feat(intelligence): Phase 2 学习循环基础 — 触发信号 + 经验行业维度

- 新增 triggers.rs: 5 种触发信号（痛点确认/正反馈/复杂工具链/用户纠正/行业模式）
- ExperienceStore 增加 industry_context + source_trigger 字段
- experience.rs format_for_injection 支持行业标签
- intelligence_hooks.rs 集成触发信号评估
- 17 个测试全通过 (7 trigger + 10 experience)

desktop/src-tauri/src/intelligence_hooks.rs

@@ -79,6 +79,7 @@ pub async fn post_conversation_hook(
     }
 
     // Step 1.6: Detect pain signals from user message
+    let mut pain_confidence: Option<f64> = None;
     if !_user_message.is_empty() {
         let messages = vec![zclaw_types::Message::user(_user_message)];
         if let Some(analysis) = crate::intelligence::pain_aggregator::analyze_for_pain_signals(&messages) {
@@ -101,6 +102,7 @@ pub async fn post_conversation_hook(
                         "[intelligence_hooks] Pain point recorded: {} (confidence: {:.2}, count: {})",
                         pain.summary, pain.confidence, pain.occurrence_count
                     );
+                    pain_confidence = Some(pain.confidence);
                 }
                 Err(e) => {
                     warn!("[intelligence_hooks] Failed to record pain point: {}", e);
@@ -109,6 +111,28 @@ pub async fn post_conversation_hook(
         }
     }
 
+    // Step 1.7: Evaluate learning triggers (rule-based, zero LLM cost)
+    if !_user_message.is_empty() {
+        let trigger_ctx = crate::intelligence::triggers::TriggerContext {
+            user_message: _user_message.to_string(),
+            tool_call_count: 0, // Will be populated from trajectory recorder in future
+            conversation_messages: vec![_user_message.to_string()],
+            pain_confidence,
+            industry_keywords: vec![], // Will be populated from industry config in Phase 3
+        };
+        let signals = crate::intelligence::triggers::evaluate_triggers(&trigger_ctx);
+        if !signals.is_empty() {
+            let signal_names: Vec<&str> = signals.iter()
+                .map(crate::intelligence::triggers::signal_description)
+                .collect();
+            debug!(
+                "[intelligence_hooks] Learning triggers activated: {:?}",
+                signal_names
+            );
+            // Future: Pass signals to LLM experience extraction (Phase 5)
+        }
+    }
+
     // Step 2: Record conversation for reflection
     let mut engine = reflection_state.lock().await;