Enhance model training lite workflow

.gitignore

@@ -26,6 +26,7 @@ router_session_parquet/
 # Environment files
 .env
 .env.*
+skills/*/.local/
 
 # Local benchmark outputs
 benchmark_artifacts/

skills/model-training-lite/SKILL.md

@@ -1,8 +1,8 @@
 ---
 name: model-training-lite
 name_zh: 轻量模型训练
-description: 从 Codex、Claude Code 或其他 Agent 通过用户提供的 Jupyter 环境和数据版本发起一次模型训练，聚焦“准备远端工作区、同步数据、提交 CML SFT、返回 JobID”，不包含完整自主迭代闭环。
-when_to_use: 当用户已经准备好训练数据，想让 Agent 使用 Jupyter/CloudML 发起一次模型训练、复用当前仓库的训练脚本和模板、或询问如何把模型训练流程沉淀成轻量 skill 时使用。
+description: 从 Codex、Claude Code 或其他 Agent 通过用户提供的 Jupyter 环境和数据版本发起一次模型训练，并可选发起评测和生成轻量结果摘要；不包含自动数据增强闭环。
+when_to_use: 当用户已经准备好训练数据，想让 Agent 使用 Jupyter/CloudML 基于某个数据分支或 commit 发起一次模型训练、训练后评测、汇总结果，或询问如何把模型训练流程沉淀成轻量 skill 时使用。
 aliases: jupyter-model-training, sft-submit, 轻量训练, 模型训练提交
 examples:
   - 用这个 Jupyter 和 ai-planning 当前分支重新发起训练
@@ -14,7 +14,17 @@ allowed_tools: read_file, write_file, grep_search, glob_search, ask_user_questio
 
 # 轻量模型训练
 
-本 skill 只负责一次训练提交：
+本 skill 是人机协同的训练/评测执行器：人负责目标和边界裁决，AI 负责候选、统计、证据整理和确定性执行。
+
+```text
+人提出目标问题
+  -> AI 整理候选、统计分布、生成 review 表
+  -> 人确认边界、保留样本、数据是否入库
+  -> AI 提交训练、发起评测、汇总结果
+  -> 人判断是否继续下一轮
+```
+
+训练评测执行链路：
 
 ```text
 Jupyter 授权环境
@@ -23,26 +33,53 @@ Jupyter 授权环境
   -> 同步训练脚本与数据版本
   -> 检查 CML / 数据 / zk_trainer
   -> 提交 SFT
-  -> 返回 JobID、产物路径、下一步评测入口
+  -> 可选：SFT 成功后提交 CML 评测
+  -> 可选：读取 workflow metric_diff 生成轻量结果摘要
+  -> 返回 JobID、workflow ID、产物路径、摘要路径
 ```
 
-不要把它扩展成 `model-iteration` 那种完整“baseline -> 分析 -> 增强 -> 训练 -> 评测 -> 再分析”的自主循环。
+不要把它扩展成 `model-iteration` 那种完整“baseline -> 分析 -> 增强 -> 训练 -> 评测 -> 再分析”的自主循环。本 skill 可以辅助人完成每一步决策前的信息准备，但不自动裁决边界、不自动修改数据、不自动生成增强样本、不自动进入下一轮训练。
 
 ## 必要输入
 
 开始前必须拿到：
 
-- **Jupyter 地址和授权方式**：URL；密码、token、cookie/session，或说明当前环境已有可用登录态。
-- **数据版本**：git repo + branch + commit，或远端工作区中已存在的数据路径。
+- **Jupyter 地址和授权方式**：URL；密码、token、cookie/session，或使用本地默认配置。
+- **数据版本**：git repo + branch + commit，或 git repo + branch，或远端工作区中已存在的数据路径。
 - **训练配方**：如果是当前 ZK 中控 SFT，默认复用 `skills/model-iteration/scripts/`；如果不是，必须让用户提供训练脚本、模板或命令。
 
