diff --git a/skills-data/xdfx-full-flow-skill-guide.md b/skills-data/xdfx-full-flow-skill-guide.md
index 014a6300..ec7ed043 100644
--- a/skills-data/xdfx-full-flow-skill-guide.md
+++ b/skills-data/xdfx-full-flow-skill-guide.md
@@ -194,7 +194,8 @@ Step1 受众目标分析完成：当前已入库受众 X 个，本步骤新增
 - 单个受众、账号、传播节点、脆弱点或策略失败，不中断整个流程。
 - 保存接口必须返回 `id`，否则不会进入下游步骤。
 - 受众目标默认入库 3 个，关键传播节点默认入库 3 个。
-- 下游脆弱点/策略默认只选择前 2 个受众和前 2 个传播节点继续，可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
+- 下游受众脆弱点/策略只能从“已在 Step2 分析出并关联关键传播节点”的受众中选择，默认取前 2 个；没有产生传播节点的受众不得继续分析脆弱点和策略。
+- 下游传播节点脆弱点/策略默认只选择前 2 个已入库传播节点继续，可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
 - 每个被选中的对象默认最多生成 3 个脆弱点、3 个策略。
 - 模型流式 `answer` 会自动拼接。
 - 流式模型接口第一次返回空响应、`y模型没有响应`、`模型没有响应` 或超时类字符串时，会自动再调用一次；仍失败才写入 `errors`。
diff --git a/skills-data/xdfx-single-agent-full-flow/SKILL.md b/skills-data/xdfx-single-agent-full-flow/SKILL.md
index 2cdce48e..b0a45773 100644
--- a/skills-data/xdfx-single-agent-full-flow/SKILL.md
+++ b/skills-data/xdfx-single-agent-full-flow/SKILL.md
@@ -86,7 +86,8 @@ python scripts/xdfx_flow.py --mode full --input xdfx-input.json --dry-run
 
 - 受众目标默认入库最优 3 个，可用 `--max-audiences` 调整。
 - 关键传播节点全局去重后默认入库最优 3 个，可用 `--max-pathways` 调整。
-- 下游脆弱点/策略默认只从已入库结果中选择前 2 个受众、前 2 个传播节点继续，可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
+- 下游受众脆弱点/策略只能从“已在 Step2 分析出并关联关键传播节点”的受众中选择，默认取前 2 个；没有产生传播节点的受众不得继续分析脆弱点和策略。
+- 下游传播节点脆弱点/策略默认从已入库传播节点中选择前 2 个继续；可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
 - 每个被选中的受众/传播节点默认生成最多 3 个脆弱点，可用 `--max-vulns` 调整。
 - 每个被选中的受众/传播节点默认生成最多 3 个策略，可用 `--max-strategies` 调整。
 - 每个保存接口必须拿到返回 `id` 才进入下游。
diff --git a/skills-data/xdfx-single-agent-full-flow/scripts/xdfx_flow.py b/skills-data/xdfx-single-agent-full-flow/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-single-agent-full-flow/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-single-agent-full-flow/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None:
diff --git a/skills-data/xdfx-split-controller/SKILL.md b/skills-data/xdfx-split-controller/SKILL.md
index 1ee180ab..71ad1d51 100644
--- a/skills-data/xdfx-split-controller/SKILL.md
+++ b/skills-data/xdfx-split-controller/SKILL.md
@@ -77,8 +77,9 @@ python scripts/xdfx_run_full_steps.py --input xdfx-input.json --state xdfx-flow-
 - Step1 的受众目标抽取结果里，`用户编号列表` / `编号id` / `id` 必须对应上一轮 `zhanghao` 账号数组的索引值；脚本会按这个索引依次找到账号并保存受众账号关系。
 - Step2 成功后会写入 `topPathways`。
 - Step1 默认入库最优 3 个受众；Step2 默认入库最优 3 个传播节点。
-- Step3/Step4 默认只选择前 2 个已入库受众/传播节点继续，每个生成最多 3 个脆弱点；可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
-- Step5/Step6 默认对上述选中的每个受众/传播节点生成最多 3 个策略。
+- Step3/Step5 的受众下游分析只能从“已在 Step2 分析出并关联关键传播节点”的受众中选择，默认取前 2 个；没有产生传播节点的受众不得继续分析脆弱点和策略。
+- Step4/Step6 的传播节点下游分析默认从已入库传播节点中选择前 2 个继续；可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
+- Step3-Step6 对上述选中的每个受众/传播节点默认生成最多 3 个脆弱点或策略。
 - 各步骤会同步前端 `task/task-action.step`；需要禁用时每条命令追加 `--no-step-status`。
 - 策略步骤会同步写入 channel、narrate、message、策略与受众/传播节点关系表，保证原前端策略表格/编排页可继续读取。
 - 策略步骤保存 `信息形式/模态` 时，必须使用脚本读取 `sys/sys-dict-data` 的 `modelType` 字典并把文字转成 value 逗号串，不能把“图片,视频”等文字直接写入 `task-action-message.modelType`。
diff --git a/skills-data/xdfx-split-controller/scripts/xdfx_flow.py b/skills-data/xdfx-split-controller/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-split-controller/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-split-controller/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None:
diff --git a/skills-data/xdfx-split-flow-skill-guide.md b/skills-data/xdfx-split-flow-skill-guide.md
index 62d52a6e..f09c6203 100644
--- a/skills-data/xdfx-split-flow-skill-guide.md
+++ b/skills-data/xdfx-split-flow-skill-guide.md
@@ -134,8 +134,9 @@ python scripts/xdfx_run_full_steps.py --input xdfx-input.json --state xdfx-flow-
 
