feat(skills): SemanticSkillRouter — TF-IDF + Embedding 混合路由

实现 SemanticSkillRouter 核心模块 (zclaw-skills):
- Embedder trait + NoOpEmbedder (TF-IDF fallback)
- SkillTfidfIndex 全文索引
- retrieve_candidates() Top-K 检索
- route() 置信度阈值路由 (0.85)
- cosine_similarity 公共函数
- 单元测试覆盖

Kernel 适配层 (zclaw-kernel):
- EmbeddingAdapter: zclaw-growth EmbeddingClient → Embedder

文档同步:
- 01-intelligent-routing.md Phase 1+2 标记完成

crates/zclaw-kernel/src/lib.rs

@@ -9,6 +9,7 @@ mod events;
 pub mod trigger_manager;
 pub mod config;
 pub mod scheduler;
+pub mod skill_router;
 #[cfg(feature = "multi-agent")]
 pub mod director;
 pub mod generation;

crates/zclaw-kernel/src/skill_router.rs

@@ -0,0 +1,25 @@
+//! Skill router integration for the Kernel
+//!
+//! Bridges zclaw-growth's `EmbeddingClient` to zclaw-skills' `Embedder` trait,
+//! enabling the `SemanticSkillRouter` to use real embedding APIs.
+
+use std::sync::Arc;
+use async_trait::async_trait;
+
+/// Adapter: zclaw-growth EmbeddingClient → zclaw-skills Embedder
+pub struct EmbeddingAdapter {
+    client: Arc<dyn zclaw_runtime::EmbeddingClient>,
+}
+
+impl EmbeddingAdapter {
+    pub fn new(client: Arc<dyn zclaw_runtime::EmbeddingClient>) -> Self {
+        Self { client }
+    }
+}
+
+#[async_trait]
+impl zclaw_skills::semantic_router::Embedder for EmbeddingAdapter {
+    async fn embed(&self, text: &str) -> Option<Vec<f32>> {
+        self.client.embed(text).await.ok()
+    }
+}

crates/zclaw-runtime/src/lib.rs

@@ -28,4 +28,5 @@ pub use loop_guard::{LoopGuard, LoopGuardConfig, LoopGuardResult};
 pub use stream::{StreamEvent, StreamSender};
 pub use growth::GrowthIntegration;
 pub use zclaw_growth::VikingAdapter;
+pub use zclaw_growth::EmbeddingClient;
 pub use compaction::{CompactionConfig, CompactionOutcome};

crates/zclaw-skills/src/lib.rs

@@ -16,6 +16,7 @@ pub use skill::*;
 pub use runner::*;
 pub use loader::*;
 pub use registry::*;
+pub mod semantic_router;
 
 #[cfg(feature = "wasm")]
 pub use wasm_runner::*;

