feat(growth,kernel,runtime): Embedding 接通 + 自学习自动化 — A线+B线 6 项实现

A线 Embedding 接通:
- A1: MemoryRetriever.set_embedding_client() + GrowthIntegration.configure_embedding()
  + Kernel.set_embedding_client() + viking_configure_embedding 传播到 Kernel
- A2: Skill 路由替换 new_tf_idf_only() 为 EmbeddingAdapter + LlmSkillFallback

B线 自学习自动化:
- B1: evolution_bridge.rs — candidate_to_manifest() (PromptOnly, disabled by default)
- B2: Kernel::generate_and_register_skill() 全链路 (LLM→parse→QualityGate→manifest→persist)
- B3: EvolutionMiddleware 双模式 (auto_mode 跳过注入, 留给 kernel 自动处理)
- B4: QualityGate 加固 (body ≥100字符 + 必须含标题 + 置信度上限 1.0)

验证: 934 tests PASS, 0 failures

crates/zclaw-kernel/Cargo.toml

@@ -17,6 +17,7 @@ zclaw-runtime = { workspace = true }
 zclaw-protocols = { workspace = true }
 zclaw-hands = { workspace = true }
 zclaw-skills = { workspace = true }
+zclaw-growth = { workspace = true }
 
 tokio = { workspace = true }
 tokio-stream = { workspace = true }

crates/zclaw-kernel/src/kernel/evolution_bridge.rs

@@ -0,0 +1,113 @@
+//! Evolution Bridge — connects growth crate's SkillCandidate to skills crate's SkillManifest
+//!
+//! The growth crate (zclaw-growth) generates SkillCandidate from conversation patterns.
+//! The skills crate (zclaw-skills) requires SkillManifest for disk persistence.
+//! This bridge lives in zclaw-kernel because it depends on both crates.
+
+use zclaw_growth::skill_generator::SkillCandidate;
+use zclaw_skills::{SkillManifest, SkillMode};
+use zclaw_types::SkillId;
+
+/// Convert a validated SkillCandidate into a SkillManifest ready for registration.
+///
+/// Safety invariants:
+/// - `mode` is always `PromptOnly` (auto-generated skills cannot execute code)
+/// - `enabled` is `false` (requires one explicit positive feedback to activate)
+/// - `body_markdown` becomes the SKILL.md body content (stored by serialize_skill_md)
+pub fn candidate_to_manifest(candidate: &SkillCandidate) -> SkillManifest {
+    let slug = name_to_slug(&candidate.name);
+
+    SkillManifest {
+        id: SkillId::new(format!("auto-{}", slug)),
+        name: candidate.name.clone(),
+        description: candidate.description.clone(),
+        version: format!("{}", candidate.version),
+        author: Some("zclaw-evolution".to_string()),
+        mode: SkillMode::PromptOnly,
+        capabilities: Vec::new(),
+        input_schema: None,
+        output_schema: None,
+        tags: vec!["auto-generated".to_string()],
+        category: None,
+        triggers: candidate.triggers.clone(),
+        tools: candidate.tools.clone(),
+        enabled: false,
+    }
+}
+
+/// Convert a human-readable name to a URL-safe slug.
+fn name_to_slug(name: &str) -> String {
+    let mut result = String::new();
+    for c in name.trim().chars() {
+        if c.is_ascii_alphanumeric() {
+            result.push(c.to_ascii_lowercase());
+        } else if c == ' ' || c == '-' || c == '_' {
+            result.push('-');
+        } else {
+            // Chinese/unicode characters: use hex representation
+            result.push_str(&format!("{:x}", c as u32));
+        }
+    }
+    result.trim_matches('-').to_string()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    fn make_candidate() -> SkillCandidate {
+        SkillCandidate {
+            name: "每日报表".to_string(),
+            description: "生成每日报表".to_string(),
+            triggers: vec!["报表".to_string(), "日报".to_string()],
+            tools: vec!["researcher".to_string()],
+            body_markdown: "# 每日报表\n步骤1\n步骤2".to_string(),
+            source_pattern: "报表生成".to_string(),
+            confidence: 0.85,
+            version: 1,
+        }
+    }
+
+    #[test]
+    fn test_candidate_to_manifest() {
+        let candidate = make_candidate();
+        let manifest = candidate_to_manifest(&candidate);
+
+        assert!(manifest.id.as_str().starts_with("auto-"));
+        assert_eq!(manifest.name, "每日报表");
+        assert_eq!(manifest.description, "生成每日报表");
+        assert_eq!(manifest.version, "1");
+        assert_eq!(manifest.author.as_deref(), Some("zclaw-evolution"));
+        assert_eq!(manifest.mode, SkillMode::PromptOnly);
+        assert!(!manifest.enabled, "auto-generated skills must start disabled");
+        assert_eq!(manifest.triggers, candidate.triggers);
+        assert_eq!(manifest.tools, candidate.tools);
+        assert!(manifest.tags.contains(&"auto-generated".to_string()));
+    }
+
+    #[test]
+    fn test_name_to_slug_ascii() {
+        assert_eq!(name_to_slug("Daily Report"), "daily-report");
+    }
+
+    #[test]
+    fn test_name_to_slug_chinese() {
+        let slug = name_to_slug("每日报表");
+        assert!(!slug.is_empty());
+        assert!(!slug.contains(' '));
+    }
+
+    #[test]
+    fn test_auto_generated_always_prompt_only() {
+        let candidate = make_candidate();
+        let manifest = candidate_to_manifest(&candidate);
+        assert_eq!(manifest.mode, SkillMode::PromptOnly);
+    }
+
+    #[test]
+    fn test_auto_generated_starts_disabled() {
+        let candidate = make_candidate();
+        let manifest = candidate_to_manifest(&candidate);
+        assert!(!manifest.enabled);
+    }
+}

