chore(release): 版本升至 1.0.3（SKILL.md / SKILL_VERSION / CHANGELOG 对齐 POLICY-RELEASE-001）

CHANGELOG.md

@@ -3,6 +3,13 @@
 本文件对应技能市场 **「更新日志」** Tab。
 发布 CI 会按当前 tag 版本提取匹配的 `##` 小节，写入该版本的 `changelog`。
 
+## 1.0.3
+
+- 缺省匹配年度收敛为赛道库最新年度：往年赛道不再混入缺省匹配结果（仍可用年度参数指定其它年份）
+- 批量匹配容错：input 目录中无法解析的画像文件自动跳过并在任务中告警说明，不再中断整批
+- 匹配理由更据实：与赛道需求标签无任何重合时不再显示"存在部分相关性"，改为提示补充研究标签
+- 职称档位补全（助理教授、工程师系列等）；未识别职称遇门槛赛道仍按保守处理并说明原因
+
 ## 1.0.2
 
 - 批量匹配健壮性：画像待补录的教师（无研究标签也无论文）改为跳过并记录原因，不再中断整批；单教师指定匹配仍明确提示补录；汇总页单列"画像待补录"

DEMO.md

@@ -4,6 +4,11 @@ demo_video_url: ""
 
 演示视频录制后请把地址填入上方 `demo_video_url`。以下是无视频时的图文演示脚本。
 
+## 演示数据准备
+
+- 可直接导入 `tests/fixtures/` 三份脱敏样例：教师画像、指南赛道、期望匹配结论
+- 缺省匹配取赛道库**最新年度**赛道：演示前确认"指南赛道"表已录入当年赛道，往年赛道不会混入结果
+
 ## 演示一：教师问推荐（对话）
 
 1. 对话任务："我是张教授，研究钙钛矿、光伏、半导体和界面调控，SCI 一区 3 篇，今年适合报什么？"
@@ -15,3 +20,8 @@ demo_video_url: ""
 1. "指南赛道"表批量导入 2027 年各学部赛道；"教师画像"表维护本院教师。
 2. 顶栏「赛道匹配」一键匹配；批量模式出汇总页，强推荐名单一目了然。
 3. 「生成建议书」重建 HTML 供打印分发。
+
+## 演示三：批量容错（可选）
+
+1. input 目录放入多位教师画像 JSON，混入一个格式损坏的文件。
+2. 发起批量匹配：坏文件被跳过并逐条告警说明，其余教师正常出报告与汇总页。

SKILL.md

@@ -1,7 +1,7 @@
 ---
 name: 申报赛道推荐助手
 description: "高校申报指南智能赛道推荐：维护教师学术画像与指南赛道库，按标签匹配 + 硬门槛过滤生成可解释的 Top 3 赛道推荐与《申报策略建议书》。"
-version: 1.0.2
+version: 1.0.3
 author: 深圳匠厂科技有限公司
 metadata:
   openclaw:

scripts/service/match_service.py

@@ -28,19 +28,28 @@ TIER_NOT = "不建议"
 
 _TIER_RANK = {TIER_NOT: 0, TIER_TRY: 1, TIER_STRONG: 2}
 
-# 职称档位：数值越大越高职称；门槛 min_title 使用"X及以上"档位
+# 职称档位：数值越大越高职称；门槛 min_title 使用"X及以上"档位。
+# 助理教授按中级档从审处理（预聘制档次各校不一，宁缺勿滥）；
+# 未识别职称遇门槛赛道一律剔除并说明原因（审慎非激进）。
 _TITLE_RANK = {
     "助教": 0,
+    "初级": 0,
     "讲师": 1,
+    "中级": 1,
     "助理研究员": 1,
+    "助理教授": 1,
+    "工程师": 1,
     "博士后": 1,
     "副高及以上": 2,
     "副教授": 2,
     "副研究员": 2,
     "高级实验师": 2,
+    "高级工程师": 2,
     "正高及以上": 3,
     "教授": 3,
     "研究员": 3,
+    "教授级高级工程师": 3,
+    "正高级工程师": 3,
 }
 