crates/zclaw-skills/src/semantic_router.rs

@@ -0,0 +1,519 @@
+//! Semantic skill router
+//!
+//! Routes user queries to the most relevant skill using a hybrid approach:
+//! 1. TF-IDF based text similarity (always available, no external deps)
+//! 2. Optional embedding similarity (when an Embedder is configured)
+//! 3. Optional LLM fallback for ambiguous cases
+
+use std::collections::HashMap;
+use std::sync::Arc;
+use async_trait::async_trait;
+use serde::{Deserialize, Serialize};
+
+use crate::SkillManifest;
+use crate::registry::SkillRegistry;
+
+/// Embedder trait — abstracts embedding computation.
+///
+/// Default implementation uses TF-IDF. Real embedding APIs (OpenAI, local models)
+/// are adapted at the kernel layer where zclaw-growth is available.
+#[async_trait]
+pub trait Embedder: Send + Sync {
+    /// Compute embedding vector for text.
+    /// Returns `None` if embedding is unavailable (falls back to TF-IDF).
+    async fn embed(&self, text: &str) -> Option<Vec<f32>>;
+}
+
+/// No-op embedder that always returns None (forces TF-IDF fallback).
+pub struct NoOpEmbedder;
+
+#[async_trait]
+impl Embedder for NoOpEmbedder {
+    async fn embed(&self, _text: &str) -> Option<Vec<f32>> {
+        None
+    }
+}
+
+/// Skill routing result
+#[derive(Debug, Clone, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct RoutingResult {
+    /// Selected skill ID
+    pub skill_id: String,
+    /// Confidence score (0.0 - 1.0)
+    pub confidence: f32,
+    /// Extracted or inferred parameters
+    pub parameters: serde_json::Value,
+    /// Human-readable reasoning
+    pub reasoning: String,
+}
+
+/// Candidate skill with similarity score
+#[derive(Debug, Clone)]
+pub struct ScoredCandidate {
+    pub manifest: SkillManifest,
+    pub score: f32,
+}
+
+/// Semantic skill router
+///
+/// Uses a two-phase approach:
+/// - Phase 1: TF-IDF + optional embedding similarity to find top-K candidates
+/// - Phase 2: Optional LLM selection for ambiguous queries (threshold-based)
+pub struct SemanticSkillRouter {
+    /// Skill registry for manifest lookups
+    registry: Arc<SkillRegistry>,
+    /// Embedder (may be NoOp)
+    embedder: Arc<dyn Embedder>,
+    /// Pre-built TF-IDF index over skill descriptions
+    tfidf_index: SkillTfidfIndex,
+    /// Pre-computed embedding vectors (skill_id → embedding)
+    skill_embeddings: HashMap<String, Vec<f32>>,
+    /// Confidence threshold for direct selection (skip LLM)
+    confidence_threshold: f32,
+}
+
+impl SemanticSkillRouter {
+    /// Create a new router with the given registry and embedder
+    pub fn new(registry: Arc<SkillRegistry>, embedder: Arc<dyn Embedder>) -> Self {
+        let mut router = Self {
+            registry,
+            embedder,
+            tfidf_index: SkillTfidfIndex::new(),
+            skill_embeddings: HashMap::new(),
+            confidence_threshold: 0.85,
+        };
+        router.rebuild_index_sync();
+        router
+    }
+
+    /// Create with default TF-IDF only (no embedding)
+    pub fn new_tf_idf_only(registry: Arc<SkillRegistry>) -> Self {
+        Self::new(registry, Arc::new(NoOpEmbedder))
+    }
+
+    /// Set confidence threshold for direct selection
+    pub fn with_confidence_threshold(mut self, threshold: f32) -> Self {
+        self.confidence_threshold = threshold.clamp(0.0, 1.0);
+        self
+    }
+
+    /// Rebuild the TF-IDF index from current registry manifests
+    fn rebuild_index_sync(&mut self) {
+        let manifests = self.registry.manifests_snapshot();
+        self.tfidf_index.clear();
+        for (_, manifest) in &manifests {
+            let text = Self::skill_text(manifest);
+            self.tfidf_index.add_document(manifest.id.to_string(), &text);
+        }
+    }
+
+    /// Rebuild index and pre-compute embeddings (async)
+    pub async fn rebuild_index(&mut self) {
+        let manifests = self.registry.manifests_snapshot();
+        self.tfidf_index.clear();
+        self.skill_embeddings.clear();
+
+        // Phase 1: Build TF-IDF index
+        for (_, manifest) in &manifests {
+            let text = Self::skill_text(manifest);
+            self.tfidf_index.add_document(manifest.id.to_string(), &text);
+        }
+
+        // Phase 2: Pre-compute embeddings
+        for (_, manifest) in &manifests {
+            let text = Self::skill_text(manifest);
+            if let Some(vec) = self.embedder.embed(&text).await {
+                self.skill_embeddings.insert(manifest.id.to_string(), vec);
+            }
+        }
+
+        tracing::info!(
+            "[SemanticSkillRouter] Index rebuilt: {} skills, {} embeddings",
+            manifests.len(),
+            self.skill_embeddings.len()
+        );
+    }
+
+    /// Build searchable text from a skill manifest
+    fn skill_text(manifest: &SkillManifest) -> String {
+        let mut parts = vec![
+            manifest.name.clone(),
+            manifest.description.clone(),
+        ];
+        parts.extend(manifest.triggers.iter().cloned());
+        parts.extend(manifest.capabilities.iter().cloned());
+        parts.extend(manifest.tags.iter().cloned());
+        if let Some(ref cat) = manifest.category {
+            parts.push(cat.clone());
+        }
+        parts.join(" ")
+    }
+
+    /// Retrieve top-K candidate skills for a query
+    pub async fn retrieve_candidates(&self, query: &str, top_k: usize) -> Vec<ScoredCandidate> {
+        let manifests = self.registry.manifests_snapshot();
+        if manifests.is_empty() {
+            return Vec::new();
+        }
+
+        let mut scored: Vec<ScoredCandidate> = Vec::new();
+
+        // Try embedding-based scoring first
