Add training and planning eval exports

skills/product-data/README.md

@@ -45,6 +45,8 @@ cat input.json | python skills/product-data/scripts/<tool>.py
 | `scripts/validate_dataset_records.py` | 校验 canonical records |
 | `scripts/export_dataset_records.py` | 导出紧凑 JSONL/JSON，并默认生成同目录 `records.csv` 表格 |
 | `scripts/export_dataset_table.py` | 已有 canonical records 时，只补生成同事流转表格 |
+| `scripts/export_training_jsonl.py` | 把 canonical records 转成训练 JSONL |
+| `scripts/export_planning_eval_csv.py` | 把 canonical records 转成含 `newPrompt` 的评测 CSV |
 
 ## 平台内优先级
 

skills/product-data/SKILL.md

@@ -3,7 +3,7 @@ name: product-data
 description: 从产品/标签定义、手写边界规则或示例 query 中提取标签边界，并生成可 review 的数据计划、dataset draft text 和 canonical metadata records。
 when_to_use: 当用户提供产品定义、标签规则、路由边界文档、示例 query、手写标签边界，并希望生成训练/评测/专项数据时使用。
 aliases: definition-data, label-data
-allowed_tools: read_file, write_file, edit_file, grep_search, glob_search, ask_user_question, python_exec, data_agent_load_input_sources, data_agent_render_source_context, data_agent_extract_case_evidence, data_agent_prepare_generation_goal, data_agent_confirm_generation_goal, data_agent_prepare_generation_plan, data_agent_show_generation_plan, data_agent_update_generation_plan, data_agent_confirm_generation_plan, data_agent_normalize_dataset_draft, data_agent_validate_dataset_records, data_agent_export_dataset_records
+allowed_tools: read_file, write_file, edit_file, grep_search, glob_search, ask_user_question, python_exec, data_agent_load_input_sources, data_agent_render_source_context, data_agent_extract_case_evidence, data_agent_prepare_generation_goal, data_agent_confirm_generation_goal, data_agent_prepare_generation_plan, data_agent_show_generation_plan, data_agent_update_generation_plan, data_agent_confirm_generation_plan, data_agent_normalize_dataset_draft, data_agent_validate_dataset_records, data_agent_export_dataset_records, data_agent_export_training_jsonl, data_agent_export_planning_eval_csv
 ---
 
 使用这个 skill 作为“产品/标签定义/手写规则/示例 query -> 输入文本化 -> generation goal 草案 -> 人工 review -> 生成计划 review -> dataset draft text -> canonical metadata records”的统一入口。
@@ -29,6 +29,8 @@ skills/product-data/
     validate_dataset_records.py
     export_dataset_records.py
     export_dataset_table.py
+    export_training_jsonl.py
+    export_planning_eval_csv.py
 ```
 
 在 ZK Data Agent 平台里，优先使用已注册的 `data_agent_*` 工具，因为它们带有平台级 review 状态、确认门禁和会话 output 路由。
@@ -148,7 +150,7 @@ review 展示必须简短清晰，不要重复解释工具和流程。每次 rev
 
 ## 必须遵守的数据生成协议
 
-不要直接生成 canonical JSON，不要直接导出最终训练/评测格式，不要绕过人类 review。
+不要直接生成 canonical JSON，不要在 canonical records 校验通过前导出训练/评测格式，不要绕过人类 review。
 
 如果需要生成数据，必须按顺序执行：
 
@@ -166,7 +168,9 @@ review 展示必须简短清晰，不要重复解释工具和流程。每次 rev
 12. 调用 `data_agent_normalize_dataset_draft`，必须传入 `confirmed_plan_id`。
 13. 调用 `data_agent_validate_dataset_records`。
 14. 如果用户要求落盘 canonical records，调用 `data_agent_export_dataset_records`，`output_path` 固定传 `output/records.jsonl`，默认导出紧凑 JSONL，并同时生成同目录 `records.csv` 表格，不要用 `write_file` 手写 JSON 或 CSV。
-15. 本阶段默认只推进到 canonical metadata records；除非用户另行要求，不做最终训练/评测格式导出。
+15. 如果用户已经明确要训练数据，调用 `data_agent_export_training_jsonl`，优先传 `records_path`，输出固定为 `output/training.jsonl`。
+16. 如果用户已经明确要评测 planningPrompt 数据，调用 `data_agent_export_planning_eval_csv`，优先传 `records_path`，输出固定为 `output/eval_planning.csv`。
+17. 如果用户只说“生成数据”但没有说明下游用途，生成 canonical records 和 `records.csv` 后，简短询问用户是否还需要导出训练 jsonl 或评测 csv；不要自己默认生成全部最终格式。
 
 生成阶段要避免一次性把大量数据塞进工具参数：
 
@@ -218,6 +222,13 @@ notes: 可选，说明覆盖的问题或边界
 
 canonical records 落盘必须使用 `data_agent_export_dataset_records`，默认格式是紧凑 JSONL：一行一个 canonical record，不带外层数组，不手写缩进 JSON。默认 `output_path` 固定传 `output/records.jsonl`；不要传 `output/<数据集名>/records.jsonl`，不要传 `tasks/...`，不要自定义文件名。工具默认同时在同目录生成 `records.csv`，用于同事之间流转和表格查看。只有用户明确要求兼容旧文件时，才使用 `output_format="json"` 导出紧凑 JSON 数组，此时工具会归一为 `output/records.json`，但表格仍然是同目录 `records.csv`。
 
+派生格式也必须从 canonical records 转换，不要让模型手写：
+
+- 训练数据：调用 `data_agent_export_training_jsonl`，输出 `training.jsonl`，每行包含 `system`、`instruction`、`output`。
+- 评测数据：调用 `data_agent_export_planning_eval_csv`，输出 `eval_planning.csv`，字段为 `request_id,newPrompt,query,类别真实标签,code标签,complex`。
+- 两个派生格式默认复用相同 prompt 构造逻辑：`[知识注入]`、`[系统状态]`、`[对话历史]`、`[当前query]`、`[function]`。
+- 对话历史默认最多取 5 轮，且相邻轮间隔不超过 5 分钟；`context` 默认注入 `location` 和 `rag` 两个字段。
+
 当前 canonical record v1 工作格式：
 
 ```json
@@ -427,6 +438,44 @@ skills/product-data/scripts/export_dataset_table.py
 
 用途：已有 canonical records 时，只补生成同事流转表格 `records.csv`，不改写元数据文件。主流程仍优先用 `product_data_export_dataset_records`，因为它会一次性导出元数据和表格。
 