 _STRONG_THRESHOLD = 80
@@ -197,7 +206,7 @@ def match_teacher(teacher: dict[str, Any], tracks: list[dict[str, Any]], season:
         if min_sci > 0:
             reasons.append(f"SCI一区余量：现有 {sci_q1} 篇 / 要求 {min_sci} 篇")
         if not reasons:
-            reasons.append("与赛道方向存在部分相关性")
+            reasons.append("现有研究标签与该赛道需求标签暂无重合，补充研究标签后可能改善匹配")
 
         if match_pct >= _STRONG_THRESHOLD:
             tier = TIER_STRONG
@@ -346,14 +355,17 @@ def run_match(
 
 
 def load_tracks(year: Optional[int] = None) -> list[dict[str, Any]]:
-    """读取赛道库（缺省全部年度；可按年度筛选）。"""
+    """读取赛道库（缺省取赛道库最新年度，与 actions.json 契约一致；显式传 year 按年度筛选）。"""
     conn = get_conn()
     try:
         cur = conn.cursor()
-        if year:
+        if year is None:
-            cur.execute("SELECT * FROM guide_tracks WHERE year = ? ORDER BY id", (year,))
+            cur.execute("SELECT MAX(year) FROM guide_tracks")
-        else:
+            row = cur.fetchone()
-            cur.execute("SELECT * FROM guide_tracks ORDER BY year DESC, id")
+            if not row or row[0] is None:
+                return []
+            year = int(row[0])
+        cur.execute("SELECT * FROM guide_tracks WHERE year = ? ORDER BY id", (year,))
         columns = [d[0] for d in cur.description]
         rows = [dict(zip(columns, row)) for row in cur.fetchall()]
     finally:
@@ -361,6 +373,35 @@ def load_tracks(year: Optional[int] = None) -> list[dict[str, Any]]:
     return rows
 
 
+def collect_input_teachers(input_dir: str) -> tuple[list[dict[str, Any]], list[dict[str, str]]]:
+    """读取 input/ 目录下的教师画像 JSON；坏文件跳过并记录原因，不中断整批。
+
+    返回 (教师列表, 跳过文件列表)；跳过文件形如 {"file": 名称, "reason": 原因}。
+    """
+    import os
+
+    files = sorted(
+        os.path.join(input_dir, name)
+        for name in os.listdir(input_dir)
+        if name.lower().endswith(".json")
+    )
+    teachers: list[dict[str, Any]] = []
+    skipped_files: list[dict[str, str]] = []
+    for path in files:
+        name = os.path.basename(path)
+        try:
+            with open(path, encoding="utf-8") as f:
+                payload = json.load(f)
+            if isinstance(payload, dict):
+                payload = payload.get("teachers") or [payload]
+            if not isinstance(payload, list):
+                raise ValueError("内容不是教师对象数组")
+            teachers.extend(item for item in payload if isinstance(item, dict))
+        except (OSError, ValueError) as exc:
+            skipped_files.append({"file": name, "reason": str(exc)})
+    return teachers, skipped_files
+
+
 def load_teachers(teacher_name: str = "") -> list[dict[str, Any]]:
     """读取教师画像（可按姓名精确筛选）。"""
     conn = get_conn()

scripts/service/task_service.py

@@ -478,7 +478,10 @@ def cmd_match(*, teacher: str = "", year: Optional[int] = None) -> int:
                 print(f"ERROR:TEACHER_NOT_FOUND {message}")
                 return 1
             if not tracks:
-                message = "指南赛道库为空：请先录入赛道（add-track / import-tracks）"
+                if year:
+                    message = f"指南赛道库中没有 {year} 年度赛道：请核对年度或先录入（add-track / import-tracks）"
+                else:
+                    message = "指南赛道库为空：请先录入赛道（add-track / import-tracks）"
                 log.warning("task_failed task_type=match error_code=TRACK_EMPTY stage=match")
                 finish(status="failed", message=message, skill=SKILL_SLUG, error_code="TRACK_EMPTY", stage="match")
                 print(f"ERROR:TRACK_EMPTY {message}")
@@ -529,9 +532,13 @@ def cmd_match(*, teacher: str = "", year: Optional[int] = None) -> int:
 
 
 def cmd_match_batch_input() -> int:
-    """批量匹配（async Action 入口）：匹配 input/ 目录中的教师画像 JSON 与库内赛道。"""
+    """批量匹配（async Action 入口）：匹配 input/ 目录中的教师画像 JSON 与库内赛道。
+
+    坏文件（解析失败/非对象数组）跳过并告警，不中断整批；画像不完整教师同样跳过并记录（E1）。
+    缺省匹配赛道库最新年度赛道。
+    """
     log = _get_task_logger()
-    from service.match_service import load_tracks, run_match
+    from service.match_service import collect_input_teachers, load_tracks, run_match
     from util.runtime_paths import get_input_dir
 
