.ai}"
+# bot from the slow-request log (has the 案1_売上明細 xlsx). Override as needed.
+BOT_ID="${BOT_ID:-c1fa021b-6c41-41d5-b1e6-adfb8896aaaa}"
+MASTERKEY="${MASTERKEY:-master}"
+QUERY="${QUERY:-2025年4月〜6月の売上実績}"
+
+# auth token = MD5(masterkey:bot_id)
+TOKEN=$(python3 -c "import hashlib,sys;print(hashlib.md5(f'{sys.argv[1]}:{sys.argv[2]}'.encode()).hexdigest())" "$MASTERKEY" "$BOT_ID")
+AUTH="authorization: Bearer ${TOKEN}"
+CT="content-type: application/json"
+
+echo "=== HOST=$HOST BOT_ID=$BOT_ID ==="
+
+echo
+echo "### 1) search_tables ###"
+curl -s --request POST "$HOST/v1/table_query/search_tables/$BOT_ID" \
+ --header "$AUTH" --header "$CT" \
+ --data "{\"query\": \"$QUERY\", \"top_k\": 20}" | python3 -m json.tool
+
+echo
+echo "### 2) get_schemas (EDIT --data table_names with names from step 1) ###"
+echo "curl -s --request POST \"$HOST/v1/table_query/get_schemas/$BOT_ID\" \\"
+echo " --header \"$AUTH\" --header \"$CT\" \\"
+echo " --data '{\"table_names\": [\"\"], \"sample_rows\": 3}' | python3 -m json.tool"
+
+echo
+echo "### 3) run_sql (EDIT the sql to match the real table/columns from step 2) ###"
+cat > /tmp/tq_plan.json <<'JSON'
+{
+ "queries": [
+ {
+ "step": 1,
+ "sql": "CREATE TEMP TABLE \"final_table_step1\" AS SELECT \"計上日\", \"得意先名\", \"売上金額\" FROM \"\" LIMIT 10",
+ "source_table_names": [""],
+ "destine_table_name": "final_table_step1",
+ "destine_table_type": "final",
+ "destine_table_description": "sample rows"
+ }
+ ]
+}
+JSON
+echo "Edit /tmp/tq_plan.json (replace ), then:"
+echo "curl -s --request POST \"$HOST/v1/table_query/run_sql/$BOT_ID\" \\"
+echo " --header \"$AUTH\" --header \"$CT\" \\"
+echo " --data @/tmp/tq_plan.json | python3 -m json.tool"
+echo
+echo "ASSERT: run_sql output 'knowledge' contains a '__src' column and 'file_ref_table'."
+
+echo
+echo "### 4) legacy table_rag_retrieve (parity reference, same question) ###"
+echo "curl -s --request POST \"$HOST/v1/table_rag_retrieve/$BOT_ID\" \\"
+echo " --header \"$AUTH\" --header \"$CT\" \\"
+echo " --data '{\"query\": \"$QUERY\"}' | python3 -m json.tool"
+echo
+echo "Compare the __src tokens / result rows between #3 and #4 for the same SQL intent."
diff --git a/utils/system_optimizer.py b/utils/system_optimizer.py
index 1f2b508..d4765b5 100644
--- a/utils/system_optimizer.py
+++ b/utils/system_optimizer.py
@@ -75,10 +75,6 @@ class SystemOptimizer:
# Network optimizations
'TCP_NODELAY': '1', # Disable Nagle's algorithm
- # Hugging Face optimizations
- 'TRANSFORMERS_CACHE': '/tmp/transformers_cache', # Use tmpfs for faster cache access
- 'HF_OFFLINE': '0', # Online mode
-
# CUDA optimizations if GPU is used
'CUDA_LAUNCH_BLOCKING': '0', # Asynchronous CUDA launch
@@ -310,4 +306,4 @@ def get_global_system_optimizer() -> SystemOptimizer:
global _global_system_optimizer
if _global_system_optimizer is None:
_global_system_optimizer = SystemOptimizer()
- return _global_system_optimizer
\ No newline at end of file
+ return _global_system_optimizer