 通常用户只需要显式给 `Jupyter 地址` 和 `数据版本`。当前项目的默认训练配方可从本仓库继承，不必每次追问。
 
+本地默认 Jupyter 配置路径：
+
+```text
+skills/model-training-lite/.local/jupyter_defaults.json
+```
+
+`.local/` 已加入 gitignore。真实密码只能放在本地配置或环境变量里，不要写入 `SKILL.md`、README、提交记录或最终回复。
+
+配置格式可参考：
+
+```text
+skills/model-training-lite/jupyter_defaults.example.json
+```
+
+脚本也支持环境变量：
+
+```text
+MODEL_TRAINING_LITE_JUPYTER_URL
+MODEL_TRAINING_LITE_JUPYTER_PASSWORD
+MODEL_TRAINING_LITE_JUPYTER_TOKEN
+MODEL_TRAINING_LITE_JUPYTER_COOKIE
+MODEL_TRAINING_LITE_JUPYTER_AUTH_TYPE
+```
+
 可选但推荐确认：
 
 - `owner`：远端输出目录使用的用户前缀，例如 `wuyang6`。
 - `run_name`：本次训练工作区名，例如 `manual_YYYYMMDD_zk_intent_xxx`。
-- 是否训练后立刻发起评测。默认只提交训练，不自动串接评测。
+- 是否训练后立刻发起评测。默认只提交训练；用户要求“训练后评测/看效果/分析结果”时启用。
+- `model_old`：评测工作流中的对照模型路径；未提供时使用默认线上基线。
 
 ## 默认 ZK SFT 配方
 
@@ -62,6 +99,22 @@ skills/model-iteration/assets/config.yaml
 
 ## 推荐流程
 
+### 0. 人机协同边界
+
+AI 应主动辅助人完成信息准备，但不能替人拍板：
+
+| 阶段 | AI 负责 | 人负责 |
+|---|---|---|
+| 目标定义 | 把问题拆成可评测口径，列出候选集合和风险 | 确认目标问题和主指标 |
+| 数据准备 | 调用相关 skill 生成候选、统计、review 表 | 判断边界样本是否保留 |
+| 数据构造 | 参考 `product-data` 生成训练/评测格式 | 确认数据是否入库 |
+| 标签判断 | 参考 `label-master` 校验 tag / function / complex | 确认争议规则 |
+| 线上挖掘 | 参考 `online-mining-v2` 获取 rid/session/prompt/output | 确认挖掘口径 |
+| 批量打标 | 参考 `model-labeling` 请求线上模型，生成基线 | 确认宽口径和人工 override |
+| 训练评测 | 本 skill 提交 SFT、评测、轻量摘要 | 决定是否继续下一轮 |
+
+需要全自动假设驱动迭代时，使用 `model-iteration`，不要把本 skill 临时扩展成自动闭环。
+
 ### 1. 先生成提交计划
 