+### `product_data_export_training_jsonl`
+
+脚本：
+
+```text
+skills/product-data/scripts/export_training_jsonl.py
+```
+
+输入支持：
+
+```json
+{
+  "records_path": "output/records.jsonl",
+  "output_dir": "output"
+}
+```
+
+用途：把 canonical records 转成训练 JSONL，默认文件名 `training.jsonl`。
+
+### `product_data_export_planning_eval_csv`
+
+脚本：
+
+```text
+skills/product-data/scripts/export_planning_eval_csv.py
+```
+
+输入支持：
+
+```json
+{
+  "records_path": "output/records.jsonl",
+  "output_dir": "output"
+}
+```
+
+用途：把 canonical records 转成含 `newPrompt` 的评测 CSV，默认文件名 `eval_planning.csv`。
+
 ## 当前可用工具
 
 - `read_file`：读取用户提供的产品定义、标签定义、样例 query 文件。
@@ -448,6 +497,8 @@ skills/product-data/scripts/export_dataset_table.py
 - `data_agent_normalize_dataset_draft`：用户确认计划后，把 dataset draft text v1 转成 canonical records；必须传入 `confirmed_plan_id`；支持 `draft_text` 或 `draft_path`，大草稿优先 `draft_path`。
 - `data_agent_validate_dataset_records`：对 canonical records 做结构、标签、时间戳和多轮上下文校验；支持 `records` 或 `records_path`。
 - `data_agent_export_dataset_records`：校验 canonical records 并落盘；支持 `records` 或 `records_path`；默认写紧凑 JSONL，一行一条，固定传 `output/records.jsonl`，并同时生成 `output/records.csv` 表格，不要再用 `write_file` 手写 records 或表格文件。
+- `data_agent_export_training_jsonl`：把 canonical records 转成训练 JSONL；支持 `records` 或 `records_path`；默认写 `output/training.jsonl`。
+- `data_agent_export_planning_eval_csv`：把 canonical records 转成评测 CSV；支持 `records` 或 `records_path`；默认写 `output/eval_planning.csv`。
 
 ## 约束
 
@@ -455,6 +506,7 @@ skills/product-data/scripts/export_dataset_table.py
 - 如果 `ask_user_question` 不可用，使用普通回复向用户提问并停止，不要自己替用户确认。
 - canonical records 通过校验前，不要生成最终导出格式。
 - canonical records 需要落盘时，必须用 `data_agent_export_dataset_records`；不要自己拼接 JSON/JSONL/CSV；不要创建数据集子目录或自定义 records 文件名。
+- 训练/评测派生格式必须从 canonical records 通过工具导出；不要让模型自己拼 prompt、手写 jsonl 或 csv。
 - 如果使用 portable scripts，它们只负责格式转换、校验和导出，不替代 `generation_goal` / `generation_plan` 的用户 review。
 - 除非用户明确要求，否则不要把“修改标签定义”和“生成数据”混在一起做。
 - 不要因为用户说“生成一些数据”就跳过边界总结和 generation plan review。

skills/product-data/knowledge/canonical_record_v1.md

@@ -55,3 +55,12 @@ canonical record 是产品数据生成链路的中间元数据格式。它不是
 `records.csv` 中的 `prev_session` 和 `context` 是紧凑 JSON 字符串；`prev_session` 中的 `timestamp` 按历史表格习惯输出为字符串。
 
 如果已有 `records.jsonl` 或 `records.json`，只需要补表格，可以执行 portable `export_dataset_table.py` 或等价工具；默认仍输出到同目录的 `records.csv`。
+
+## 派生格式
+
+训练和评测格式都从 canonical records 转换，不由模型手写：
+
+- `training.jsonl`：每行 `{"system": "...", "instruction": "...", "output": "..."}`，由 `export_training_jsonl.py` 或 `data_agent_export_training_jsonl` 生成。
+- `eval_planning.csv`：字段为 `request_id,newPrompt,query,类别真实标签,code标签,complex`，由 `export_planning_eval_csv.py` 或 `data_agent_export_planning_eval_csv` 生成。
+
+`instruction` 和 `newPrompt` 使用同一套 prompt 模板，默认包含 `[知识注入]`、`[系统状态]`、`[对话历史]`、`[当前query]`、`[function]`。历史轮次默认最多取 5 轮，且相邻时间间隔不超过 5 分钟。

skills/product-data/schemas/export_planning_eval_csv.input.schema.json

@@ -0,0 +1,25 @@
+{
+  "type": "object",
+  "properties": {
+    "records": {
+      "type": "array",
+      "description": "Canonical records. Provide exactly one of records or records_path."
+    },
+    "records_path": {
+      "type": "string",
+      "description": "Path to records JSON/JSONL. Provide exactly one of records or records_path."
+    },
+    "output_path": {"type": "string"},
+    "output_dir": {"type": "string"},
+    "session_num": {"type": "integer", "minimum": 1, "default": 5},
+    "session_time_minutes": {"type": "integer", "minimum": 1, "default": 5},
+    "context_fields": {
+      "type": "array",
+      "items": {"type": "string"},
+      "default": ["location", "rag"]
+    },
+    "complex_default": {"type": "boolean", "default": false},
+    "require_validation_ok": {"type": "boolean", "default": true},
+    "overwrite": {"type": "boolean", "default": true}
+  }
+}

skills/product-data/schemas/export_planning_eval_csv.output.schema.json

@@ -0,0 +1,13 @@
+{
+  "type": "object",
+  "properties": {
+    "ok": {"type": "boolean"},
+    "output_path": {"type": "string"},
+    "output_format": {"type": "string"},
+    "record_count": {"type": "integer"},
+    "bytes_written": {"type": "integer"},
+    "validation": {"type": "object"},
+    "error": {"type": "string"}
+  },
+  "required": ["ok"]
+}

skills/product-data/schemas/export_training_jsonl.input.schema.json

@@ -0,0 +1,28 @@
+{
+  "type": "object",
+  "properties": {
+    "records": {
+      "type": "array",
+      "description": "Canonical records. Provide exactly one of records or records_path."
+    },
+    "records_path": {
+      "type": "string",
+      "description": "Path to records JSON/JSONL. Provide exactly one of records or records_path."
+    },
+    "output_path": {"type": "string"},
+    "output_dir": {"type": "string"},
+    "session_num": {"type": "integer", "minimum": 1, "default": 5},
+    "session_time_minutes": {"type": "integer", "minimum": 1, "default": 5},
+    "context_fields": {
+      "type": "array",
+      "items": {"type": "string"},
+      "default": ["location", "rag"]
+    },
+    "system_prompt": {
+      "type": "string",
+      "default": "你是小爱同学，中文智能语音助手。"
+    },
+    "require_validation_ok": {"type": "boolean", "default": true},
+    "overwrite": {"type": "boolean", "default": true}
+  }
+}

