新增文件管理与技能管理功能：

- **文件管理**：
  - 支持文件上传、解析和删除。
  - 限制支持格式（txt, md, docx, pdf），最大 10MB。
  - 提供前端上传区、文件列表及后端解析服务。

- **技能管理**：
  - 新增技能的创建、更新及删除功能。
  - 支持分配自定义提示词和工具权限。
  - 提供技能列表及分组工具选择交互界面。

同时调整相关 API 和前端组件，优化协作体验。

backend/app/services/file_parser.py

@@ -0,0 +1,64 @@
+"""文件解析服务：将上传的文件转换为纯文本"""
+
+from pathlib import Path
+
+
+# 支持的文件扩展名
+ALLOWED_EXTENSIONS = {".txt", ".md", ".docx", ".pdf"}
+
+# 最大文件大小 10MB
+MAX_FILE_SIZE = 10 * 1024 * 1024
+
+
+def parse_file(file_path: Path, mime_type: str) -> str:
+    """解析文件为纯文本，返回文本内容"""
+    ext = file_path.suffix.lower()
+
+    if ext in (".txt", ".md"):
+        return _parse_text(file_path)
+    elif ext == ".docx":
+        return _parse_docx(file_path)
+    elif ext == ".pdf":
+        return _parse_pdf(file_path)
+    else:
+        raise ValueError(f"不支持的文件格式: {ext}")
+
+
+def _parse_text(file_path: Path) -> str:
+    """解析纯文本文件，自动检测编码"""
+    for encoding in ("utf-8", "gbk", "gb18030", "utf-16", "latin-1"):
+        try:
+            return file_path.read_text(encoding=encoding)
+        except (UnicodeDecodeError, UnicodeError):
+            continue
+    # 最后用 errors='replace' 兜底
+    return file_path.read_text(encoding="utf-8", errors="replace")
+
+
+def _parse_docx(file_path: Path) -> str:
+    """解析 Word 文档"""
+    try:
+        from docx import Document
+    except ImportError:
+        raise RuntimeError("需要安装 python-docx: pip install python-docx")
+
+    doc = Document(str(file_path))
+    paragraphs = [p.text for p in doc.paragraphs if p.text.strip()]
+    return "\n\n".join(paragraphs)
+
+
+def _parse_pdf(file_path: Path) -> str:
+    """解析 PDF 文件"""
+    try:
+        import fitz  # pymupdf
+    except ImportError:
+        raise RuntimeError("需要安装 pymupdf: pip install pymupdf")
+
+    doc = fitz.open(str(file_path))
+    pages = []
+    for page in doc:
+        text = page.get_text().strip()
+        if text:
+            pages.append(text)
+    doc.close()
+    return "\n\n".join(pages)

backend/app/services/tools.py

@@ -6,7 +6,8 @@ from datetime import datetime, timezone
 from sqlmodel import Session, select, func
 
 from ..database import engine
-from ..models import Novel, Outline, WorldSetting, Character, Chapter
+from ..models import Novel, Outline, WorldSetting, Character, Chapter, NovelFile
+from ..database import DATA_DIR
 
 
 # ── 工具定义（provider 无关格式）──
@@ -204,25 +205,66 @@ TOOL_DEFINITIONS: list[dict] = [
             "required": ["chapter_id"],
         },
     },
+    # ── 文件工具 ──
+    {
+        "name": "list_files",
+        "description": "列出当前小说关联的所有上传文件，返回文件名、大小和纯文本字符数",
+        "parameters": {
+            "type": "object",
+            "properties": {},
+        },
+    },
+    {
+        "name": "read_file",
+        "description": "读取指定上传文件的纯文本内容。支持分段读取（offset/limit 按字符数），适合大文件。返回文本片段和总字符数。",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "file_id": {"type": "integer", "description": "文件 ID（从 list_files 获取）"},
+                "offset": {"type": "integer", "description": "起始字符位置（默认 0）"},
+                "limit": {"type": "integer", "description": "读取字符数（默认 5000，最大 10000）"},
+            },
+            "required": ["file_id"],
+        },
+    },
 ]
 