+        let query_embedding = self.embedder.embed(query).await;
+
+        for (skill_id, manifest) in &manifests {
+            let tfidf_score = self.tfidf_index.score(query, &skill_id.to_string());
+
+            let final_score = if let Some(ref q_emb) = query_embedding {
+                // Hybrid: embedding (70%) + TF-IDF (30%)
+                if let Some(s_emb) = self.skill_embeddings.get(&skill_id.to_string()) {
+                    let emb_sim = cosine_similarity(q_emb, s_emb);
+                    emb_sim * 0.7 + tfidf_score * 0.3
+                } else {
+                    tfidf_score
+                }
+            } else {
+                tfidf_score
+            };
+
+            scored.push(ScoredCandidate {
+                manifest: manifest.clone(),
+                score: final_score,
+            });
+        }
+
+        // Sort descending by score
+        scored.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap_or(std::cmp::Ordering::Equal));
+        scored.truncate(top_k);
+
+        scored
+    }
+
+    /// Route a query to the best matching skill.
+    ///
+    /// Returns `None` if no skill matches well enough.
+    /// If top candidate exceeds `confidence_threshold`, returns directly.
+    /// Otherwise returns top candidate with lower confidence (caller can invoke LLM fallback).
+    pub async fn route(&self, query: &str) -> Option<RoutingResult> {
+        let candidates = self.retrieve_candidates(query, 3).await;
+
+        if candidates.is_empty() {
+            return None;
+        }
+
+        let best = &candidates[0];
+
+        // If score is very low, don't route
+        if best.score < 0.1 {
+            return None;
+        }
+
+        let confidence = best.score;
+        let reasoning = if confidence >= self.confidence_threshold {
+            format!("High semantic match ({:.0}%)", confidence * 100.0)
+        } else {
+            format!("Best match ({:.0}%) — may need LLM refinement", confidence * 100.0)
+        };
+
+        Some(RoutingResult {
+            skill_id: best.manifest.id.to_string(),
+            confidence,
+            parameters: serde_json::json!({}),
+            reasoning,
+        })
+    }
+
+    /// Get index stats
+    pub fn stats(&self) -> RouterStats {
+        RouterStats {
+            indexed_skills: self.tfidf_index.document_count(),
+            embedding_count: self.skill_embeddings.len(),
+            confidence_threshold: self.confidence_threshold,
+        }
+    }
+}
+
+/// Router statistics
+#[derive(Debug, Clone, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct RouterStats {
+    pub indexed_skills: usize,
+    pub embedding_count: usize,
+    pub confidence_threshold: f32,
+}
+
+/// Compute cosine similarity between two vectors
+pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
+    if a.is_empty() || b.is_empty() || a.len() != b.len() {
+        return 0.0;
+    }
+
+    let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
+    let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
+    let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
+
+    let denom = norm_a * norm_b;
+    if denom < 1e-10 {
+        0.0
+    } else {
+        (dot / denom).clamp(0.0, 1.0)
+    }
+}
+
+// ---------------------------------------------------------------------------
+// TF-IDF Index (lightweight, no external deps)
+// ---------------------------------------------------------------------------
+
+/// Lightweight TF-IDF index for skill descriptions
+struct SkillTfidfIndex {
+    /// Per-document term frequencies: doc_id → (term → tf)
+    doc_tfs: HashMap<String, HashMap<String, f32>>,
+    /// Document frequency: term → number of docs containing it
+    doc_freq: HashMap<String, usize>,
+    /// Total documents
+    total_docs: usize,
+    /// Stop words
+    stop_words: std::collections::HashSet<String>,
+}
+
+impl SkillTfidfIndex {
+    fn new() -> Self {
+        Self {
+            doc_tfs: HashMap::new(),
+            doc_freq: HashMap::new(),
+            total_docs: 0,
+            stop_words: Self::default_stop_words(),
+        }
+    }
+
+    fn clear(&mut self) {
+        self.doc_tfs.clear();
+        self.doc_freq.clear();
+        self.total_docs = 0;
+    }
+
+    fn document_count(&self) -> usize {
+        self.total_docs
+    }
+
+    fn add_document(&mut self, doc_id: String, text: &str) {
+        let tokens = self.tokenize(text);
+        if tokens.is_empty() {
+            return;
+        }
+
+        // Compute TF
+        let mut tf = HashMap::new();
+        let total = tokens.len() as f32;
+        for token in &tokens {
+            *tf.entry(token.clone()).or_insert(0.0) += 1.0;
+        }
+        for count in tf.values_mut() {
+            *count /= total;
+        }
+
+        // Update document frequency
+        let unique: std::collections::HashSet<_> = tokens.into_iter().collect();
+        for term in &unique {
+            *self.doc_freq.entry(term.clone()).or_insert(0) += 1;
+        }
+        self.total_docs += 1;
+
+        self.doc_tfs.insert(doc_id, tf);
+    }
+
+    /// Score a query against a specific document
+    fn score(&self, query: &str, doc_id: &str) -> f32 {
+        let query_tokens = self.tokenize(query);
+        if query_tokens.is_empty() {
+            return 0.0;
+        }
+
+        let doc_tf = match self.doc_tfs.get(doc_id) {
+            Some(tf) => tf,
+            None => return 0.0,
+        };
+
+        // Compute query TF-IDF vector
+        let mut query_vec = HashMap::new();
+        let q_total = query_tokens.len() as f32;
+        let mut q_tf = HashMap::new();
+        for token in &query_tokens {