     with job_context(skill=SKILL_SLUG):
@@ -546,26 +553,17 @@ def cmd_match_batch_input() -> int:
                 return 1
 
             input_dir = get_input_dir()
-            files = sorted(
+            teachers, skipped_files = collect_input_teachers(input_dir)
-                os.path.join(input_dir, name)
+            for entry in skipped_files:
-                for name in os.listdir(input_dir)
+                log.warning("batch_input_file_skipped file=%s reason=%s stage=batch", entry["file"], entry["reason"])
-                if name.lower().endswith(".json")
+                emit(f"{entry['file']}：{entry['reason']}，该文件已跳过", type="warn", skill=SKILL_SLUG, stage="batch")
-            )
+            if not teachers:
-            if not files:
+                message = f"input 目录没有可用的教师画像 JSON（{len(skipped_files)} 个文件无法解析）：{input_dir}"
-                message = f"input 目录没有教师画像 JSON：{input_dir}"
                 log.warning("task_failed task_type=match_batch error_code=TEACHER_NOT_FOUND stage=batch")
                 finish(status="failed", message=message, skill=SKILL_SLUG, error_code="TEACHER_NOT_FOUND", stage="batch")
                 print(f"ERROR:TEACHER_NOT_FOUND {message}")
                 return 1
 
-            teachers = []
-            for path in files:
-                with open(path, encoding="utf-8") as f:
-                    payload = json.load(f)
-                if isinstance(payload, dict):
-                    payload = payload.get("teachers") or [payload]
-                teachers.extend(payload)
-
             tracks = load_tracks()
             if not tracks:
                 message = "指南赛道库为空：请先录入赛道（add-track / import-tracks）"
@@ -597,6 +595,7 @@ def cmd_match_batch_input() -> int:
                     "batch_no": result["batch_no"],
                     "count": result["count"],
                     "skipped": result.get("skipped") or [],
+                    "skipped_files": skipped_files,
                     "tier_counts": result["tier_counts"],
                     "brief_file": result["brief_file"],
                 }

scripts/util/constants.py

@@ -1,6 +1,6 @@
 """技能标识、版本与平台公共库约束（申报赛道推荐助手）。"""
 
 SKILL_SLUG = "match-guide-track-default"
-SKILL_VERSION = "1.0.2"
+SKILL_VERSION = "1.0.3"
 LOG_LOGGER_NAME = "openclaw.skill.match_guide_track_default"
 PLATFORM_KIT_MIN_VERSION = "1.2.11"

tests/test_match_rules.py

@@ -2,6 +2,9 @@
 """赛道匹配内核金标准用例（离线、确定性）。"""
 from __future__ import annotations
 
+import json
+import os
+import tempfile
 import unittest
 
 from _support import IsolatedDataRoot
@@ -80,13 +83,29 @@ class TestScoring(MatchRuleTestBase):
         self.assertEqual(top["match_pct"], 100)
         self.assertEqual(top["tier"], "强烈推荐")
 
-    def test_t1_try_band(self) -> None:
+    def test_coverage_only_below_try_band(self) -> None:
         result = self._match(_teacher(researchTags="钙钛矿,光伏", recentPapers=""))
         entry = next(e for e in result["top3"] if e["track"] == "面上项目·光伏材料")
-        # coverage 2/3 → 0.7*0.667=0.4667 → 47% 不建议？ + 无 priority → 47%；二维材料 coverage 1/3=0.333→23%
+        # 仅覆盖率 2/3 → 0.7×0.6667=46.7% → 47%，低于 50% 可尝试档 → 不建议
         self.assertEqual(entry["match_pct"], 47)
         self.assertEqual(entry["tier"], "不建议")
 