skills/product-data/schemas/export_training_jsonl.output.schema.json

@@ -0,0 +1,13 @@
+{
+  "type": "object",
+  "properties": {
+    "ok": {"type": "boolean"},
+    "output_path": {"type": "string"},
+    "output_format": {"type": "string"},
+    "record_count": {"type": "integer"},
+    "bytes_written": {"type": "integer"},
+    "validation": {"type": "object"},
+    "error": {"type": "string"}
+  },
+  "required": ["ok"]
+}

skills/product-data/scripts/export_planning_eval_csv.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from product_data_portable import (
+    default_planning_eval_output_path,
+    emit_error,
+    emit_success,
+    export_planning_eval_csv,
+    load_json_payload,
+    load_records,
+)
+
+
+def main() -> int:
+    try:
+        payload = load_json_payload()
+        records = load_records(payload)
+        result = export_planning_eval_csv(
+            records,
+            output_path=default_planning_eval_output_path(payload),
+            session_num=int(payload.get("session_num", 5)),
+            session_time_minutes=int(payload.get("session_time_minutes", 5)),
+            context_fields=payload.get("context_fields"),
+            complex_default=bool(payload.get("complex_default", False)),
+            require_validation_ok=bool(payload.get("require_validation_ok", True)),
+            overwrite=bool(payload.get("overwrite", True)),
+        )
+        emit_success(result)
+        return 0
+    except Exception as exc:  # noqa: BLE001 - CLI 需要把错误稳定转成 JSON
+        emit_error(exc)
+        return 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

skills/product-data/scripts/export_training_jsonl.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from product_data_portable import (
+    DEFAULT_SYSTEM_PROMPT,
+    default_training_output_path,
+    emit_error,
+    emit_success,
+    export_training_jsonl,
+    load_json_payload,
+    load_records,
+)
+
+
+def main() -> int:
+    try:
+        payload = load_json_payload()
+        records = load_records(payload)
+        result = export_training_jsonl(
+            records,
+            output_path=default_training_output_path(payload),
+            session_num=int(payload.get("session_num", 5)),
+            session_time_minutes=int(payload.get("session_time_minutes", 5)),
+            context_fields=payload.get("context_fields"),
+            system_prompt=str(payload.get("system_prompt") or DEFAULT_SYSTEM_PROMPT),
+            require_validation_ok=bool(payload.get("require_validation_ok", True)),
+            overwrite=bool(payload.get("overwrite", True)),
+        )
+        emit_success(result)
+        return 0
+    except Exception as exc:  # noqa: BLE001 - CLI 需要把错误稳定转成 JSON
+        emit_error(exc)
+        return 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

skills/product-data/scripts/product_data_portable.py

@@ -19,6 +19,8 @@ from typing import Any
 
 DEFAULT_REQUEST_ID = "aabbccdd"
 DEFAULT_TIMESTAMP_STEP_MS = 60_000
+DEFAULT_SYSTEM_PROMPT = "你是小爱同学，中文智能语音助手。"
+DEFAULT_CONTEXT_FIELDS = ("location", "rag")
 
 
 class ProductDataError(ValueError):
@@ -205,7 +207,7 @@ def case_to_record(
     if not current_query:
         raise ProductDataError(f"case {index} current 用户 line must be non-empty")
 
-    target = str(case.get("target") or "").strip()
+    target = canonical_target(str(case.get("target") or "").strip())
     if not target:
         raise ProductDataError(f"case {index} target is required")
 
@@ -447,6 +449,89 @@ def export_dataset_table(
     }
 
 
+def export_training_jsonl(
+    records: list[dict[str, Any]],
+    *,
+    output_path: str,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    system_prompt: str = DEFAULT_SYSTEM_PROMPT,
+    require_validation_ok: bool = True,
+    overwrite: bool = True,
+) -> dict[str, Any]:
+    if session_num <= 0:
+        raise ProductDataError("session_num must be greater than 0")
+    if session_time_minutes <= 0:
+        raise ProductDataError("session_time_minutes must be greater than 0")
+    validation = validate_dataset_records(records)
+    if require_validation_ok and not validation["ok"]:
+        raise ProductDataError(f"records failed validation with {validation['error_count']} errors")
+    path = Path(output_path).expanduser()
+    if path.exists() and not overwrite:
+        raise ProductDataError(f"output_path already exists: {output_path}")
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fields = normalize_context_fields(context_fields)
+    content = "".join(
+        training_jsonl_line(
+            record,
+            session_num=session_num,
+            session_time_minutes=session_time_minutes,
+            context_fields=fields,
+            system_prompt=system_prompt,
+        )
+        + "\n"
+        for record in records
+    )
+    path.write_text(content, encoding="utf-8")
+    return {
+        "output_path": str(path),
+        "output_format": "jsonl",
+        "record_count": len(records),
+        "bytes_written": len(content.encode("utf-8")),
+        "validation": validation,
+    }
+
+
+def export_planning_eval_csv(
+    records: list[dict[str, Any]],
+    *,
+    output_path: str,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    complex_default: bool = False,
+    require_validation_ok: bool = True,
+    overwrite: bool = True,
+) -> dict[str, Any]:
+    if session_num <= 0:
+        raise ProductDataError("session_num must be greater than 0")
+    if session_time_minutes <= 0:
+        raise ProductDataError("session_time_minutes must be greater than 0")
+    validation = validate_dataset_records(records)
+    if require_validation_ok and not validation["ok"]:
+        raise ProductDataError(f"records failed validation with {validation['error_count']} errors")
+    path = Path(output_path).expanduser()
+    if path.exists() and not overwrite:
+        raise ProductDataError(f"output_path already exists: {output_path}")
+    path.parent.mkdir(parents=True, exist_ok=True)
+    content = render_planning_eval_csv(
+        records,
+        session_num=session_num,
+        session_time_minutes=session_time_minutes,
+        context_fields=context_fields,
+        complex_default=complex_default,
+    )
+    path.write_text(content, encoding="utf-8-sig")
+    return {
+        "output_path": str(path),
+        "output_format": "csv",
+        "record_count": len(records),
+        "bytes_written": len(content.encode("utf-8-sig")),
+        "validation": validation,
+    }
+
+
 def render_dataset_records_table_csv(records: list[dict[str, Any]]) -> str:
     output = StringIO()
     writer = csv.DictWriter(output, fieldnames=dataset_table_columns(), lineterminator="\n")
@@ -456,6 +541,185 @@ def render_dataset_records_table_csv(records: list[dict[str, Any]]) -> str:
     return output.getvalue()
 