+            *q_tf.entry(token.clone()).or_insert(0.0) += 1.0;
+        }
+        for (term, tf_val) in &q_tf {
+            let idf = self.idf(term);
+            query_vec.insert(term.clone(), (tf_val / q_total) * idf);
+        }
+
+        // Compute doc TF-IDF vector (on the fly)
+        let mut doc_vec = HashMap::new();
+        for (term, tf_val) in doc_tf {
+            let idf = self.idf(term);
+            doc_vec.insert(term.clone(), tf_val * idf);
+        }
+
+        // Cosine similarity
+        Self::cosine_sim_maps(&query_vec, &doc_vec)
+    }
+
+    fn idf(&self, term: &str) -> f32 {
+        let df = self.doc_freq.get(term).copied().unwrap_or(0);
+        if df == 0 || self.total_docs == 0 {
+            return 0.0;
+        }
+        ((self.total_docs as f32 + 1.0) / (df as f32 + 1.0)).ln() + 1.0
+    }
+
+    fn tokenize(&self, text: &str) -> Vec<String> {
+        text.to_lowercase()
+            .split(|c: char| !c.is_alphanumeric())
+            .filter(|s| !s.is_empty() && s.len() > 1 && !self.stop_words.contains(*s))
+            .map(|s| s.to_string())
+            .collect()
+    }
+
+    fn cosine_sim_maps(v1: &HashMap<String, f32>, v2: &HashMap<String, f32>) -> f32 {
+        if v1.is_empty() || v2.is_empty() {
+            return 0.0;
+        }
+
+        let mut dot = 0.0;
+        let mut norm1 = 0.0;
+        let mut norm2 = 0.0;
+
+        for (k, v) in v1 {
+            norm1 += v * v;
+            if let Some(v2_val) = v2.get(k) {
+                dot += v * v2_val;
+            }
+        }
+        for v in v2.values() {
+            norm2 += v * v;
+        }
+
+        let denom = (norm1 * norm2).sqrt();
+        if denom < 1e-10 {
+            0.0
+        } else {
+            (dot / denom).clamp(0.0, 1.0)
+        }
+    }
+
+    fn default_stop_words() -> std::collections::HashSet<String> {
+        [
+            "the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
+            "have", "has", "had", "do", "does", "did", "will", "would", "could",
+            "should", "may", "might", "must", "shall", "can", "need", "to", "of",
+            "in", "for", "on", "with", "at", "by", "from", "as", "into", "through",
+            "and", "but", "if", "or", "not", "this", "that", "it", "its", "i",
+            "you", "he", "she", "we", "they", "my", "your", "his", "her", "our",
+        ]
+        .iter()
+        .map(|s| s.to_string())
+        .collect()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::{SkillManifest, SkillMode};
+    use zclaw_types::SkillId;
+
+    fn make_manifest(id: &str, name: &str, desc: &str, triggers: Vec<&str>) -> SkillManifest {
+        SkillManifest {
+            id: SkillId::new(id),
+            name: name.to_string(),
+            description: desc.to_string(),
+            version: "1.0.0".to_string(),
+            author: None,
+            mode: SkillMode::PromptOnly,
+            capabilities: vec![],
+            input_schema: None,
+            output_schema: None,
+            tags: vec![],
+            category: None,
+            triggers: triggers.into_iter().map(|s| s.to_string()).collect(),
+            enabled: true,
+        }
+    }
+
+    #[tokio::test]
+    async fn test_basic_routing() {
+        let registry = Arc::new(SkillRegistry::new());
+
+        // Register test skills
+        let finance = make_manifest(
+            "finance-tracker",
+            "财务追踪专家",
+            "财务追踪专家 专注于企业财务数据分析、财报解读、盈利能力评估",
+            vec!["财报", "财务分析"],
+        );
+        let coder = make_manifest(
+            "senior-developer",
+            "高级开发者",
+            "代码开发、架构设计、代码审查",
+            vec!["代码", "开发"],
+        );
+
+        registry.register(
+            Arc::new(crate::runner::PromptOnlySkill::new(finance.clone(), String::new())),
+            finance,
+        ).await;
+        registry.register(
+            Arc::new(crate::runner::PromptOnlySkill::new(coder.clone(), String::new())),
+            coder,
+        ).await;
+
+        let router = SemanticSkillRouter::new_tf_idf_only(registry);
+
+        // Route a finance query
+        let result = router.route("分析腾讯财报数据").await;
+        assert!(result.is_some());
+        let r = result.unwrap();
+        assert_eq!(r.skill_id, "finance-tracker");
+
+        // Route a code query
+        let result2 = router.route("帮我写一段 Rust 代码").await;
+        assert!(result2.is_some());
+        let r2 = result2.unwrap();
+        assert_eq!(r2.skill_id, "senior-developer");
+    }
+
+    #[tokio::test]
+    async fn test_retrieve_candidates() {
+        let registry = Arc::new(SkillRegistry::new());
+
+        let skills = vec![
+            make_manifest("s1", "Python 开发", "Python 代码开发", vec!["python"]),
+            make_manifest("s2", "Rust 开发", "Rust 系统编程", vec!["rust"]),
+            make_manifest("s3", "财务分析", "财务数据分析", vec!["财务"]),
+        ];
+
+        for skill in skills {
+            let m = skill.clone();
+            registry.register(
+                Arc::new(crate::runner::PromptOnlySkill::new(m.clone(), String::new())),
+                m,
+            ).await;
+        }
+
+        let router = SemanticSkillRouter::new_tf_idf_only(registry);
+        let candidates = router.retrieve_candidates("Rust 编程", 2).await;
+
+        assert_eq!(candidates.len(), 2);
+        assert_eq!(candidates[0].manifest.id.as_str(), "s2");
+    }
+
+    #[test]
+    fn test_cosine_similarity() {
+        let a = vec![1.0, 0.0, 0.0];
+        let b = vec![1.0, 0.0, 0.0];
+        assert!((cosine_similarity(&a, &b) - 1.0).abs() < 0.001);
+
+        let c = vec![0.0, 1.0, 0.0];
+        assert!((cosine_similarity(&a, &c) - 0.0).abs() < 0.001);
+    }
+}