+    def test_t1_try_band_with_supplement_hint(self) -> None:
+        result = self._match(_teacher(researchTags="钙钛矿,光伏,界面调控"))
+        entry = next(e for e in result["top3"] if e["track"] == "面上项目·光伏材料")
+        # 覆盖率 2/3 → 0.7×0.6667=46.7% + 优先方向 1 命中 0.15 = 61.7% → 62%，落在可尝试档
+        self.assertEqual(entry["match_pct"], 62)
+        self.assertEqual(entry["tier"], "可尝试")
+        self.assertTrue(any("建议补充" in r for r in entry["reasons"]))
+
+    def test_zero_match_reason_is_factual(self) -> None:
+        result = self._match(_teacher(researchTags="量子计算", recentPapers="", projectHistory=""))
+        entry = next(e for e in result["top3"] if e["track"] == "面上项目·光伏材料")
+        reasons_text = "".join(entry["reasons"])
+        self.assertEqual(entry["match_pct"], 0)
+        self.assertIn("暂无重合", reasons_text)
+        self.assertNotIn("部分相关性", reasons_text)
+
     def test_t1_strong_boundary(self) -> None:
         # 恰好 80% 属强烈推荐
         result = self._match(_teacher(researchTags="钙钛矿,光伏,半导体", recentPapers=""))
@@ -210,5 +229,77 @@ class TestBatchSkipIncomplete(MatchRuleTestBase):
             run_match([_teacher(researchTags="", recentPapers="")], _tracks(), "2027", skip_incomplete=False)
 
 
+class TestTrackYearScope(MatchRuleTestBase):
+    """缺省取赛道库最新年度（actions.json 契约）；显式年度不受影响。"""
+
+    def _seed_two_years(self) -> None:
+        from service.profile_service import upsert_track
+
+        upsert_track({"year": 2026, "track_name": "旧方向", "program_name": "面上项目", "track_tags": "钙钛矿,光伏"})
+        upsert_track({"year": 2027, "track_name": "新方向", "program_name": "面上项目", "track_tags": "量子计算"})
+
+    def test_load_tracks_defaults_to_latest_year(self) -> None:
+        from service.match_service import load_tracks
+
+        self._seed_two_years()
+        self.assertEqual({t["year"] for t in load_tracks()}, {2027})
+
+    def test_load_tracks_explicit_year(self) -> None:
+        from service.match_service import load_tracks
+
+        self._seed_two_years()
+        self.assertEqual({t["year"] for t in load_tracks(year=2026)}, {2026})
+
+    def test_load_tracks_empty_library(self) -> None:
+        from service.match_service import load_tracks
+
+        self.assertEqual(load_tracks(), [])
+
+    def test_kernel_default_matches_latest_year_only(self) -> None:
+        from service.match_service import load_teachers, load_tracks, run_match
+        from service.profile_service import upsert_teacher
+
+        self._seed_two_years()
+        upsert_teacher({"teacher_name": "张教授", "title": "教授", "research_tags": "量子计算,量子信息"})
+        payload = run_match(load_teachers(), load_tracks(), "2027")
+        report = payload["reports"][0]
+        all_tracks = [e["track"] for entries in report["tiers"].values() for e in entries]
+        all_tracks += [e["track"] for e in report["top3"]]
+        self.assertIn("面上项目·新方向", all_tracks)
+        self.assertNotIn("面上项目·旧方向", all_tracks)
+
+
+class TestBatchInputCollection(MatchRuleTestBase):
+    """input/ 目录收集：坏文件跳过并记录原因，不中断整批。"""
+
+    def test_collect_input_teachers_skips_bad_files(self) -> None:
+        from service.match_service import collect_input_teachers
+
+        with tempfile.TemporaryDirectory() as tmp:
+            with open(os.path.join(tmp, "a.json"), "w", encoding="utf-8") as f:
+                json.dump([_teacher(), _teacher(teacherName="李教授")], f, ensure_ascii=False)
+            with open(os.path.join(tmp, "single.json"), "w", encoding="utf-8") as f:
+                json.dump({"teacherName": "王教授"}, f, ensure_ascii=False)
+            with open(os.path.join(tmp, "bad.json"), "w", encoding="utf-8") as f:
+                f.write("{not-json")
+            with open(os.path.join(tmp, "scalar.json"), "w", encoding="utf-8") as f:
+                json.dump("just-a-string", f, ensure_ascii=False)
+
+            teachers, skipped = collect_input_teachers(tmp)
+
+        names = [t.get("teacherName") for t in teachers]
+        self.assertEqual(names, ["张教授", "李教授", "王教授"])
+        self.assertEqual({e["file"] for e in skipped}, {"bad.json", "scalar.json"})
+        self.assertTrue(all(e["reason"] for e in skipped))
+
+    def test_collect_input_teachers_empty_dir(self) -> None:
+        from service.match_service import collect_input_teachers
+
+        with tempfile.TemporaryDirectory() as tmp:
+            teachers, skipped = collect_input_teachers(tmp)
+        self.assertEqual(teachers, [])
+        self.assertEqual(skipped, [])
+
+
 if __name__ == "__main__":
     unittest.main()