 
+def render_planning_eval_csv(
+    records: list[dict[str, Any]],
+    *,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    complex_default: bool = False,
+) -> str:
+    fields = normalize_context_fields(context_fields)
+    output = StringIO()
+    writer = csv.DictWriter(
+        output,
+        fieldnames=["request_id", "newPrompt", "query", "类别真实标签", "code标签", "complex"],
+        lineterminator="\n",
+    )
+    writer.writeheader()
+    for record in records:
+        source = record.get("source") if isinstance(record.get("source"), dict) else {}
+        turn = record.get("turn") if isinstance(record.get("turn"), dict) else {}
+        label = record.get("label") if isinstance(record.get("label"), dict) else {}
+        target = str(label.get("target") or "")
+        writer.writerow(
+            {
+                "request_id": str(source.get("request_id") or ""),
+                "newPrompt": build_training_instruction(
+                    record,
+                    session_num=session_num,
+                    session_time_minutes=session_time_minutes,
+                    context_fields=fields,
+                ),
+                "query": str(turn.get("query") or ""),
+                "类别真实标签": category_label_from_target(target),
+                "code标签": target,
+                "complex": "true" if complex_default else "false",
+            }
+        )
+    return output.getvalue()
+
+
+def build_training_instruction(
+    record: dict[str, Any],
+    *,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+) -> str:
+    turn = record.get("turn") if isinstance(record.get("turn"), dict) else {}
+    query = str(turn.get("query") or "")
+    context = record.get("context") if isinstance(record.get("context"), dict) else {}
+    prev_session = record.get("prev_session") if isinstance(record.get("prev_session"), list) else []
+    current_ts = optional_int_for_export(turn.get("timestamp"))
+    if current_ts is None:
+        source = record.get("source") if isinstance(record.get("source"), dict) else {}
+        current_ts = optional_int_for_export(source.get("timestamp"))
+    session, last_tts = training_history(
+        prev_session,
+        current_ts=current_ts,
+        session_num=session_num,
+        session_time_minutes=session_time_minutes,
+    )
+    fields = normalize_context_fields(context_fields)
+
+    instruction = "请参考用户的[当前query]、[对话历史]、[知识注入]、[系统状态]识别出[当前query]的[function]结果，[function]是python的code形式。\n"
+    instruction += "[知识注入]\n"
+    instruction += f"{context_prompt_block(context, fields)}\n"
+    instruction += "[系统状态]\n"
+    instruction += "{}\n"
+    instruction += "[对话历史]\n"
+    if session:
+        history_parts = [f"用户: {session_query}" for session_query in session]
+        if last_tts is not None:
+            history_parts.append(f"小爱: {last_tts}")
+        instruction += "\n".join(history_parts)
+        instruction += "\n"
+    instruction += "[当前query]\n"
+    instruction += f"用户: {query}\n"
+    instruction += "[function]\n"
+    return instruction
+
+
+def training_jsonl_line(
+    record: dict[str, Any],
+    *,
+    session_num: int,
+    session_time_minutes: int,
+    context_fields: list[str],
+    system_prompt: str,
+) -> str:
+    label = record.get("label") if isinstance(record.get("label"), dict) else {}
+    payload = {
+        "system": system_prompt,
+        "instruction": build_training_instruction(
+            record,
+            session_num=session_num,
+            session_time_minutes=session_time_minutes,
+            context_fields=context_fields,
+        ),
+        "output": str(label.get("target") or ""),
+    }
+    return json.dumps(payload, ensure_ascii=False, separators=(",", ":"))
+
+
+def training_history(
+    prev_session: list[Any],
+    *,
+    current_ts: int | None,
+    session_num: int,
+    session_time_minutes: int,
+) -> tuple[list[str], str | None]:
+    if current_ts is None:
+        return [], None
+    session_time_ms = session_time_minutes * 60 * 1000
+    valid_items: list[tuple[int, str, str]] = []
+    for item in prev_session:
+        if not isinstance(item, dict):
+            continue
+        ts = optional_int_for_export(item.get("timestamp"))
+        query = str(item.get("query") or "")
+        tts = str(item.get("tts") or "")
+        if ts is not None and ts > 0 and query:
+            valid_items.append((ts, query, tts))
+    valid_items.sort(key=lambda item: item[0])
+
+    session: list[str] = []
+    last_tts: str | None = None
+    last_ts = current_ts
+    count = 0
+    for ts, query, tts in reversed(valid_items):
+        if last_ts - ts <= session_time_ms and last_ts >= ts:
+            session.append(query)
+            if count == 0:
+                last_tts = tts
+            last_ts = ts
+            count += 1
+            if count >= session_num:
+                break
+        else:
+            break
+    session.reverse()
+    return session, last_tts
+
+
+def context_prompt_block(context: dict[str, Any], fields: list[str]) -> str:
+    lines = ["{"]
+    for index, field in enumerate(fields):
+        comma = "," if index < len(fields) - 1 else ""
+        value = str(context.get(field, ""))
+        lines.append(f'"{field}": {json.dumps(value, ensure_ascii=False)}{comma}')
+    lines.append("}")
+    return "\n".join(lines)
+
+
+def normalize_context_fields(context_fields: list[str] | None) -> list[str]:
+    if context_fields is not None and not isinstance(context_fields, list):
+        raise ProductDataError("context_fields must be an array of strings")
+    fields = context_fields or list(DEFAULT_CONTEXT_FIELDS)
+    normalized = [field.strip() for field in fields if isinstance(field, str) and field.strip()]
+    return normalized or list(DEFAULT_CONTEXT_FIELDS)
+
+
+def optional_int_for_export(value: Any) -> int | None:
+    if isinstance(value, bool) or value is None or value == "":
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def category_label_from_target(target: str) -> str:
+    agent_match = re.match(r'''^Agent\s*\(\s*tag\s*=\s*["']([^"']+)["']\s*\)$''', target.strip())
+    if agent_match:
+        return agent_match.group(1)
+    function_names = re.findall(r"\b([A-Za-z_][A-Za-z0-9_]*)\s*\(", target)
+    if function_names:
+        return function_names[-1]
+    return target
+
+
 def dataset_table_columns() -> list[str]:
     return [
         "request_id",
@@ -524,6 +788,26 @@ def default_table_output_path(payload: dict[str, Any]) -> str:
     return str(Path("output") / "records.csv")
 
 
+def default_training_output_path(payload: dict[str, Any]) -> str:
+    if isinstance(payload.get("output_path"), str) and payload["output_path"].strip():
+        return payload["output_path"].strip()
+    if isinstance(payload.get("output_dir"), str) and payload["output_dir"].strip():
+        return str(Path(payload["output_dir"]).expanduser() / "training.jsonl")
+    if isinstance(payload.get("records_path"), str) and payload["records_path"].strip():
+        return str(Path(payload["records_path"]).expanduser().with_name("training.jsonl"))
+    return str(Path("output") / "training.jsonl")
+
+
+def default_planning_eval_output_path(payload: dict[str, Any]) -> str:
+    if isinstance(payload.get("output_path"), str) and payload["output_path"].strip():
+        return payload["output_path"].strip()
+    if isinstance(payload.get("output_dir"), str) and payload["output_dir"].strip():
+        return str(Path(payload["output_dir"]).expanduser() / "eval_planning.csv")
+    if isinstance(payload.get("records_path"), str) and payload["records_path"].strip():
+        return str(Path(payload["records_path"]).expanduser().with_name("eval_planning.csv"))
+    return str(Path("output") / "eval_planning.csv")
+
+
 def target_type(target: str) -> str:
     if re.match(r"^Agent\s*\(\s*tag\s*=", target):
         return "agent"
@@ -532,6 +816,13 @@ def target_type(target: str) -> str:
     return "unknown"
 
