refactor: split submit_sft_via_cml.sh into submit_sft.sh + submit_cml_eval.sh

- submit_sft.sh: pure SFT submission, prints JobID and exits (no embedded watcher)
- submit_cml_eval.sh: pure CML eval, reads workflow_id/version from config.yaml
- jupyter_runtime write_text: rewrite to use Contents API instead of terminal websocket
- SKILL.md: restrict trigger to explicit UI click only
- program.md: update §5.2 docs to reflect two-script workflow

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

skills/model-iteration/scripts/submit_cml_eval.sh

@@ -0,0 +1,76 @@
+#!/bin/bash
+# 起 CML evaluation workflow（**只做这一件事**）。
+# workflow_id / version 都从 skills/model-iteration/assets/config.yaml 读，不在这里硬编码。
+#
+# 用法：
+#   ./submit_cml_eval.sh <EVAL_RUNDIC> [MODEL_NEW] [MODEL_OLD]
+#
+# 默认值：
+#   MODEL_NEW = $AUTORESEARCH_ROOT/sft_output（最新一轮 SFT 产物）
+#   MODEL_OLD = /mnt/wangsenhao/verl_zk/qwen4b_cispo_wokl_add_bvt_2/global_step_5/actor/huggingface
+
+set -euo pipefail
+
+EVAL_RUNDIC=${1:?usage: $0 <EVAL_RUNDIC> [MODEL_NEW] [MODEL_OLD]}
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT=${AUTORESEARCH_ROOT:-/mnt/wangsenhao/autoresearch-zk}
+MODEL_NEW=${2:-${MODEL_NEW:-$ROOT/sft_output}}
+MODEL_OLD=${3:-${MODEL_OLD:-/mnt/wangsenhao/verl_zk/qwen4b_cispo_wokl_add_bvt_2/global_step_5/actor/huggingface}}
+
+# 定位 config.yaml：先看脚本旁边的 ../assets/，再看 skills/ 树
+find_config() {
+  local candidates=(
+    "$SCRIPT_DIR/../assets/config.yaml"
+    "${ZK_AGENT_SKILLS_ROOT:-}/model-iteration/assets/config.yaml"
+    "${AUTORESEARCH_CHAT_ROOT:-}/skills/model-iteration/assets/config.yaml"
+  )
+  local c
+  for c in "${candidates[@]}"; do
+    if [ -n "$c" ] && [ -f "$c" ]; then
+      echo "$c"
+      return 0
+    fi
+  done
+  return 1
+}
+
+CONFIG=$(find_config) || {
+  echo "[eval] ❌ 找不到 config.yaml（尝试过：$SCRIPT_DIR/../assets/、\$ZK_AGENT_SKILLS_ROOT/model-iteration/assets/、\$AUTORESEARCH_CHAT_ROOT/skills/...）" >&2
+  exit 1
+}
+
+# 极简 yaml 解析：只提 cml_eval.workflow_id / cml_eval.version（与后端 _read_workflow_version 风格保持一致）
+read_cml_eval_field() {
+  local field=$1
+  awk -v field="$field" '
+    /^[^[:space:]#]/ { in_cml = ($1 == "cml_eval:") }
+    in_cml && $1 == field { gsub(/^["\x27]|["\x27]$/, "", $2); print $2; exit }
+  ' "$CONFIG"
+}
+
+EVAL_WORKFLOW_ID=$(read_cml_eval_field "workflow_id:")
+EVAL_VERSION=$(read_cml_eval_field "version:")
+
+if [ -z "$EVAL_WORKFLOW_ID" ] || [ -z "$EVAL_VERSION" ]; then
+  echo "[eval] ❌ 从 $CONFIG 读到的 cml_eval 不完整：workflow_id='$EVAL_WORKFLOW_ID' version='$EVAL_VERSION'" >&2
+  exit 1
+fi
+
+if [ -f ~/.cloudml-cli/.profile ]; then
+  source ~/.cloudml-cli/.profile
+else
+  echo "[eval] ❌ 未找到 ~/.cloudml-cli/.profile，请先安装并初始化 cml" >&2
+  exit 1
+fi
+
+echo "[eval] EVAL_RUNDIC=$EVAL_RUNDIC  workflow_id=$EVAL_WORKFLOW_ID  version=$EVAL_VERSION  (config: $CONFIG)"
+echo "[eval] MODEL_NEW=$MODEL_NEW"
+echo "[eval] MODEL_OLD=$MODEL_OLD"
+
+cml workflow run \
+  --workflow_id "$EVAL_WORKFLOW_ID" --version "$EVAL_VERSION" \
+  --global_inputs runDic="$EVAL_RUNDIC" \
+  --global_inputs model_path_new="$MODEL_NEW" \
+  --global_inputs model_path_old="$MODEL_OLD"
+
+echo "[eval] ✅ 已提交 workflow$EVAL_RUNDIC，产物在 /mnt/xiaoai-zk-model-train-tj5/workflow5/workflow${EVAL_RUNDIC}/"