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_sft.sh

@@ -0,0 +1,63 @@
+#!/bin/bash
+# 用 cml custom_train submit 提交 SFT 训练（**只做这一件事**）。
+# 训练完成后请用 scripts/submit_cml_eval.sh 单独起评测，watcher 由 agent 自己挂。
+#
+# 用法：
+#   ./submit_sft.sh <SFT_RUNDIC> [PREV_RUNDIC]
+#
+# 推荐调用方式（runDic 由 resolve_run_ids.sh 决定，不要手敲）：
+#   eval "$(./scripts/resolve_run_ids.sh)"
+#   ./scripts/submit_sft.sh "$SFT_RUNDIC"
+#
+# 语义（与 program.md §746 + §5.2 对齐）：
+#   SFT_RUNDIC  = R{n-1}.runDic（augment / yaml / 归档全用这个，不 +1）
+#   PREV_RUNDIC = SFT_RUNDIC - 1（默认；只用作上一轮 sft_output 改名）
+
+set -euo pipefail
+
+SFT_RUNDIC=${1:?usage: $0 <SFT_RUNDIC> [PREV_RUNDIC]}
+PREV_RUNDIC=${2:-$((SFT_RUNDIC-1))}
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT=${AUTORESEARCH_ROOT:-/mnt/wangsenhao/autoresearch-zk}
+TPL=${SFT_TRAIN_JOB_TEMPLATE:-$SCRIPT_DIR/sft_train_job.yaml.tpl}
+YAML=/tmp/sft_train_job_r${SFT_RUNDIC}.yaml
+
+if [ -f ~/.cloudml-cli/.profile ]; then
+  source ~/.cloudml-cli/.profile
+else
+  echo "[sft] ❌ 未找到 ~/.cloudml-cli/.profile，请先安装并初始化 cml" >&2
+  exit 1
+fi
+
+# 1. 渲染 yaml 模板
+sed \
+  -e "s|{RUNDIC}|${SFT_RUNDIC}|g" \
+  -e "s|{PREV_RUNDIC}|${PREV_RUNDIC}|g" \
+  -e "s|{AUTORESEARCH_ROOT}|${ROOT}|g" \
+  "$TPL" > "$YAML"
+echo "[sft] yaml: $YAML  (SFT_RUNDIC=$SFT_RUNDIC PREV_RUNDIC=$PREV_RUNDIC)"
+
+# 2. 提交训练任务
+SUBMIT_OUT=$(cml custom_train submit --filename "$YAML" 2>&1)
+echo "$SUBMIT_OUT"
+JOB_ID=$(echo "$SUBMIT_OUT" | grep -oE 't-[0-9]+-[a-z0-9]+' | head -1)
+if [ -z "$JOB_ID" ]; then
+  echo "[sft] ❌ 提交失败" >&2
+  exit 1
+fi
+echo "[sft] ✅ JobID: $JOB_ID"
+
+cat <<EOF
+
+[sft] 任务提交完成，关键命令：
+  查看任务状态:  cml custom_train describe $JOB_ID
+  查看实时日志:  cml custom_train logs $JOB_ID --follow
+  停止任务:      cml custom_train kill $JOB_ID
+
+训练成功后产物在: $ROOT/sft_output/_SUCCESS
+随后用 scripts/submit_cml_eval.sh <EVAL_RUNDIC> 起评测（不要在本脚本里串接，避免跨 step bg 任务）。
+
+JobID:        $JOB_ID
+SFT_RUNDIC:   $SFT_RUNDIC   (yaml / sft_output 命名)
+EOF