 
+def canonical_target(target: str) -> str:
+    agent_match = re.match(r'''^Agent\s*\(\s*tag\s*=\s*["']([^"']+)["']\s*\)$''', target)
+    if agent_match:
+        return f'Agent(tag="{agent_match.group(1)}")'
+    return target
+
+
 def record_id(*, source_type: str, batch_id: str, request_id: str, index: int) -> str:
     if source_type == "generated":
         return f"gen_{safe_token(batch_id)}_{index:06d}"

skills/product-data/tools.yaml

@@ -38,3 +38,19 @@ tools:
     fallback:
       command: python skills/product-data/scripts/export_dataset_table.py --input <input.json>
       stdin: true
+  - name: product_data_export_training_jsonl
+    description: Convert canonical records into training JSONL lines with system, instruction and output fields.
+    script: scripts/export_training_jsonl.py
+    input_schema: schemas/export_training_jsonl.input.schema.json
+    output_schema: schemas/export_training_jsonl.output.schema.json
+    fallback:
+      command: python skills/product-data/scripts/export_training_jsonl.py --input <input.json>
+      stdin: true
+  - name: product_data_export_planning_eval_csv
+    description: Convert canonical records into evaluation CSV with request_id, newPrompt, query, 类别真实标签, code标签 and complex columns.
+    script: scripts/export_planning_eval_csv.py
+    input_schema: schemas/export_planning_eval_csv.input.schema.json
+    output_schema: schemas/export_planning_eval_csv.output.schema.json
+    fallback:
+      command: python skills/product-data/scripts/export_planning_eval_csv.py --input <input.json>
+      stdin: true

src/agent_tool_specs/data_agent.py

@@ -547,4 +547,102 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
             },
             handler=resolve_handler(handlers, 'data_agent_export_dataset_records', 'data-agent'),
         ),
+        AgentTool(
+            name='data_agent_export_training_jsonl',
+            description=(
+                'Convert canonical data-agent records into training JSONL. Each line has system, instruction, '
+                'and output. The instruction contains 知识注入, 系统状态, 对话历史, 当前query, and function sections.'
+            ),
+            parameters={
+                'type': 'object',
+                'properties': {
+                    'records': {
+                        'oneOf': [
+                            {'type': 'array'},
+                            {'type': 'string'},
+                        ],
+                        'description': 'Canonical records as an array, or a JSON string containing the array. Provide exactly one of records or records_path.',
+                    },
+                    'records_path': {
+                        'type': 'string',
+                        'description': 'Path to records JSON/JSONL. Prefer this when records are large. Provide exactly one of records or records_path.',
+                    },
+                    'output_path': {
+                        'type': 'string',
+                        'description': 'Optional requested path. Runtime normalizes to current session output/training.jsonl.',
+                    },
+                    'session_num': {
+                        'type': 'integer',
+                        'minimum': 1,
+                        'description': 'Max history queries to include. Defaults to 5.',
+                    },
+                    'session_time_minutes': {
+                        'type': 'integer',
+                        'minimum': 1,
+                        'description': 'Max adjacent history gap in minutes. Defaults to 5.',
+                    },
+                    'context_fields': {
+                        'type': 'array',
+                        'items': {'type': 'string'},
+                        'description': 'Context fields to inject. Defaults to ["location", "rag"].',
+                    },
+                    'system_prompt': {
+                        'type': 'string',
+                        'description': 'Defaults to 你是小爱同学，中文智能语音助手。',
+                    },
+                    'require_validation_ok': {'type': 'boolean'},
+                    'overwrite': {'type': 'boolean'},
+                },
+            },
+            handler=resolve_handler(handlers, 'data_agent_export_training_jsonl', 'data-agent'),
+        ),
+        AgentTool(
+            name='data_agent_export_planning_eval_csv',
+            description=(
+                'Convert canonical data-agent records into evaluation CSV with columns: request_id, newPrompt, '
+                'query, 类别真实标签, code标签, complex. newPrompt uses the same prompt body as training instruction.'
+            ),
+            parameters={
+                'type': 'object',
+                'properties': {
+                    'records': {
+                        'oneOf': [
+                            {'type': 'array'},
+                            {'type': 'string'},
+                        ],
+                        'description': 'Canonical records as an array, or a JSON string containing the array. Provide exactly one of records or records_path.',
+                    },
+                    'records_path': {
+                        'type': 'string',
+                        'description': 'Path to records JSON/JSONL. Prefer this when records are large. Provide exactly one of records or records_path.',
+                    },
+                    'output_path': {
+                        'type': 'string',
+                        'description': 'Optional requested path. Runtime normalizes to current session output/eval_planning.csv.',
+                    },
+                    'session_num': {
+                        'type': 'integer',
+                        'minimum': 1,
+                        'description': 'Max history queries to include. Defaults to 5.',
+                    },
+                    'session_time_minutes': {
+                        'type': 'integer',
+                        'minimum': 1,
+                        'description': 'Max adjacent history gap in minutes. Defaults to 5.',
+                    },
+                    'context_fields': {
+                        'type': 'array',
+                        'items': {'type': 'string'},
+                        'description': 'Context fields to inject. Defaults to ["location", "rag"].',
+                    },
+                    'complex_default': {
+                        'type': 'boolean',
+                        'description': 'Default complex column value. Defaults to false.',
+                    },
+                    'require_validation_ok': {'type': 'boolean'},
+                    'overwrite': {'type': 'boolean'},
+                },
+            },
+            handler=resolve_handler(handlers, 'data_agent_export_planning_eval_csv', 'data-agent'),
+        ),
     ]

src/agent_tools.py

@@ -34,6 +34,8 @@ from .data_agent_records import (
     confirm_generation_goal,
     confirm_generation_plan,
     export_dataset_records,
+    export_planning_eval_csv,
+    export_training_jsonl,
     get_generation_plan,
     normalize_dataset_draft,
     prepare_generation_goal,
@@ -1049,6 +1051,8 @@ def default_tool_registry() -> dict[str, AgentTool]:
             'data_agent_normalize_dataset_draft': _normalize_dataset_draft_tool,
             'data_agent_validate_dataset_records': _validate_dataset_records_tool,
             'data_agent_export_dataset_records': _export_dataset_records_tool,
+            'data_agent_export_training_jsonl': _export_training_jsonl_tool,
+            'data_agent_export_planning_eval_csv': _export_planning_eval_csv_tool,
         }),
     ]
     return {tool.name: tool for tool in tools}
@@ -1550,6 +1554,70 @@ def _export_dataset_records_tool(arguments: dict[str, Any], context: ToolExecuti
     return json.dumps(payload, ensure_ascii=False, indent=2)
 
