feat(cli): 添加 --rule 选项支持模板自动发现

重构 ccw cli 模板系统：

- 新增 template-discovery.ts 模块，支持扁平化模板自动发现
- 添加 --rule <template> 选项，自动加载 protocol 和 template
- 模板目录从嵌套结构 (prompts/category/file.txt) 迁移到扁平结构 (prompts/category-function.txt)
- 更新所有 agent/command 文件，使用 $PROTO $TMPL 环境变量替代 $(cat ...) 模式
- 支持模糊匹配：--rule 02-review-architecture 可匹配 analysis-review-architecture.txt

其他更新：
- Dashboard: 添加 Claude Manager 和 Issue Manager 页面
- Codex-lens: 增强 chain_search 和 clustering 模块

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

codex-lens/src/codexlens/mcp/schema.py

@@ -0,0 +1,113 @@
+"""MCP data models."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field, asdict
+from typing import List, Optional
+
+
+@dataclass
+class SymbolInfo:
+    """Information about a code symbol."""
+    name: str
+    kind: str
+    file_path: str
+    line_start: int
+    line_end: int
+    signature: Optional[str] = None
+    documentation: Optional[str] = None
+
+    def to_dict(self) -> dict:
+        return {k: v for k, v in asdict(self).items() if v is not None}
+
+
+@dataclass
+class ReferenceInfo:
+    """Information about a symbol reference."""
+    file_path: str
+    line: int
+    column: int
+    context: str
+    relationship_type: str
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+
+@dataclass
+class RelatedSymbol:
+    """Related symbol (import, call target, etc.)."""
+    name: str
+    kind: str
+    relationship: str  # "imports", "calls", "inherits", "uses"
+    file_path: Optional[str] = None
+
+    def to_dict(self) -> dict:
+        return {k: v for k, v in asdict(self).items() if v is not None}
+
+
+@dataclass
+class MCPContext:
+    """Model Context Protocol context object.
+
+    This is the structured context that gets injected into
+    LLM prompts to provide code understanding.
+    """
+    version: str = "1.0"
+    context_type: str = "code_context"
+    symbol: Optional[SymbolInfo] = None
+    definition: Optional[str] = None
+    references: List[ReferenceInfo] = field(default_factory=list)
+    related_symbols: List[RelatedSymbol] = field(default_factory=list)
+    metadata: dict = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        """Convert to dictionary for JSON serialization."""
+        result = {
+            "version": self.version,
+            "context_type": self.context_type,
+            "metadata": self.metadata,
+        }
+
+        if self.symbol:
+            result["symbol"] = self.symbol.to_dict()
+        if self.definition:
+            result["definition"] = self.definition
+        if self.references:
+            result["references"] = [r.to_dict() for r in self.references]
+        if self.related_symbols:
+            result["related_symbols"] = [s.to_dict() for s in self.related_symbols]
+
+        return result
+
+    def to_json(self, indent: int = 2) -> str:
+        """Serialize to JSON string."""
+        return json.dumps(self.to_dict(), indent=indent)
+
+    def to_prompt_injection(self) -> str:
+        """Format for injection into LLM prompt."""
+        parts = ["<code_context>"]
+
+        if self.symbol:
+            parts.append(f"## Symbol: {self.symbol.name}")
+            parts.append(f"Type: {self.symbol.kind}")
+            parts.append(f"Location: {self.symbol.file_path}:{self.symbol.line_start}")
+
+        if self.definition:
+            parts.append("\n## Definition")
+            parts.append(f"```\n{self.definition}\n```")
+
+        if self.references:
+            parts.append(f"\n## References ({len(self.references)} found)")
+            for ref in self.references[:5]:  # Limit to 5
+                parts.append(f"- {ref.file_path}:{ref.line} ({ref.relationship_type})")
+                parts.append(f"  ```\n  {ref.context}\n  ```")
+
+        if self.related_symbols:
+            parts.append("\n## Related Symbols")
+            for sym in self.related_symbols[:10]:  # Limit to 10
+                parts.append(f"- {sym.name} ({sym.relationship})")
+
+        parts.append("</code_context>")
+        return "\n".join(parts)