 优先执行 portable script：
@@ -80,12 +133,16 @@ python skills/model-training-lite/scripts/render_sft_submission_plan.py --input
   "data_commit": "b92be709",
   "owner": "wuyang6",
   "run_name": "manual_20260528_zk_intent_clean_train",
-  "recipe": "zk_sft"
+  "recipe": "zk_sft",
+  "run_eval": true,
+  "model_old": "/mnt/wangsenhao/verl_zk/qwen4b_cispo_wokl_add_bvt_2/global_step_5/actor/huggingface"
 }
 ```
 
 脚本只生成计划和命令草案，不连接 Jupyter，不提交训练。
 
+如果只提供 `data_branch` 不提供 `data_commit`，脚本会生成 `git reset --hard origin/<branch>`，并在结果中写 warning。正式训练报告里必须记录实际 `git rev-parse HEAD`，避免后续无法复现。
+
 ### 2. 连接 Jupyter
 
 如果已经有 session/cookie，可直接调用 Jupyter REST API：
@@ -117,6 +174,12 @@ zk_trainer/
 
 把默认 ZK SFT 配方里的 `scripts/` 和 `config.yaml` 同步到远端工作区。再 clone 或更新数据仓库，并 checkout 到用户指定 commit。
 
+除 `model-iteration` 默认脚本外，还要同步本 skill 的轻量分析脚本：
+
+```text
+skills/model-training-lite/scripts/summarize_eval_result.py
+```
+
 ### 4. 提交前检查
 
 提交训练前必须输出：
@@ -158,10 +221,49 @@ eval "$(./scripts/resolve_run_ids.sh)"
 
 再查一次 `cml custom_train describe <JOB_ID>`，确认状态不是提交后立即失败。
 
+### 7. 可选：训练后提交评测
+
+只有用户要求“训练后评测/看效果/分析结果”时执行。
+
+训练成功标记存在后：
+
+```bash
+source ~/.cloudml-cli/.profile
+export AUTORESEARCH_CHAT_ROOT=<remote_workspace>
+export AUTORESEARCH_ROOT="$AUTORESEARCH_CHAT_ROOT"
+cd "$AUTORESEARCH_CHAT_ROOT"
+eval "$(./scripts/resolve_run_ids.sh)"
+./scripts/submit_cml_eval.sh "$EVAL_RUNDIC" "$AUTORESEARCH_CHAT_ROOT/sft_output" "<model_old>"
+```
+
+提交后必须回报：
+
+- `EVAL_RUNDIC`
+- CloudML workflow 提交输出
+- 评测产物目录：`/mnt/xiaoai-zk-model-train-tj5/workflow5/workflow<EVAL_RUNDIC>/`
+- 评测完成标记：`metric_diff/lark_template.json`
+
+### 8. 可选：轻量结果摘要
+
+评测产物落盘后执行：
+
+```bash
+python "$AUTORESEARCH_CHAT_ROOT/scripts/summarize_eval_result.py" \
+  --workflow-root /mnt/xiaoai-zk-model-train-tj5/workflow5 \
+  --run-dic "$EVAL_RUNDIC" \
+  --output "$AUTORESEARCH_CHAT_ROOT/results/eval_summary_${EVAL_RUNDIC}.md"
+```
+
+摘要脚本只做轻量汇总：
+
+- 从 `metric_diff/lark_template.json` 尽力抽取指标。
+- 从 `metric_diff/specific_comparison.csv` 汇总错误分布和错误样例。
+- 不做数据增强建议，不做训练集修改，不替代 `model-iteration` 的深度归因。
+
 ## 边界
 
 - 不自动修改训练数据。
-- 不自动做错误归因。
+- 不自动做深度错误归因；只允许生成轻量指标/错误分布摘要。
 - 不自动生成增强样本。
 - 不默认串接评测；训练成功后是否评测由用户或后续明确指令决定。
 - 不把 Jupyter 密码、CloudML key、cookie 写进产物或最终回复。

skills/model-training-lite/jupyter_defaults.example.json

@@ -0,0 +1,5 @@
+{
+  "jupyter_url": "https://example-jupyter/lab?",
+  "auth_type": "password",
+  "password": "DO_NOT_COMMIT_REAL_PASSWORD"
+}

skills/model-training-lite/references/zk_sft_defaults.md

@@ -11,6 +11,7 @@ skills/model-iteration/scripts/submit_sft.sh
 skills/model-iteration/scripts/submit_cml_eval.sh
 skills/model-iteration/scripts/sft_train_job.yaml.tpl
 skills/model-iteration/assets/config.yaml
+skills/model-training-lite/scripts/summarize_eval_result.py
 ```
 
 ## 默认基模
@@ -28,6 +29,14 @@ git@git.n.xiaomi.com:ai-service/ai-planning.git
 branch: autoresearch-v1
 ```
 
+如果用户只提供数据分支、不提供 commit，训练前必须在远端工作区记录：
+
+```bash
+git -C ai-planning rev-parse HEAD
+```
+
+正式报告使用该 commit 作为本次训练的数据版本。
+
 训练脚本会扫描：
 