 
+def _export_training_jsonl_tool(arguments: dict[str, Any], context: ToolExecutionContext) -> str:
+    require_validation_ok = arguments.get('require_validation_ok', True)
+    if not isinstance(require_validation_ok, bool):
+        raise ToolExecutionError('require_validation_ok must be a boolean')
+    overwrite = arguments.get('overwrite', True)
+    if not isinstance(overwrite, bool):
+        raise ToolExecutionError('overwrite must be a boolean')
+    system_prompt = arguments.get('system_prompt', '你是小爱同学，中文智能语音助手。')
+    if not isinstance(system_prompt, str):
+        raise ToolExecutionError('system_prompt must be a string')
+    try:
+        records = _records_from_arguments(arguments, context)
+        payload = export_training_jsonl(
+            records,
+            root=str(context.root),
+            output_path=_data_agent_output_path(
+                _optional_string(arguments, 'output_path') or 'output/training.jsonl',
+                context,
+                canonical_filename='training.jsonl',
+            ),
+            session_num=_coerce_int(arguments, 'session_num', 5),
+            session_time_minutes=_coerce_int(arguments, 'session_time_minutes', 5),
+            context_fields=_optional_string_list(arguments, 'context_fields'),
+            system_prompt=system_prompt,
+            require_validation_ok=require_validation_ok,
+            overwrite=overwrite,
+        )
+    except (DataRecordError, json.JSONDecodeError) as exc:
+        raise ToolExecutionError(str(exc)) from exc
+    return json.dumps(payload, ensure_ascii=False, indent=2)
+
+
+def _export_planning_eval_csv_tool(arguments: dict[str, Any], context: ToolExecutionContext) -> str:
+    require_validation_ok = arguments.get('require_validation_ok', True)
+    if not isinstance(require_validation_ok, bool):
+        raise ToolExecutionError('require_validation_ok must be a boolean')
+    overwrite = arguments.get('overwrite', True)
+    if not isinstance(overwrite, bool):
+        raise ToolExecutionError('overwrite must be a boolean')
+    complex_default = arguments.get('complex_default', False)
+    if not isinstance(complex_default, bool):
+        raise ToolExecutionError('complex_default must be a boolean')
+    try:
+        records = _records_from_arguments(arguments, context)
+        payload = export_planning_eval_csv(
+            records,
+            root=str(context.root),
+            output_path=_data_agent_output_path(
+                _optional_string(arguments, 'output_path') or 'output/eval_planning.csv',
+                context,
+                canonical_filename='eval_planning.csv',
+            ),
+            session_num=_coerce_int(arguments, 'session_num', 5),
+            session_time_minutes=_coerce_int(arguments, 'session_time_minutes', 5),
+            context_fields=_optional_string_list(arguments, 'context_fields'),
+            complex_default=complex_default,
+            require_validation_ok=require_validation_ok,
+            overwrite=overwrite,
+        )
+    except (DataRecordError, json.JSONDecodeError) as exc:
+        raise ToolExecutionError(str(exc)) from exc
+    return json.dumps(payload, ensure_ascii=False, indent=2)
+
+
 def _draft_text_from_arguments(arguments: dict[str, Any], context: ToolExecutionContext) -> str:
     draft_text = arguments.get('draft_text')
     draft_path = arguments.get('draft_path')

src/data_agent_records.py

@@ -18,6 +18,8 @@ from typing import Any
 
 DEFAULT_REQUEST_ID = 'aabbccdd'
 DEFAULT_TIMESTAMP_STEP_MS = 60_000
+DEFAULT_SYSTEM_PROMPT = '你是小爱同学，中文智能语音助手。'
+DEFAULT_CONTEXT_FIELDS = ('location', 'rag')
 DEFAULT_PLAN_STATE_FILE = '.port_sessions/data_agent_generation_plans.json'
 DEFAULT_GOAL_STATE_FILE = '.port_sessions/data_agent_generation_goals.json'
 
@@ -520,6 +522,100 @@ def export_dataset_records(
     return payload
 
 
+def export_training_jsonl(
+    records: list[dict[str, Any]],
+    *,
+    root: str,
+    output_path: str,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    system_prompt: str = DEFAULT_SYSTEM_PROMPT,
+    require_validation_ok: bool = True,
+    overwrite: bool = True,
+) -> dict[str, Any]:
+    """导出训练集 JSONL：system / instruction / output。"""
+
+    if session_num <= 0:
+        raise DataRecordError('session_num must be greater than 0')
+    if session_time_minutes <= 0:
+        raise DataRecordError('session_time_minutes must be greater than 0')
+    validation = validate_dataset_records(records)
+    if require_validation_ok and not validation['ok']:
+        raise DataRecordError(
+            f'records failed validation with {validation["error_count"]} errors'
+        )
+    path = _resolve_output_path(root, output_path)
+    if path.exists() and not overwrite:
+        raise DataRecordError(f'output_path already exists: {output_path}')
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fields = _normalize_context_fields(context_fields)
+    content = ''.join(
+        _training_jsonl_line(
+            record,
+            session_num=session_num,
+            session_time_minutes=session_time_minutes,
+            context_fields=fields,
+            system_prompt=system_prompt,
+        )
+        + '\n'
+        for record in records
+    )
+    path.write_text(content, encoding='utf-8')
+    return {
+        'output_path': str(path.relative_to(Path(root).resolve())),
+        'output_format': 'jsonl',
+        'record_count': len(records),
+        'bytes_written': len(content.encode('utf-8')),
+        'validation': validation,
+    }
+
+
+def export_planning_eval_csv(
+    records: list[dict[str, Any]],
+    *,
+    root: str,
+    output_path: str,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    complex_default: bool = False,
+    require_validation_ok: bool = True,
+    overwrite: bool = True,
+) -> dict[str, Any]:
+    """导出评测 CSV：request_id / newPrompt / query / 类别真实标签 / code标签 / complex。"""
+
+    if session_num <= 0:
+        raise DataRecordError('session_num must be greater than 0')
+    if session_time_minutes <= 0:
+        raise DataRecordError('session_time_minutes must be greater than 0')
+    validation = validate_dataset_records(records)
+    if require_validation_ok and not validation['ok']:
+        raise DataRecordError(
+            f'records failed validation with {validation["error_count"]} errors'
+        )
+    path = _resolve_output_path(root, output_path)
+    if path.exists() and not overwrite:
+        raise DataRecordError(f'output_path already exists: {output_path}')
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fields = _normalize_context_fields(context_fields)
+    content = render_planning_eval_csv(
+        records,
+        session_num=session_num,
+        session_time_minutes=session_time_minutes,
+        context_fields=fields,
+        complex_default=complex_default,
+    )
+    path.write_text(content, encoding='utf-8-sig')
+    return {
+        'output_path': str(path.relative_to(Path(root).resolve())),
+        'output_format': 'csv',
+        'record_count': len(records),
+        'bytes_written': len(content.encode('utf-8-sig')),
+        'validation': validation,
+    }
+
+
 def render_dataset_records_table_csv(records: list[dict[str, Any]]) -> str:
     """把 canonical records 转成同事流转用的表格 CSV。"""
 
