mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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f14418603a
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>
114 lines
3.4 KiB
Python
114 lines
3.4 KiB
Python
"""MCP data models."""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field, asdict
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from typing import List, Optional
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@dataclass
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class SymbolInfo:
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"""Information about a code symbol."""
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name: str
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kind: str
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file_path: str
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line_start: int
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line_end: int
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signature: Optional[str] = None
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documentation: Optional[str] = None
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def to_dict(self) -> dict:
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return {k: v for k, v in asdict(self).items() if v is not None}
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@dataclass
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class ReferenceInfo:
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"""Information about a symbol reference."""
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file_path: str
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line: int
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column: int
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context: str
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relationship_type: str
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def to_dict(self) -> dict:
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return asdict(self)
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@dataclass
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class RelatedSymbol:
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"""Related symbol (import, call target, etc.)."""
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name: str
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kind: str
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relationship: str # "imports", "calls", "inherits", "uses"
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file_path: Optional[str] = None
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def to_dict(self) -> dict:
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return {k: v for k, v in asdict(self).items() if v is not None}
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@dataclass
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class MCPContext:
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"""Model Context Protocol context object.
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This is the structured context that gets injected into
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LLM prompts to provide code understanding.
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"""
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version: str = "1.0"
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context_type: str = "code_context"
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symbol: Optional[SymbolInfo] = None
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definition: Optional[str] = None
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references: List[ReferenceInfo] = field(default_factory=list)
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related_symbols: List[RelatedSymbol] = field(default_factory=list)
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metadata: dict = field(default_factory=dict)
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def to_dict(self) -> dict:
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"""Convert to dictionary for JSON serialization."""
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result = {
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"version": self.version,
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"context_type": self.context_type,
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"metadata": self.metadata,
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}
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if self.symbol:
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result["symbol"] = self.symbol.to_dict()
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if self.definition:
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result["definition"] = self.definition
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if self.references:
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result["references"] = [r.to_dict() for r in self.references]
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if self.related_symbols:
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result["related_symbols"] = [s.to_dict() for s in self.related_symbols]
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return result
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def to_json(self, indent: int = 2) -> str:
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"""Serialize to JSON string."""
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return json.dumps(self.to_dict(), indent=indent)
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def to_prompt_injection(self) -> str:
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"""Format for injection into LLM prompt."""
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parts = ["<code_context>"]
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if self.symbol:
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parts.append(f"## Symbol: {self.symbol.name}")
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parts.append(f"Type: {self.symbol.kind}")
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parts.append(f"Location: {self.symbol.file_path}:{self.symbol.line_start}")
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if self.definition:
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parts.append("\n## Definition")
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parts.append(f"```\n{self.definition}\n```")
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if self.references:
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parts.append(f"\n## References ({len(self.references)} found)")
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for ref in self.references[:5]: # Limit to 5
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parts.append(f"- {ref.file_path}:{ref.line} ({ref.relationship_type})")
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parts.append(f" ```\n {ref.context}\n ```")
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if self.related_symbols:
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parts.append("\n## Related Symbols")
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for sym in self.related_symbols[:10]: # Limit to 10
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parts.append(f"- {sym.name} ({sym.relationship})")
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parts.append("</code_context>")
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return "\n".join(parts)
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