学习路径多阶段化：拓扑层按每阶段≤3技能拆分，阶段命名改为 基础补齐→岗位核心*→项目验证；简历裸姓名/专硕识别修复

app/match/engine.py

@@ -27,27 +27,37 @@ def _coverage(have: set[str], want: list[str]) -> tuple[list[str], list[str], fl
     return matched, missing, cov
 
 
-def topological_stages(missing: list[str], lexicon: dict) -> list[dict]:
-    """把缺失技能按前置关系拓扑排序分阶段（Kahn 分层）。
+def topological_stages(missing: list[str], lexicon: dict,
+                       max_per_stage: int = 3) -> list[dict]:
+    """把缺失技能按前置关系拓扑排序分阶段（Kahn 分层 + 每层按上限再拆分）。
 
     前置关系取自技能词表 prereqs；前置技能若已掌握或不在缺失集中则视为已满足。
+    同一拓扑层技能过多时按 max_per_stage 拆成多个连续阶段，让路线图呈
+    "基础补齐 → 岗位核心 → … → 项目验证"的多阶段结构。
     每个阶段附带结构化验收标准（产出物 / 量化验收项 / 自测问答），按技能类别生成。
     """
     prereq = {s["name"]: [p for p in s.get("prereqs", []) if p in missing]
               for s in lexicon["skills"] if s["name"] in missing}
     cat_of = {s["name"]: s.get("category", "") for s in lexicon["skills"]}
     remaining = set(missing)
-    stages: list[dict] = []
-    stage_no = 1
+    layers: list[list[str]] = []
     while remaining:
         ready = sorted(s for s in remaining if not [p for p in prereq.get(s, []) if p in remaining])
         if not ready:  # 有环兜底：按字典序放出，保证终止
             ready = sorted(remaining)
-        stages.append({"stage": stage_no, "skills": ready,
-                       "goal": f"第 {stage_no} 阶段：掌握 {'、'.join(ready)}",
-                       "acceptance": build_acceptance(ready, cat_of)})
+        layers.append(ready)
         remaining -= set(ready)
-        stage_no += 1
+
+    # 拓扑层 → 按每阶段技能数上限拆分
+    stages: list[dict] = []
+    stage_no = 1
+    for layer in layers:
+        for i in range(0, len(layer), max_per_stage):
+            chunk = layer[i:i + max_per_stage]
+            stages.append({"stage": stage_no, "skills": chunk,
+                           "goal": f"第 {stage_no} 阶段：掌握 {'、'.join(chunk)}",
+                           "acceptance": build_acceptance(chunk, cat_of)})
+            stage_no += 1
     return stages