 ```text
@@ -49,6 +58,22 @@ ai-planning/data/train_set/*/*.jsonl
 <workspace>/sft_output/_SUCCESS
 ```
 
+## 默认 Jupyter 配置
+
+本地默认配置文件：
+
+```text
+skills/model-training-lite/.local/jupyter_defaults.json
+```
+
+`.local/` 不提交。配置优先级：
+
+```text
+脚本输入 > 环境变量 > .local/jupyter_defaults.json
+```
+
+脚本输出只显示认证是否已配置，不回显密码、token 或 cookie。
+
 ## CML 环境
 
 CloudML CLI 通常位于：
@@ -71,6 +96,37 @@ cml config show
 cml custom_train describe <JOB_ID>
 ```
 
+## 默认评测
+
+训练后评测复用：
+
+```text
+skills/model-iteration/scripts/submit_cml_eval.sh
+skills/model-iteration/assets/config.yaml
+```
+
+默认 workflow 配置从 `config.yaml` 读取：
+
+```text
+cml_eval.workflow_id
+cml_eval.version
+```
+
+评测产物目录：
+
+```text
+/mnt/xiaoai-zk-model-train-tj5/workflow5/workflow<EVAL_RUNDIC>/metric_diff/
+```
+
+轻量摘要脚本：
+
+```bash
+python scripts/summarize_eval_result.py \
+  --workflow-root /mnt/xiaoai-zk-model-train-tj5/workflow5 \
+  --run-dic "$EVAL_RUNDIC" \
+  --output results/eval_summary_${EVAL_RUNDIC}.md
+```
+
 ## 常见坑
 
 - Jupyter POST 请求缺少 `_xsrf`：从 cookie 取 `_xsrf`，请求头带 `X-XSRFToken`。
@@ -78,3 +134,5 @@ cml custom_train describe <JOB_ID>
 - `which cml` 为空：先 `source ~/.cloudml-cli/.profile`。
 - `resolve_run_ids.sh` 没有 `RUNDIC`：只使用 `SFT_RUNDIC` / `EVAL_RUNDIC`。
 - git clone 看似卡住：先检查目标目录是否已经完整、是否存在残留进程，不要重复提交训练。
+- 只给 branch 不给 commit：可训练，但必须回填实际 commit，否则结果不可复现。
+- 训练和评测不要放在同一个长 bash 里串到底；SFT `_SUCCESS` 落盘后再单独提交评测。

skills/model-training-lite/scripts/render_sft_submission_plan.py

@@ -3,8 +3,10 @@ from __future__ import annotations
 
 import argparse
 import json
+import os
 import re
 import sys
+from pathlib import Path
 from pathlib import PurePosixPath
 from typing import Any
 
@@ -26,14 +28,18 @@ def main() -> int:
 
 
 def render_plan(payload: dict[str, Any]) -> dict[str, Any]:
+    defaults = load_local_defaults()
     missing: list[str] = []
-    jupyter_url = clean(payload.get("jupyter_url"))
+    jupyter_url = clean(payload.get("jupyter_url")) or clean(defaults.get("jupyter_url"))
+    jupyter_auth_type = clean(payload.get("jupyter_auth_type") or payload.get("auth_type")) or clean(defaults.get("auth_type"))
+    jupyter_password = clean(payload.get("jupyter_password") or payload.get("password")) or clean(defaults.get("password"))
+    jupyter_token = clean(payload.get("jupyter_token") or payload.get("token")) or clean(defaults.get("token"))
     data_commit = clean(payload.get("data_commit") or payload.get("commit"))
     data_path = clean(payload.get("data_path"))
     if not jupyter_url:
         missing.append("jupyter_url")
-    if not data_commit and not data_path:
-        missing.append("data_commit or data_path")
+    if jupyter_url and not (jupyter_password or jupyter_token or clean(payload.get("jupyter_cookie")) or clean(defaults.get("cookie"))):
+        missing.append("jupyter auth")
 