 - Step1 入库最优 3 个受众。
 - Step2 入库最优 3 个传播节点。
-- Step3/Step4 只从已入库结果中默认选择前 2 个受众/传播节点继续，每个生成最多 3 个脆弱点；可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
-- Step5/Step6 对上述选中的每个受众/传播节点默认生成最多 3 个策略。
+- Step3/Step5 的受众下游分析只能从“已在 Step2 分析出并关联关键传播节点”的受众中选择，默认取前 2 个；没有产生传播节点的受众不得继续分析脆弱点和策略。
+- Step4/Step6 的传播节点下游分析默认从已入库传播节点中选择前 2 个继续；可用 `--downstream-audiences` / `--downstream-pathways` 调成 1 或 2。
+- Step3-Step6 对上述选中的每个受众/传播节点默认生成最多 3 个脆弱点或策略。
 - Step5/Step6 的策略模态必须按前端 `modelType` 字典保存为 value 逗号串；脚本会自动把“图片,视频/文本,图片”等文字转成 `task-action-message.modelType` 需要的值。
 
 总控反馈用户时建议使用这种格式：
diff --git a/skills-data/xdfx-step1-audience/scripts/xdfx_flow.py b/skills-data/xdfx-step1-audience/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-step1-audience/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-step1-audience/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None:
diff --git a/skills-data/xdfx-step2-pathway/scripts/xdfx_flow.py b/skills-data/xdfx-step2-pathway/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-step2-pathway/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-step2-pathway/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None:
diff --git a/skills-data/xdfx-step3-audience-vulns/scripts/xdfx_flow.py b/skills-data/xdfx-step3-audience-vulns/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-step3-audience-vulns/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-step3-audience-vulns/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None:
diff --git a/skills-data/xdfx-step4-pathway-vulns/scripts/xdfx_flow.py b/skills-data/xdfx-step4-pathway-vulns/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-step4-pathway-vulns/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-step4-pathway-vulns/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None:
diff --git a/skills-data/xdfx-step5-audience-strategy/scripts/xdfx_flow.py b/skills-data/xdfx-step5-audience-strategy/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-step5-audience-strategy/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-step5-audience-strategy/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None:
diff --git a/skills-data/xdfx-step6-pathway-strategy/scripts/xdfx_flow.py b/skills-data/xdfx-step6-pathway-strategy/scripts/xdfx_flow.py
index d9580e2f..8e62e51e 100644
--- a/skills-data/xdfx-step6-pathway-strategy/scripts/xdfx_flow.py
+++ b/skills-data/xdfx-step6-pathway-strategy/scripts/xdfx_flow.py
@@ -368,6 +368,7 @@ def default_state() -> dict[str, Any]:
         "topAudiences": [],
         "pathwayCandidates": [],
         "topPathways": [],
+        "audiencesWithPathways": [],
         "audienceVulns": {},
         "pathwayVulns": {},
         "audienceStrategies": [],
@@ -2055,6 +2056,88 @@ def selected_accounts_for_item(item: dict[str, Any], *, audience: bool) -> list[
     return [item.get("account") or item]
 