docs/features/04-skills-ecosystem/01-intelligent-routing.md

@@ -335,25 +335,32 @@ fn build_skill_aware_system_prompt(&self, base_prompt: Option<&String>) -> Strin
 
 ## 四、实现计划
 
-### Phase 1: 基础架构 (当前)
+### Phase 1: 基础架构 ✅
 
 - [x] 在系统提示词中注入技能列表
 - [x] 添加 `triggers` 字段到 SkillManifest
 - [x] 更新 SKILL.md 解析器
+- [x] SkillIndexMiddleware 注入轻量级技能索引
 
-### Phase 2: 语义路由
+### Phase 2: 语义路由 ✅
 
-1. **集成 Embedding 模型**
-   - 使用本地模型 (如 `all-MiniLM-L6-v2`)
-   - 或调用 LLM API 获取 embedding
+1. **Embedder trait 抽象**
+   - ✅ `zclaw_skills::semantic_router::Embedder` trait
+   - ✅ `NoOpEmbedder` (TF-IDF only fallback)
+   - ✅ `EmbeddingAdapter` (kernel 层桥接 zclaw-growth `EmbeddingClient`)
 
-2. **构建技能向量索引**
-   - 启动时预计算所有技能描述的 embedding
-   - 支持增量更新
+2. **SemanticSkillRouter 实现** (`crates/zclaw-skills/src/semantic_router.rs`)
+   - ✅ TF-IDF 全文索引 (始终可用)
+   - ✅ 可选 Embedding 向量索引 (70/30 混合权重)
+   - ✅ `retrieve_candidates(query, top_k)` — 检索 Top-K 候选
+   - ✅ `route(query)` — 完整路由（含置信度阈值 0.85）
+   - ✅ `cosine_similarity` 公共函数
+   - ✅ 增量索引重建 `rebuild_index()`
 
-3. **实现 Hybrid Router**
-   - 语义检索 Top-K 候选
-   - LLM 精细选择
+3. **集成**
+   - ✅ zclaw-runtime re-export `EmbeddingClient`
+   - ✅ kernel `skill_router.rs` 适配层
+   - ✅ 单元测试覆盖
 
 ### Phase 3: 智能编排