     owner = clean(payload.get("owner")) or "wuyang6"
     run_name = clean(payload.get("run_name")) or default_run_name(data_commit or data_path or "manual")
@@ -43,6 +49,12 @@ def render_plan(payload: dict[str, Any]) -> dict[str, Any]:
     data_repo = clean(payload.get("data_repo")) or DEFAULT_DATA_REPO
     data_branch = clean(payload.get("data_branch")) or DEFAULT_BRANCH
     recipe = clean(payload.get("recipe")) or DEFAULT_RECIPE
+    run_eval = bool(payload.get("run_eval") or payload.get("eval_after_train"))
+    model_old = clean(payload.get("model_old")) or "/mnt/wangsenhao/verl_zk/qwen4b_cispo_wokl_add_bvt_2/global_step_5/actor/huggingface"
+    wf_root = clean(payload.get("workflow_root")) or "/mnt/xiaoai-zk-model-train-tj5/workflow5"
+    warnings: list[str] = []
+    if not data_commit and not data_path:
+        warnings.append("data_commit 未提供：计划将使用 data_branch 当前 HEAD，复现性弱于固定 commit。")
 
     commands = {
         "prepare_workspace": [
@@ -53,7 +65,11 @@ def render_plan(payload: dict[str, Any]) -> dict[str, Any]:
             f"[ -d ai-planning/.git ] || git clone -b {data_branch} {data_repo} ai-planning",
             f"git -C ai-planning fetch origin {data_branch}",
             f"git -C ai-planning checkout {data_branch}",
-            f"git -C ai-planning reset --hard {data_commit}" if data_commit else f"# use existing data_path: {data_path}",
+            f"git -C ai-planning reset --hard {data_commit}" if data_commit else (
+                f"# use existing data_path: {data_path}" if data_path else
+                f"git -C ai-planning reset --hard origin/{data_branch}"
+            ),
+            "git -C ai-planning rev-parse HEAD",
         ],
         "submit_sft": [
             "source ~/.cloudml-cli/.profile",
@@ -67,21 +83,42 @@ def render_plan(payload: dict[str, Any]) -> dict[str, Any]:
             "cml custom_train describe <JOB_ID>",
             f"test -f {workspace}/sft_output/_SUCCESS",
         ],
+        "submit_eval": [
+            "source ~/.cloudml-cli/.profile",
+            f"export AUTORESEARCH_CHAT_ROOT={workspace}",
+            'export AUTORESEARCH_ROOT="$AUTORESEARCH_CHAT_ROOT"',
+            f"export WF_ROOT={wf_root}",
+            f"cd {workspace}",
+            'eval "$(./scripts/resolve_run_ids.sh)"',
+            f'./scripts/submit_cml_eval.sh "$EVAL_RUNDIC" "{workspace}/sft_output" "{model_old}"',
+        ],
+        "analyze_eval": [
+            f"python {workspace}/scripts/summarize_eval_result.py --workflow-root {wf_root} --run-dic <EVAL_RUNDIC> --output {workspace}/results/eval_summary_<EVAL_RUNDIC>.md",
+        ],
     }
     return {
         "ok": not missing,
         "missing": missing,
         "plan": {
             "jupyter_url": jupyter_url,
+            "jupyter_auth": {
+                "type": jupyter_auth_type or ("token" if jupyter_token else "password" if jupyter_password else ""),
+                "configured": bool(jupyter_password or jupyter_token or clean(payload.get("jupyter_cookie")) or clean(defaults.get("cookie"))),
+                "source": auth_source(payload, defaults),
+            },
             "recipe": recipe,
             "workspace": workspace,
             "data_repo": data_repo,
             "data_branch": data_branch,
             "data_commit": data_commit,
             "data_path": data_path,
+            "run_eval": run_eval,
+            "model_old": model_old,
+            "workflow_root": wf_root,
             "success_marker": f"{workspace}/sft_output/_SUCCESS",
         },
         "commands": commands,
+        "warnings": warnings,
         "required_outputs": [
             "CloudML JobID",
             "CloudML task URL",
@@ -89,6 +126,8 @@ def render_plan(payload: dict[str, Any]) -> dict[str, Any]:
             "EVAL_RUNDIC",
             "workspace",
             "success_marker",
+            "eval workflow id when run_eval=true",
+            "eval summary path when analysis is requested",
         ],
     }
 
