v1.1.1: 升级 AI 续聘建议为 LLM + Playwright RPA 采集 SmartCampus 平台材料

新增：

- scripts/service/platform_collector.py：Playwright RPA（持久化 browser profile 复用登录态），抓合同表（姓名/单位/到期日/剩余天数/预警/建议），逐行点"续聘材料包"按钮下载 PDF/Word 到 data/materials/

- scripts/service/material_parser.py：PDF/Word 文本提取（pypdf → pdf2image + pytesseract OCR 扫描件兜底；python-docx 抽 docx；antiword 兜底 .doc）

- scripts/service/llm_decision.py：AI 决策统一入口（优先调宿主 jiangchang_skill_core.llm_manager.call_llm_json；本地降级到规则版；输出 decision_source ∈ {llm, rules, rules_no_materials}）

- scripts/db/migrations.py：idempotent ALTER/CREATE，建 materials_content 表 + renewal_tasks 加 decision_source / materials_summary 列

修改：

- scripts/db/connection.py：init_db() 自动调 apply_all() 应用 v1.1.1 迁移

- scripts/db/repository.py：加 insert_material / list_materials_for_contract / list_pending_materials / summarize_materials / get_task_full

- scripts/service/task_generator.py：AI 决策统一改为调 llm_decision.decide_suggestion(materials_summary=...)；reason 文案加「已结合材料包（X 份文件，Y 字 / 待解析）综合判断：...」

- scripts/service/html_renderer.py：ctx 加 DECISION_SOURCE（LLM 推理 / 本地规则 / 本地规则（无材料））+ MATERIALS_COUNT

- scripts/templates/task.html.j2：footer 加 "AI 建议来源：{{DECISION_SOURCE}} · 已审阅材料 {{MATERIALS_COUNT}} 份"

- scripts/cli/app.py：新增 2 个 CLI（collect-from-platform / parse-materials）；现有 9 个不动

- requirements.txt：playwright / pypdf / python-docx / pdf2image / pytesseract / Pillow

- .env.example：SMARTCAMPUS_BASE_URL / SMARTCAMPUS_PROFILE_DIR / MATERIALS_DOWNLOAD_DIR / OCR_LANG / LLM_MODEL_NAME / LLM_API_BASE_URL / LLM_API_TOKEN

- SKILL.md：version 1.1.0 → 1.1.1；用户意图路由表加 2 行；新增「数据来源」段（三层来源 + AI 决策透明化）

- README.md：使用前要准备 → 加 playwright install chromium 步骤

- CHANGELOG.md：v1.1.1 条目

- scripts/util/constants.py：SKILL_VERSION 1.1.0 → 1.1.1

本地验证：11 个 CLI 全部 exit 0；decision_source=rules；reason 含「已结合材料包（6 份文件，待解析）」；HTML footer 含决策来源。

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

scripts/service/html_renderer.py

@@ -63,6 +63,22 @@ def render_task_html(conn: sqlite3.Connection, task_id: str) -> Path:
     suggestion_class = SUGGESTION_CLASS_MAP.get(task["ai_suggestion"], "observe")
     suggestion_label = SUGGESTION_LABELS.get(task["ai_suggestion"], task["ai_suggestion"])
 
+    # v1.1.1：决策来源透明化
+    decision_source = task["decision_source"] if "decision_source" in task.keys() else "rules"
+    source_label_map = {
+        "llm": "LLM 推理",
+        "rules": "本地规则",
+        "rules_no_materials": "本地规则（无材料）",
+    }
+    decision_source_label = source_label_map.get(decision_source, decision_source)
+
+    # v1.1.1：材料计数（来自 materials_content）
+    materials_count = conn.execute(
+        "SELECT COUNT(*) AS n FROM materials_content "
+        "WHERE contract_id = ? AND extracted_text IS NOT NULL",
+        (task["contract_id"],),
+    ).fetchone()["n"]
+
     ctx = {
         "TITLE": "续聘准备任务书",
         "TASK_ID": task_id,
@@ -82,6 +98,8 @@ def render_task_html(conn: sqlite3.Connection, task_id: str) -> Path:
         "SUGGESTION_REASON": task["suggestion_reason"],
         "DEADLINE": deadline,
         "CHECKLIST_BODY": checklist_html,
+        "DECISION_SOURCE": decision_source_label,
+        "MATERIALS_COUNT": materials_count,
     }
 
     template_path = get_templates_dir() / "task.html.j2"