@@ -531,6 +627,185 @@ def render_dataset_records_table_csv(records: list[dict[str, Any]]) -> str:
     return output.getvalue()
 
 
+def render_planning_eval_csv(
+    records: list[dict[str, Any]],
+    *,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    complex_default: bool = False,
+) -> str:
+    """把 canonical records 转成含 newPrompt 的评测 CSV。"""
+
+    fields = _normalize_context_fields(context_fields)
+    output = StringIO()
+    writer = csv.DictWriter(
+        output,
+        fieldnames=['request_id', 'newPrompt', 'query', '类别真实标签', 'code标签', 'complex'],
+        lineterminator='\n',
+    )
+    writer.writeheader()
+    for record in records:
+        source = record.get('source') if isinstance(record.get('source'), dict) else {}
+        turn = record.get('turn') if isinstance(record.get('turn'), dict) else {}
+        label = record.get('label') if isinstance(record.get('label'), dict) else {}
+        target = str(label.get('target') or '')
+        writer.writerow(
+            {
+                'request_id': str(source.get('request_id') or ''),
+                'newPrompt': build_training_instruction(
+                    record,
+                    session_num=session_num,
+                    session_time_minutes=session_time_minutes,
+                    context_fields=fields,
+                ),
+                'query': str(turn.get('query') or ''),
+                '类别真实标签': _category_label_from_target(target),
+                'code标签': target,
+                'complex': 'true' if complex_default else 'false',
+            }
+        )
+    return output.getvalue()
+
+
+def build_training_instruction(
+    record: dict[str, Any],
+    *,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+) -> str:
+    turn = record.get('turn') if isinstance(record.get('turn'), dict) else {}
+    query = str(turn.get('query') or '')
+    context = record.get('context') if isinstance(record.get('context'), dict) else {}
+    prev_session = record.get('prev_session') if isinstance(record.get('prev_session'), list) else []
+    current_ts = _optional_int_for_export(turn.get('timestamp'))
+    if current_ts is None:
+        source = record.get('source') if isinstance(record.get('source'), dict) else {}
+        current_ts = _optional_int_for_export(source.get('timestamp'))
+    session, last_tts = _training_history(
+        prev_session,
+        current_ts=current_ts,
+        session_num=session_num,
+        session_time_minutes=session_time_minutes,
+    )
+    fields = _normalize_context_fields(context_fields)
+
+    instruction = '请参考用户的[当前query]、[对话历史]、[知识注入]、[系统状态]识别出[当前query]的[function]结果，[function]是python的code形式。\n'
+    instruction += '[知识注入]\n'
+    instruction += f'{_context_prompt_block(context, fields)}\n'
+    instruction += '[系统状态]\n'
+    instruction += '{}\n'
+    instruction += '[对话历史]\n'
+    if session:
+        history_parts = [f'用户: {session_query}' for session_query in session]
+        if last_tts is not None:
+            history_parts.append(f'小爱: {last_tts}')
+        instruction += '\n'.join(history_parts)
+        instruction += '\n'
+    instruction += '[当前query]\n'
+    instruction += f'用户: {query}\n'
+    instruction += '[function]\n'
+    return instruction
+
+
+def _training_jsonl_line(
+    record: dict[str, Any],
+    *,
+    session_num: int,
+    session_time_minutes: int,
+    context_fields: list[str],
+    system_prompt: str,
+) -> str:
+    label = record.get('label') if isinstance(record.get('label'), dict) else {}
+    payload = {
+        'system': system_prompt,
+        'instruction': build_training_instruction(
+            record,
+            session_num=session_num,
+            session_time_minutes=session_time_minutes,
+            context_fields=context_fields,
+        ),
+        'output': str(label.get('target') or ''),
+    }
+    return json.dumps(payload, ensure_ascii=False, separators=(',', ':'))
+
+
+def _training_history(
+    prev_session: list[Any],
+    *,
+    current_ts: int | None,
+    session_num: int,
+    session_time_minutes: int,
+) -> tuple[list[str], str | None]:
+    if current_ts is None:
+        return [], None
+    session_time_ms = session_time_minutes * 60 * 1000
+    valid_items: list[tuple[int, str, str]] = []
+    for item in prev_session:
+        if not isinstance(item, dict):
+            continue
+        ts = _optional_int_for_export(item.get('timestamp'))
+        query = str(item.get('query') or '')
+        tts = str(item.get('tts') or '')
+        if ts is not None and ts > 0 and query:
+            valid_items.append((ts, query, tts))
+    valid_items.sort(key=lambda item: item[0])
+
+    session: list[str] = []
+    last_tts: str | None = None
+    last_ts = current_ts
+    count = 0
+    for ts, query, tts in reversed(valid_items):
+        if last_ts - ts <= session_time_ms and last_ts >= ts:
+            session.append(query)
+            if count == 0:
+                last_tts = tts
+            last_ts = ts
+            count += 1
+            if count >= session_num:
+                break
+        else:
+            break
+    session.reverse()
+    return session, last_tts
+
+
+def _context_prompt_block(context: dict[str, Any], fields: list[str]) -> str:
+    lines = ['{']
+    for index, field in enumerate(fields):
+        comma = ',' if index < len(fields) - 1 else ''
+        value = str(context.get(field, ''))
+        lines.append(f'"{field}": {json.dumps(value, ensure_ascii=False)}{comma}')
+    lines.append('}')
+    return '\n'.join(lines)
+
+
+def _normalize_context_fields(context_fields: list[str] | None) -> list[str]:
+    fields = context_fields or list(DEFAULT_CONTEXT_FIELDS)
+    normalized = [field.strip() for field in fields if isinstance(field, str) and field.strip()]
+    return normalized or list(DEFAULT_CONTEXT_FIELDS)
+
+
+def _optional_int_for_export(value: Any) -> int | None:
+    if isinstance(value, bool) or value is None or value == '':
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _category_label_from_target(target: str) -> str:
+    agent_match = re.match(r'''^Agent\s*\(\s*tag\s*=\s*["']([^"']+)["']\s*\)$''', target.strip())
+    if agent_match:
+        return agent_match.group(1)
+    function_names = re.findall(r'\b([A-Za-z_][A-Za-z0-9_]*)\s*\(', target)
+    if function_names:
+        return function_names[-1]
+    return target
+
+
 def _dataset_table_columns() -> list[str]:
     return [
         'request_id',