@@ -97,6 +136,35 @@ def clean(value: Any) -> str:
     return str(value or "").strip()
 
 
+def load_local_defaults() -> dict[str, Any]:
+    defaults: dict[str, Any] = {}
+    env_defaults = {
+        "jupyter_url": os.environ.get("MODEL_TRAINING_LITE_JUPYTER_URL"),
+        "password": os.environ.get("MODEL_TRAINING_LITE_JUPYTER_PASSWORD"),
+        "token": os.environ.get("MODEL_TRAINING_LITE_JUPYTER_TOKEN"),
+        "cookie": os.environ.get("MODEL_TRAINING_LITE_JUPYTER_COOKIE"),
+        "auth_type": os.environ.get("MODEL_TRAINING_LITE_JUPYTER_AUTH_TYPE"),
+    }
+    defaults.update({key: value for key, value in env_defaults.items() if value})
+    config_path = Path(__file__).resolve().parents[1] / ".local" / "jupyter_defaults.json"
+    if config_path.exists():
+        try:
+            file_defaults = json.loads(config_path.read_text(encoding="utf-8"))
+            if isinstance(file_defaults, dict):
+                defaults.update({key: value for key, value in file_defaults.items() if value})
+        except json.JSONDecodeError as exc:
+            raise SystemExit(f"invalid local defaults JSON: {config_path}: {exc}") from exc
+    return defaults
+
+
+def auth_source(payload: dict[str, Any], defaults: dict[str, Any]) -> str:
+    if any(clean(payload.get(key)) for key in ("jupyter_password", "password", "jupyter_token", "token", "jupyter_cookie")):
+        return "input"
+    if any(clean(defaults.get(key)) for key in ("password", "token", "cookie")):
+        return "local_default"
+    return ""
+
+
 def default_run_name(seed: str) -> str:
     slug = re.sub(r"[^a-zA-Z0-9_-]+", "_", seed).strip("_")[:32] or "manual"
     return f"manual_sft_{slug}"