crates/zclaw-kernel/src/kernel/mod.rs

@@ -9,6 +9,7 @@ mod triggers;
 mod approvals;
 mod orchestration;
 mod a2a;
+mod evolution_bridge;
 
 use std::sync::Arc;
 use tokio::sync::{broadcast, Mutex};
@@ -55,6 +56,8 @@ pub struct Kernel {
     growth: std::sync::Mutex<Option<std::sync::Arc<zclaw_runtime::GrowthIntegration>>>,
     /// Optional LLM driver for memory extraction (set by Tauri desktop layer)
     extraction_driver: Option<Arc<dyn zclaw_runtime::LlmDriverForExtraction>>,
+    /// Optional embedding client for semantic search (set by Tauri desktop layer)
+    embedding_client: Option<Arc<dyn zclaw_runtime::EmbeddingClient>>,
     /// MCP tool adapters — shared with Tauri MCP manager, updated dynamically
     mcp_adapters: Arc<std::sync::RwLock<Vec<zclaw_protocols::McpToolAdapter>>>,
     /// Dynamic industry keyword configs — shared with Tauri frontend, loaded from SaaS
@@ -166,6 +169,7 @@ impl Kernel {
             viking,
             growth: std::sync::Mutex::new(None),
             extraction_driver: None,
+            embedding_client: None,
             mcp_adapters: Arc::new(std::sync::RwLock::new(Vec::new())),
             industry_keywords: Arc::new(tokio::sync::RwLock::new(Vec::new())),
             a2a_router,
@@ -258,7 +262,17 @@ impl Kernel {
             }
 
             // Build semantic router from the skill registry (75 SKILL.md loaded at boot)
-            let semantic_router = SemanticSkillRouter::new_tf_idf_only(self.skills.clone());
+            let semantic_router = if let Some(ref embed_client) = self.embedding_client {
+                let adapter = crate::skill_router::EmbeddingAdapter::new(embed_client.clone());
+                let mut router = SemanticSkillRouter::new(self.skills.clone(), Arc::new(adapter));
+                if let Some(llm_fallback) = self.make_llm_skill_fallback() {
+                    router = router.with_llm_fallback(llm_fallback);
+                }
+                tracing::debug!("[Kernel] SemanticSkillRouter created with embedding support");
+                router
+            } else {
+                SemanticSkillRouter::new_tf_idf_only(self.skills.clone())
+            };
             let adapter = SemanticRouterAdapter::new(Arc::new(semantic_router));
             let mw = zclaw_runtime::middleware::butler_router::ButlerRouterMiddleware::with_router_and_shared_keywords(
                 Box::new(adapter),
@@ -286,6 +300,10 @@ impl Kernel {
                 if let Some(ref driver) = self.extraction_driver {
                     g = g.with_llm_driver(driver.clone());
                 }
+                // Propagate embedding client to memory retriever if configured
+                if let Some(ref embed_client) = self.embedding_client {
+                    g.configure_embedding(embed_client.clone());
+                }
                 *cached = Some(std::sync::Arc::new(g));
             }
             cached.as_ref().expect("growth present").clone()
@@ -475,6 +493,25 @@ impl Kernel {
         }
     }
 