tests/test_data_agent_records.py

@@ -12,11 +12,14 @@ from src.data_agent_records import (
     confirm_generation_goal,
     confirm_generation_plan,
     export_dataset_records,
+    export_planning_eval_csv,
+    export_training_jsonl,
     get_generation_plan,
     normalize_dataset_draft,
     prepare_generation_goal,
     prepare_generation_plan,
     render_dataset_records_table_csv,
+    render_planning_eval_csv,
     update_generation_plan,
     validate_dataset_records,
 )
@@ -179,6 +182,90 @@ target: Agent(tag='地图导航')
         self.assertEqual(prev_session[0]['timestamp'], '1755567870500')
         self.assertEqual(rows[0]['function'], 'Agent(tag="地图导航")')
 
+    def test_export_training_jsonl_uses_prompt_template_and_history(self) -> None:
+        records = normalize_dataset_draft(
+            '''
+# dataset_label: 地图导航边界数据
+### case: 多轮顺路停车场
+用户: 查一下附近停车场
+小爱: 找到了附近停车场
+用户: 帮我找个顺路的
+target: Agent(tag='地图导航')
+'''.strip(),
+            batch_id='demo',
+            base_timestamp=1_755_567_930_500,
+            timestamp_step_ms=60_000,
+        )['records']
+        records[0]['context'] = {'location': '北京', 'rag': '地图服务可用', 'other': '忽略'}
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            payload = export_training_jsonl(
+                records,
+                root=tmp_dir,
+                output_path='output/training.jsonl',
+            )
+            output_path = Path(tmp_dir) / payload['output_path']
+            line = json.loads(output_path.read_text(encoding='utf-8').splitlines()[0])
+
+        self.assertEqual(payload['output_format'], 'jsonl')
+        self.assertEqual(line['system'], '你是小爱同学，中文智能语音助手。')
+        self.assertEqual(line['output'], 'Agent(tag="地图导航")')
+        self.assertIn('[知识注入]\n{\n"location": "北京",\n"rag": "地图服务可用"\n}', line['instruction'])
+        self.assertIn('用户: 查一下附近停车场\n小爱: 找到了附近停车场', line['instruction'])
+        self.assertIn('[当前query]\n用户: 帮我找个顺路的', line['instruction'])
+
+    def test_render_planning_eval_csv_uses_expected_columns(self) -> None:
+        records = normalize_dataset_draft(
+            '''
+# dataset_label: 地图导航边界数据
+### case: 多轮顺路停车场
+用户: 查一下附近停车场
+小爱: 找到了附近停车场
+用户: 帮我找个顺路的
+target: Agent(tag='地图导航')
+'''.strip(),
+            batch_id='demo',
+            base_timestamp=1_755_567_930_500,
+        )['records']
+
+        rows = list(csv.DictReader(render_planning_eval_csv(records).splitlines()))
+
+        self.assertEqual(
+            list(rows[0].keys()),
+            ['request_id', 'newPrompt', 'query', '类别真实标签', 'code标签', 'complex'],
+        )
+        self.assertEqual(rows[0]['request_id'], 'aabbccdd')
+        self.assertEqual(rows[0]['query'], '帮我找个顺路的')
+        self.assertEqual(rows[0]['类别真实标签'], '地图导航')
+        self.assertEqual(rows[0]['code标签'], 'Agent(tag="地图导航")')
+        self.assertEqual(rows[0]['complex'], 'false')
+        self.assertIn('[function]', rows[0]['newPrompt'])
+
+    def test_export_planning_eval_csv_writes_file(self) -> None:
+        records = normalize_dataset_draft(
+            '''
+# dataset_label: 时间工具数据
+### case: 几点
+用户: 现在几点
+target: CalendarQA(type="TIME")
+'''.strip(),
+            batch_id='demo',
+            base_timestamp=1_755_567_930_500,
+        )['records']
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            payload = export_planning_eval_csv(
+                records,
+                root=tmp_dir,
+                output_path='output/eval_planning.csv',
+                complex_default=True,
+            )
+            output_path = Path(tmp_dir) / payload['output_path']
+            rows = list(csv.DictReader(output_path.read_text(encoding='utf-8-sig').splitlines()))
+
+        self.assertEqual(payload['output_format'], 'csv')
+        self.assertEqual(rows[0]['类别真实标签'], 'CalendarQA')
+        self.assertEqual(rows[0]['code标签'], 'CalendarQA(type="TIME")')
+        self.assertEqual(rows[0]['complex'], 'true')
+
     def test_normalize_online_draft_uses_real_request_metadata(self) -> None:
         records = normalize_dataset_draft(
             '''
@@ -641,11 +728,33 @@ target: Agent(tag='地图导航')
             self.assertTrue(export_result.ok, export_result.content)
             export_payload = json.loads(export_result.content)
             table_exists = (root / export_payload['table_output_path']).exists()
+            training_result = execute_tool(
+                default_tool_registry(),
+                'data_agent_export_training_jsonl',
+                {'records_path': str(records_path)},
+                context,
+            )
+            self.assertTrue(training_result.ok, training_result.content)
+            training_payload = json.loads(training_result.content)
+            training_exists = (root / training_payload['output_path']).exists()
+            eval_result = execute_tool(
+                default_tool_registry(),
+                'data_agent_export_planning_eval_csv',
+                {'records_path': str(records_path)},
+                context,
+            )
+            self.assertTrue(eval_result.ok, eval_result.content)
+            eval_payload = json.loads(eval_result.content)
+            eval_exists = (root / eval_payload['output_path']).exists()
 
         self.assertTrue(validate_result.ok, validate_result.content)
         self.assertTrue(json.loads(validate_result.content)['ok'])
         self.assertEqual(export_payload['record_count'], 1)
         self.assertTrue(table_exists)
+        self.assertEqual(training_payload['output_path'], '.port_sessions/data_agent_output/training.jsonl')
+        self.assertTrue(training_exists)
+        self.assertEqual(eval_payload['output_path'], '.port_sessions/data_agent_output/eval_planning.csv')
+        self.assertTrue(eval_exists)
 
     def test_tool_schemas_avoid_top_level_composition_keywords(self) -> None:
         # 部分 Bedrock/Anthropic 兼容后端不接受顶层 anyOf/oneOf/allOf。