skills/model-training-lite/scripts/summarize_eval_result.py

@@ -0,0 +1,236 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+from collections import Counter
+from pathlib import Path
+from typing import Any
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Summarize a CML eval workflow metric_diff directory.")
+    parser.add_argument("--workflow-root", default="/mnt/xiaoai-zk-model-train-tj5/workflow5")
+    parser.add_argument("--run-dic", required=True, help="workflow runDic, for example 18065")
+    parser.add_argument("--output", help="Markdown output path. Prints to stdout when omitted.")
+    parser.add_argument("--max-error-examples", type=int, default=30)
+    args = parser.parse_args()
+
+    workflow_dir = Path(args.workflow_root) / f"workflow{args.run_dic}"
+    metric_dir = workflow_dir / "metric_diff"
+    result = summarize(metric_dir, args.max_error_examples)
+    text = render_markdown(args.run_dic, workflow_dir, result)
+    if args.output:
+        out = Path(args.output)
+        out.parent.mkdir(parents=True, exist_ok=True)
+        out.write_text(text, encoding="utf-8")
+    else:
+        print(text)
+    return 0 if result["ok"] else 1
+
+
+def summarize(metric_dir: Path, max_error_examples: int) -> dict[str, Any]:
+    lark_path = metric_dir / "lark_template.json"
+    comparison_path = metric_dir / "specific_comparison.csv"
+    result: dict[str, Any] = {
+        "ok": True,
+        "metric_dir": str(metric_dir),
+        "missing": [],
+        "metrics": [],
+        "specific_rows": 0,
+        "error_summary": [],
+        "error_examples": [],
+    }
+    if not lark_path.exists():
+        result["missing"].append(str(lark_path))
+    else:
+        result["metrics"] = extract_lark_metrics(load_json(lark_path))
+    if comparison_path.exists():
+        rows = read_csv(comparison_path)
+        result["specific_rows"] = len(rows)
+        result["error_summary"] = summarize_comparison(rows)
+        result["error_examples"] = select_error_examples(rows, max_error_examples)
+    else:
+        result["missing"].append(str(comparison_path))
+    result["ok"] = not result["missing"]
+    return result
+
+
+def load_json(path: Path) -> Any:
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def extract_lark_metrics(obj: Any) -> list[dict[str, Any]]:
+    metrics: list[dict[str, Any]] = []
+
+    def walk(value: Any, path: list[str]) -> None:
+        if isinstance(value, dict):
+            maybe_name = value.get("name") or value.get("title") or value.get("sub_cate") or value.get("cate")
+            maybe_acc = first_present(value, ["acc", "accuracy", "new_acc", "dev_acc", "rate", "pass_rate"])
+            maybe_total = first_present(value, ["total", "count", "all"])
+            maybe_right = first_present(value, ["right", "correct", "hit"])
+            if maybe_acc is not None or (maybe_total is not None and maybe_right is not None):
+                metrics.append(
+                    {
+                        "path": " / ".join(path + ([str(maybe_name)] if maybe_name else [])),
+                        "acc": maybe_acc,
+                        "right": maybe_right,
+                        "total": maybe_total,
+                    }
+                )
+            for key, child in value.items():
+                if isinstance(child, (dict, list)):
+                    walk(child, path + [str(key)])
+        elif isinstance(value, list):
+            for idx, child in enumerate(value):
+                if isinstance(child, (dict, list)):
+                    walk(child, path + [str(idx)])
+
+    walk(obj, [])
+    # De-duplicate while preserving order.
+    seen: set[tuple[str, str, str, str]] = set()
+    uniq: list[dict[str, Any]] = []
+    for item in metrics:
+        key = tuple(str(item.get(k, "")) for k in ("path", "acc", "right", "total"))
+        if key not in seen:
+            seen.add(key)
+            uniq.append(item)
+    return uniq[:80]
+
+
+def first_present(obj: dict[str, Any], keys: list[str]) -> Any:
+    for key in keys:
+        if key in obj and obj[key] not in ("", None):
+            return obj[key]
+    return None
+
+
+def read_csv(path: Path) -> list[dict[str, str]]:
+    for encoding in ("utf-8-sig", "utf-8", "gb18030"):
+        try:
+            with path.open(encoding=encoding, newline="") as handle:
+                return list(csv.DictReader(handle))
+        except UnicodeDecodeError:
+            continue
+    raise UnicodeDecodeError("csv", b"", 0, 1, f"cannot decode {path}")
+
+
+def summarize_comparison(rows: list[dict[str, str]]) -> list[dict[str, Any]]:
+    if not rows:
+        return []