 
-def get_openai_tools() -> list[dict]:
+def _filter_defs(allowed: list[str] | None) -> list[dict]:
+    """根据白名单过滤工具定义"""
+    if not allowed:
+        return TOOL_DEFINITIONS
+    return [t for t in TOOL_DEFINITIONS if t["name"] in allowed]
+
+
+def get_openai_tools(allowed: list[str] | None = None) -> list[dict]:
     """转换为 OpenAI function calling 格式"""
     return [
         {"type": "function", "function": t}
-        for t in TOOL_DEFINITIONS
+        for t in _filter_defs(allowed)
     ]
 
 
-def get_anthropic_tools() -> list[dict]:
+def get_anthropic_tools(allowed: list[str] | None = None) -> list[dict]:
     """转换为 Anthropic tool_use 格式"""
     return [
         {"name": t["name"], "description": t["description"], "input_schema": t["parameters"]}
-        for t in TOOL_DEFINITIONS
+        for t in _filter_defs(allowed)
     ]
 
 
+def get_tools_description(allowed: list[str] | None = None) -> str:
+    """动态生成工具能力描述文本（用于系统提示词）"""
+    defs = _filter_defs(allowed)
+    if not defs:
+        return ""
+    lines = ["你拥有以下工具能力，可以直接操作小说数据："]
+    for t in defs:
+        lines.append(f"- {t['name']}: {t['description']}")
+    lines.append("\n请根据用户需求主动使用工具，不要仅给出建议而不执行。")
+    return "\n".join(lines)
+
+
 # ── 工具执行 ──
 
 
@@ -247,6 +289,8 @@ def execute_tool(name: str, arguments: dict, novel_id: int) -> str:
             "create_chapter": _create_chapter,
             "update_chapter": _update_chapter,
             "delete_chapter": _delete_chapter,
+            "list_files": _list_files,
+            "read_file": _read_file,
         }
         handler = handlers.get(name)
         if not handler:
@@ -593,3 +637,53 @@ def _delete_chapter(session: Session, novel_id: int, args: dict) -> str:
     session.delete(chapter)
     session.commit()
     return json.dumps({"deleted": True, "id": args["chapter_id"]}, ensure_ascii=False)
+
+
+# ── 文件处理函数 ──
+
+
+def _list_files(session: Session, novel_id: int, _args: dict) -> str:
+    items = session.exec(
+        select(NovelFile)
+        .where(NovelFile.novel_id == novel_id)
+        .order_by(NovelFile.created_at.desc())
+    ).all()
+    result = [
+        {
+            "id": f.id,
+            "filename": f.filename,
+            "file_size": f.file_size,
+            "text_length": f.text_length,
+            "created_at": f.created_at.isoformat() if f.created_at else None,
+        }
+        for f in items
+    ]
+    return json.dumps({"files": result, "total": len(result)}, ensure_ascii=False)
+
+
+def _read_file(session: Session, novel_id: int, args: dict) -> str:
+    file_id = args["file_id"]
+    offset = max(0, args.get("offset", 0))
+    limit = min(10000, max(1, args.get("limit", 5000)))
+
+    novel_file = session.get(NovelFile, file_id)
+    if not novel_file or novel_file.novel_id != novel_id:
+        return json.dumps({"error": "文件不存在"}, ensure_ascii=False)
+
+    # 读取预解析的纯文本缓存
+    text_path = DATA_DIR / "uploads" / str(novel_id) / f"{novel_file.stored_name}.txt"
+    if not text_path.exists():
+        return json.dumps({"error": "文件纯文本缓存不存在，可能需要重新上传"}, ensure_ascii=False)
+
+    full_text = text_path.read_text(encoding="utf-8")
+    total_length = len(full_text)
+    segment = full_text[offset:offset + limit]
+
+    return json.dumps({
+        "filename": novel_file.filename,
+        "text": segment,
+        "offset": offset,
+        "limit": limit,
+        "total_length": total_length,
+        "has_more": offset + limit < total_length,
+    }, ensure_ascii=False)