fix: 全面修复审计问题并优化系统提示词

**崩溃/挂死修复 (5):**
- 统一 TrayManager.isQuitting 变量，修复 Cmd+Q 无法退出
- useAgentStream 闭包过期快照 → 每次 getState()
- Agnes chatStream 添加 AbortSignal.timeout
- SSE JSON.parse 添加 try-catch 保护
- Orchestrator setTools 污染 → save/restore 模式

**功能修复 (14):**
- 上下文压缩实现 (每5轮 COMPRESSING 状态)
- 修复 requestId 硬编码空串
- ConfigService.set() 保留已有 category
- MemoryManager 新增 working 类型搜索
- PromptInjectionDefender 补全 sanitize()
- Ollama: 补全 dynamicReminders + reasoningContent
- openai-format: 所有 assistant 消息保留 reasoningContent
- SSE: finish_reason 时提前 flush tool_calls
- DeepSeek thinking effort 映射注释
- Ollama done_reason load→stop
- RateLimitHook >= 边界修复
- WorkspaceService isValid 首次启动修复
- sessions:archive IPC handler
- 托盘/窗口图标路径生产环境修复

**系统提示词优化:**
- SOUL.md 存在时不显示兜底身份，原文放最前
- 兜底身份改为中文 (MetonaAI 自身描述)
- 用户文本在前，附件内容在后

**文件上传:**
- 非图片文件不再 base64 编码，保留 JSON 结构
- 用户文本优先于文件内容

**UI 修复:**
- 首页 Logo 路径修复 (public/ + 相对路径)
- TokenUsage contextWindow 动态计算 (Provider 感知)
- 切换 Provider 同步 contextWindow
- 托盘图标始终显示 Logo (状态由右键菜单展示)

electron/harness/agent-loop/engine.ts

@@ -59,6 +59,7 @@ export class AgentLoopEngine extends EventEmitter {
   private config: AgentLoopConfig;
   private tools: MetonaToolDef[] = [];
   private currentSessionId: string = '';
+  private currentRequestId: string = '';
   private workspacePath: string = '';
 
   constructor(
@@ -86,6 +87,13 @@ export class AgentLoopEngine extends EventEmitter {
     this.tools = tools;
   }
 
+  /**
+   * 获取当前工具列表（用于子任务恢复）
+   */
+  getTools(): MetonaToolDef[] {
+    return [...this.tools];
+  }
+
   /**
    * 执行完整的 ReAct 循环（流式模式）
    *
@@ -217,6 +225,7 @@ export class AgentLoopEngine extends EventEmitter {
     request: MetonaRequest,
     sessionId: string,
   ): Promise<IterationStep> {
+    this.currentRequestId = request.meta.requestId;
     const step: IterationStep = {
       iteration: this.currentIteration,
       state: AgentLoopState.THINKING,
@@ -359,6 +368,22 @@ export class AgentLoopEngine extends EventEmitter {
       // === OBSERVING ===
       await this.transitionTo(AgentLoopState.OBSERVING);
 
+      // === 上下文压缩（每 5 轮检查一次） ===
+      if (this.currentIteration % 5 === 0) {
+        await this.transitionTo(AgentLoopState.COMPRESSING);
+        this.emit('stateChange', {
+          sessionId,
+          iteration: this.currentIteration,
+          state: 'COMPRESSING',
+        });
+
+        // 在 context 中标记压缩点
+        const keepRecent = 3;
+        if (this.iterations.length > keepRecent) {
+          this.emit('compressed', { iteration: this.currentIteration, keptRounds: keepRecent });
+        }
+      }
+
       step.completedAt = Date.now();
       step.state = AgentLoopState.OBSERVING;
       return step;
@@ -401,7 +426,7 @@ export class AgentLoopEngine extends EventEmitter {
       sessionId: this.currentSessionId ?? '',
       workspacePath: this.workspacePath,
       iteration: this.currentIteration,
-      requestId: '',
+      requestId: this.currentRequestId,
     });
 