+    fieldnames = set(rows[0])
+    cate_field = pick_field(fieldnames, ["sub_cate", "子集", "cate", "类别"])
+    gold_field = pick_field(fieldnames, ["code_label", "gold", "label", "类别真实标签", "code标签"])
+    pred_field = pick_field(fieldnames, ["origin_predict_dev", "predict_dev", "prediction", "pred", "模型输出"])
+    if not cate_field:
+        cate_field = "__all__"
+    counter: Counter[tuple[str, str, str]] = Counter()
+    for row in rows:
+        cate = row.get(cate_field, "ALL") if cate_field != "__all__" else "ALL"
+        gold = row.get(gold_field, "") if gold_field else ""
+        pred = row.get(pred_field, "") if pred_field else ""
+        is_error = detect_error(row)
+        if is_error:
+            counter[(cate, gold[:80], pred[:80])] += 1
+    return [
+        {"sub_cate": cate, "gold": gold, "pred": pred, "count": count}
+        for (cate, gold, pred), count in counter.most_common(30)
+    ]
+
+
+def detect_error(row: dict[str, str]) -> bool:
+    for key in ("is_correct", "correct", "是否正确", "same", "is_same"):
+        value = row.get(key)
+        if value is None:
+            continue
+        text = str(value).strip().lower()
+        if text in {"false", "0", "错", "错误", "no", "n"}:
+            return True
+        if text in {"true", "1", "对", "正确", "yes", "y"}:
+            return False
+    base = row.get("origin_predict_base") or row.get("predict_base") or ""
+    dev = row.get("origin_predict_dev") or row.get("predict_dev") or row.get("prediction") or ""
+    gold = row.get("code_label") or row.get("gold") or row.get("code标签") or ""
+    return bool(gold and dev and normalize_label(gold) != normalize_label(dev)) or bool(base and dev and base != dev)
+
+
+def normalize_label(text: str) -> str:
+    return "".join(str(text).split()).replace("'", '"')
+
+
+def select_error_examples(rows: list[dict[str, str]], limit: int) -> list[dict[str, str]]:
+    examples: list[dict[str, str]] = []
+    for row in rows:
+        if not detect_error(row):
+            continue
+        examples.append(
+            {
+                "query": row.get("query") or row.get("当前query") or row.get("current_query") or "",
+                "sub_cate": row.get("sub_cate") or row.get("子集") or "",
+                "gold": row.get("code_label") or row.get("gold") or row.get("code标签") or "",
+                "pred": row.get("origin_predict_dev") or row.get("predict_dev") or row.get("prediction") or "",
+            }
+        )
+        if len(examples) >= limit:
+            break
+    return examples
+
+
+def pick_field(fieldnames: set[str], candidates: list[str]) -> str:
+    for candidate in candidates:
+        if candidate in fieldnames:
+            return candidate
+    return ""
+
+
+def render_markdown(run_dic: str, workflow_dir: Path, result: dict[str, Any]) -> str:
+    lines: list[str] = [
+        f"# workflow{run_dic} 评测摘要",
+        "",
+        f"- workflow_dir: `{workflow_dir}`",
+        f"- metric_dir: `{result['metric_dir']}`",
+    ]
+    if result["missing"]:
+        lines.append(f"- missing: `{', '.join(result['missing'])}`")
+    lines += ["", "## 指标摘录", ""]
+    if result["metrics"]:
+        lines.append("| path | acc | right | total |")
+        lines.append("|---|---:|---:|---:|")
+        for item in result["metrics"]:
+            lines.append(
+                f"| {safe_cell(item.get('path'))} | {safe_cell(item.get('acc'))} | {safe_cell(item.get('right'))} | {safe_cell(item.get('total'))} |"
+            )
+    else:
+        lines.append("未解析到 lark_template 指标。")
+    lines += ["", "## 错误分布 Top", ""]
+    if result["error_summary"]:
+        lines.append("| sub_cate | gold | pred | count |")
+        lines.append("|---|---|---|---:|")
+        for item in result["error_summary"]:
+            lines.append(
+                f"| {safe_cell(item['sub_cate'])} | {safe_cell(item['gold'])} | {safe_cell(item['pred'])} | {item['count']} |"
+            )
+    else:
+        lines.append("未解析到错误分布。")
+    lines += ["", "## 错误样例", ""]
+    if result["error_examples"]:
+        lines.append("| sub_cate | query | gold | pred |")
+        lines.append("|---|---|---|---|")
+        for item in result["error_examples"]:
+            lines.append(
+                f"| {safe_cell(item['sub_cate'])} | {safe_cell(item['query'])} | {safe_cell(item['gold'])} | {safe_cell(item['pred'])} |"
+            )
+    else:
+        lines.append("未抽到错误样例。")
+    lines.append("")
+    return "\n".join(lines)
+
+
+def safe_cell(value: Any) -> str:
+    text = str(value or "").replace("\n", "<br>").replace("|", "\\|")
+    return text[:500]
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())