+    /// Set the embedding client for semantic search.
+    ///
+    /// Propagates to both the skill router (ButlerRouter) and memory retrieval
+    /// (GrowthIntegration). The next middleware chain creation will use the
+    /// configured client for embedding-based similarity.
+    pub fn set_embedding_client(&mut self, client: Arc<dyn zclaw_runtime::EmbeddingClient>) {
+        tracing::info!("[Kernel] Embedding client configured for semantic search");
+        self.embedding_client = Some(client);
+        // Invalidate cached GrowthIntegration so next request builds with new embedding
+        if let Ok(mut g) = self.growth.lock() {
+            *g = None;
+        }
+    }
+
+    /// Create an LLM skill fallback using the kernel's LLM driver.
+    fn make_llm_skill_fallback(&self) -> Option<Arc<dyn zclaw_skills::semantic_router::RuntimeLlmIntent>> {
+        Some(Arc::new(crate::skill_router::LlmSkillFallback::new(self.driver.clone())))
+    }
+
     /// Get a reference to the shared MCP adapters list.
     ///
     /// The Tauri MCP manager updates this list when services start/stop.

crates/zclaw-kernel/src/kernel/skills.rs

@@ -76,4 +76,77 @@ impl Kernel {
         }
         self.skills.execute(&zclaw_types::SkillId::new(id), &ctx, input).await
     }
+
+    /// Generate a skill from an aggregated pattern and register it.
+    ///
+    /// Full pipeline:
+    /// 1. Build LLM prompt from pattern
+    /// 2. Call LLM to get JSON response
+    /// 3. Parse response into SkillCandidate
+    /// 4. Validate through QualityGate (threshold 0.85 for auto-mode)
+    /// 5. Convert to SkillManifest (PromptOnly, disabled by default)
+    /// 6. Persist to disk via SkillRegistry
+    pub async fn generate_and_register_skill(
+        &self,
+        pattern: &zclaw_growth::pattern_aggregator::AggregatedPattern,
+    ) -> Result<String> {
+        // 1. Build prompt
+        let prompt = zclaw_growth::skill_generator::SkillGenerator::build_prompt(pattern);
+
+        // 2. Call LLM
+        let request = zclaw_runtime::driver::CompletionRequest {
+            model: self.driver.provider().to_string(),
+            system: Some("你是技能设计专家，只返回 JSON 格式的技能定义。".to_string()),
+            messages: vec![zclaw_types::Message::user(prompt)],
+            max_tokens: Some(1024),
+            temperature: Some(0.3),
+            stream: false,
+            ..Default::default()
+        };
+
+        let response = self.driver.complete(request).await?;
+        let text = response.content.iter()
+            .filter_map(|block| match block {
+                zclaw_runtime::driver::ContentBlock::Text { text } => Some(text.as_str()),
+                _ => None,
+            })
+            .collect::<Vec<_>>()
+            .join("");
+
+        // 3. Parse into SkillCandidate
+        let candidate = zclaw_growth::skill_generator::SkillGenerator::parse_response(
+            &text, pattern,
+        )?;
+
+        // 4. Validate through QualityGate (higher threshold for auto-generation)
+        let existing_triggers: Vec<String> = self.skills.list().await
+            .into_iter()
+            .flat_map(|m| m.triggers)
+            .collect();
+        let gate = zclaw_growth::quality_gate::QualityGate::new(0.85, existing_triggers);
+        let report = gate.validate_skill(&candidate);
+        if !report.passed {
+            return Err(zclaw_types::ZclawError::ConfigError(format!(
+                "QualityGate rejected: {}", report.issues.join("; ")
+            )));
+        }
+
+        // 5. Convert to SkillManifest (PromptOnly, disabled)
+        let manifest = super::evolution_bridge::candidate_to_manifest(&candidate);
+        let skill_id = manifest.id.to_string();
+
+        // 6. Persist to disk
+        let skills_dir = self.config.skills_dir.as_ref()
+            .ok_or_else(|| zclaw_types::ZclawError::InvalidInput(
+                "Skills directory not configured".into()
+            ))?;
+        self.skills.create_skill(skills_dir, manifest).await?;
+
+        tracing::info!(
+            "[Kernel] Auto-generated skill '{}' (id={}) registered (disabled)",
+            candidate.name, skill_id
+        );
+
+        Ok(skill_id)
+    }
 }