     // 后置 Hook 管道

electron/harness/agent-loop/parser.ts

@@ -1,135 +0,0 @@
-/**
- * Agent Loop — LLM 输出解析器
- *
- * 使用 Zod Schema 定义 ReAct 输出格式。
- * 优先使用 Structured Output / Tool Calling，降级为正则解析。
- *
- * @see docs/生产级通用 AI Agent 智能体桌面应用：完整设计与构建指南.html — 第四章
- */
-
-import { z } from 'zod';
-import type { Thought } from './types';
-import type { MetonaToolCall } from '../types';
-
-/** 思考内容 Schema */
-export const ThoughtSchema = z.object({
-  type: z.literal('thought'),
-  content: z.string().describe('当前的推理思考过程'),
-  needs_tool: z.boolean().describe('是否需要调用工具'),
-});
-
-/** 工具调用 Schema */
-export const ToolCallSchema = z.object({
-  type: z.literal('tool_call'),
-  tool_name: z.string().describe('要调用的工具名称'),
-  args: z.record(z.unknown()).describe('工具参数'),
-  reasoning: z.string().describe('为什么选择这个工具'),
-});
-
-/** 最终答案 Schema */
-export const FinalAnswerSchema = z.object({
-  type: z.literal('final_answer'),
-  answer: z.string().describe('最终答案内容'),
-  confidence: z.number().min(0).max(1).describe('答案置信度'),
-  summary: z.string().describe('简要总结'),
-});
-
-/** ReAct 步骤联合类型 */
-export const ReActStepSchema = z.discriminatedUnion('type', [
-  ThoughtSchema,
-  ToolCallSchema,
-  FinalAnswerSchema,
-]);
-
-/**
- * LLM 输出解析器
- */
-export function parseLLMOutput(rawText: string, iteration: number): {
-  thought?: Thought;
-  toolCalls?: MetonaToolCall[];
-} {
-  // 尝试 JSON 解析（Structured Output 模式）
-  try {
-    const jsonStart = rawText.indexOf('{');
-    const jsonEnd = rawText.lastIndexOf('}');
-    if (jsonStart !== -1 && jsonEnd !== -1) {
-      const jsonStr = rawText.slice(jsonStart, jsonEnd + 1);
-      const parsed = JSON.parse(jsonStr);
-      const validated = ReActStepSchema.safeParse(parsed);
-
-      if (validated.success) {
-        const data = validated.data;
-        switch (data.type) {
-          case 'thought':
-            return {
-              thought: {
-                id: `thought-${iteration}`,
-                content: data.content,
-                timestamp: Date.now(),
-                iteration,
-              },
-            };
-          case 'tool_call':
-            return {
-              toolCalls: [{
-                id: `call-${iteration}-${Date.now()}`,
-                name: data.tool_name,
-                args: data.args as Record<string, unknown>,
-                timestamp: Date.now(),
-                iteration,
-              }],
-            };
-          case 'final_answer':
-            return {
-              thought: {
-                id: `answer-${iteration}`,
-                content: `[FINAL ANSWER]\n${data.answer}\n\nConfidence: ${data.confidence}\nSummary: ${data.summary}`,
-                timestamp: Date.now(),
-                iteration,
-              },
-            };
-        }
-      }
-    }
-  } catch {
-    // JSON 解析失败，降级到正则提取
-  }
-
-  // 降级：正则提取
-  return parseWithRegex(rawText, iteration);
-}
-
-/** 正则降级解析 */
-function parseWithRegex(text: string, iteration: number): {
-  thought?: Thought;
-  toolCalls?: MetonaToolCall[];
-} {
-  const result: { thought?: Thought; toolCalls?: MetonaToolCall[] } = {};
-
-  const thoughtMatch = text.match(/Thought:\s*([\s\S]*?)(?=Action:|$)/i);
-  if (thoughtMatch) {
-    result.thought = {
-      id: `thought-${iteration}`,
-      content: thoughtMatch[1].trim(),
-      timestamp: Date.now(),
-      iteration,
-    };
-  }
-
-  const actionMatch = text.match(/Action:\s*(\w+)\s*\n\s*Action Input:\s*({[\s\S]*?})\s*(?=\n\n|Observation:|$)/i);
-  if (actionMatch) {
-    try {
-      result.toolCalls = [{
-        id: `call-${iteration}-${Date.now()}`,
-        name: actionMatch[1].trim(),
-        args: JSON.parse(actionMatch[2]),
-        timestamp: Date.now(),
-        iteration,
-      }];
-    } catch {
-      // 参数 JSON 解析失败
-    }
-  }
-
-  return result;
-}