 
+def id_ref_key(value: Any) -> str:
+    number = as_int(value)
+    if number is not None:
+        return str(number)
+    return str(value or "").strip()
+
+
+def audience_name_key(value: Any) -> str:
+    return re.sub(r"\s+", "", str(value or "")).lower()
+
+
+def compact_pathway_ref(pathway: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "pathwayAccountId": pathway.get("pathwayAccountId") or pathway.get("pathwayId"),
+        "accountId": pathway.get("accountId"),
+        "accountName": pathway.get("accountName"),
+    }
+
+
+def add_unique_pathway(target: list[dict[str, Any]], pathway: dict[str, Any]) -> None:
+    key = id_ref_key(pathway.get("pathwayAccountId") or pathway.get("pathwayId") or pathway.get("accountId") or pathway.get("accountName"))
+    if not key:
+        return
+    seen = {
+        id_ref_key(item.get("pathwayAccountId") or item.get("pathwayId") or item.get("accountId") or item.get("accountName"))
+        for item in target
+    }
+    if key not in seen:
+        target.append(pathway)
+
+
+def audience_pathway_pairs(state: dict[str, Any]) -> list[tuple[dict[str, Any], list[dict[str, Any]]]]:
+    """Return only audiences that produced at least one saved key pathway."""
+    id_links: dict[str, list[dict[str, Any]]] = {}
+    name_links: dict[str, list[dict[str, Any]]] = {}
+    for pathway in state.get("topPathways") or []:
+        if not isinstance(pathway, dict):
+            continue
+        for design_id in pathway.get("designIds") or []:
+            key = id_ref_key(design_id)
+            if key:
+                id_links.setdefault(key, []).append(pathway)
+        for name in pathway.get("relatedAudiences") or []:
+            key = audience_name_key(name)
+            if key:
+                name_links.setdefault(key, []).append(pathway)
+
+    pairs: list[tuple[dict[str, Any], list[dict[str, Any]]]] = []
+    for audience_item in state.get("topAudiences") or []:
+        if not isinstance(audience_item, dict):
+            continue
+        linked: list[dict[str, Any]] = []
+        design_key = id_ref_key(audience_item.get("designId"))
+        for pathway in id_links.get(design_key, []):
+            add_unique_pathway(linked, pathway)
+        name_key = audience_name_key(audience_item.get("targetAudience"))
+        for pathway in name_links.get(name_key, []):
+            add_unique_pathway(linked, pathway)
+        if linked:
+            item = {**audience_item, "downstreamPathways": [compact_pathway_ref(pathway) for pathway in linked]}
+            pairs.append((item, linked))
+    return pairs
+
+
+def audience_pathway_summary(state: dict[str, Any]) -> list[dict[str, Any]]:
+    summary: list[dict[str, Any]] = []
+    for audience_item, pathways in audience_pathway_pairs(state):
+        summary.append(
+            {
+                "designId": audience_item.get("designId"),
+                "targetAudience": audience_item.get("targetAudience"),
+                "pathwayCount": len(pathways),
+                "pathways": [compact_pathway_ref(pathway) for pathway in pathways],
+            }
+        )
+    return summary
+
+
+def has_audiences_with_pathways(state: dict[str, Any]) -> bool:
+    return bool(audience_pathway_pairs(state))
+
+
 def downstream_limit(ctx: FlowContext, *, audience: bool) -> int:
     """Limit expensive downstream analysis while keeping 3 upstream rows stored."""
     raw = ctx.downstream_audiences if audience else ctx.downstream_pathways
@@ -2066,22 +2149,35 @@ def downstream_items(ctx: FlowContext, state: dict[str, Any], stage: str, *, aud
     source_key = "topAudiences" if audience else "topPathways"
     id_key = "designId" if audience else "pathwayAccountId"
     selected_key = "selectedDownstreamAudiences" if audience else "selectedDownstreamPathways"
-    items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    all_items = [item for item in (state.get(source_key) or []) if isinstance(item, dict)]
+    if audience:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
+        items = [item for item, _ in audience_pathway_pairs(state)]
+        if not items:
+            state[selected_key] = []
+            record_error(state, stage, "没有已分析出关键传播节点的受众，不能继续分析受众脆弱点或策略；请先重试关键传播节点分析")
+            return []
+    else:
+        items = all_items
     limit = downstream_limit(ctx, audience=audience)
     selected = items[:limit]
-    state[selected_key] = [
-        {
+    selected_rows: list[dict[str, Any]] = []
+    for item in selected:
+        row = {
             "id": item.get(id_key),
             "name": item.get("targetAudience") if audience else item.get("accountName"),
         }
-        for item in selected
-    ]
-    if items:
+        if audience:
+            row["pathwayCount"] = len(item.get("downstreamPathways") or [])
+            row["pathways"] = item.get("downstreamPathways") or []
+        selected_rows.append(row)
+    state[selected_key] = selected_rows
+    if selected:
         record_event(
             state,
             stage,
-            f"下游分析选择 {len(selected)}/{len(items)} 个{'受众' if audience else '传播节点'}",
-            {"limit": limit, "selected": state[selected_key]},
+            f"下游分析选择 {len(selected)}/{len(items)} 个{'已产生传播节点的受众' if audience else '传播节点'}",
+            {"limit": limit, "selected": state[selected_key], "candidateCount": len(items), "totalStoredCount": len(all_items)},
         )
     return selected
 
@@ -2183,6 +2279,7 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         record_error(state, stage, "没有已入库受众，无法分析传播节点")
         return
     if state.get("topPathways") and not ctx.dry_run:
+        state["audiencesWithPathways"] = audience_pathway_summary(state)
         record_event(state, stage, "topPathways 已存在，跳过。如需重跑请清空 state 中该字段。")
         return
     all_candidates: list[dict[str, Any]] = []
@@ -2278,9 +2375,10 @@ def run_step2(ctx: FlowContext, state: dict[str, Any]) -> None:
         http_post_json(ctx, state, "save_relation", relation_payload, stage + "/save-relation")
         saved.append(item)
     state["topPathways"] = saved
+    state["audiencesWithPathways"] = audience_pathway_summary(state)
     if saved:
         mark_step_status(ctx, state, ["step-analysis-keypoint", "step-analysis-weakpoint"], stage + "/step-status")
-    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个")
+    record_event(state, stage, f"完成，保存传播节点 {len(saved)} 个，可继续下游分析的受众 {len(state.get('audiencesWithPathways') or [])} 个")
 
 
 def run_step3(ctx: FlowContext, state: dict[str, Any]) -> None: