fix(audit): 修复深度审计发现的 P0/P1 问题 (8项)

基于 DEEP_AUDIT_REPORT.md 修复 2 CRITICAL + 4 HIGH + 1 MEDIUM 问题:

- C1: PromptOnly 技能集成 LLM 调用 — 定义 LlmCompleter trait，
  通过 LlmDriverAdapter 桥接 zclaw_runtime::LlmDriver，
  PromptOnlySkill.execute() 现在调用 LLM 生成内容
- C2: 反思引擎空记忆 bug — 新增 query_memories_for_reflection()
  从 VikingStorage 查询真实记忆传入 reflect()
- H7: Agent Store 接口适配 — KernelClient 添加 listClones/createClone/
  deleteClone/updateClone 方法，映射到 agent_* 命令
- H8: Hand 审批检查 — hand_execute 执行前检查 needs_approval，
  需审批返回 pending_approval 状态
- M1: 幽灵命令注册 — 注册 hand_get/hand_run_status/hand_run_list
  三个 Tauri 桩命令
- H1/H2: SpeechHand/TwitterHand 添加 demo 标签
- H5: 归档过时 VERIFICATION_REPORT

文档更新: DEEP_AUDIT_REPORT.md 标记修复状态，README.md 更新
关键指标和变更历史。整体完成度从 ~50% 提升至 ~58%。

crates/zclaw-skills/src/runner.rs

@@ -4,6 +4,7 @@ use async_trait::async_trait;
 use serde_json::Value;
 use std::process::Command;
 use std::time::Instant;
+use tracing::warn;
 use zclaw_types::Result;
 
 use super::{Skill, SkillContext, SkillManifest, SkillResult};
@@ -38,8 +39,21 @@ impl Skill for PromptOnlySkill {
         &self.manifest
     }
 
-    async fn execute(&self, _context: &SkillContext, input: Value) -> Result<SkillResult> {
+    async fn execute(&self, context: &SkillContext, input: Value) -> Result<SkillResult> {
         let prompt = self.format_prompt(&input);
+
+        // If an LLM completer is available, generate an AI response
+        if let Some(completer) = &context.llm {
+            match completer.complete(&prompt).await {
+                Ok(response) => return Ok(SkillResult::success(Value::String(response))),
+                Err(e) => {
+                    warn!("[PromptOnlySkill] LLM completion failed: {}, falling back to raw prompt", e);
+                    // Fall through to return raw prompt
+                }
+            }
+        }
+
+        // No LLM available — return formatted prompt (backward compatible)
         Ok(SkillResult::success(Value::String(prompt)))
     }
 }

crates/zclaw-skills/src/skill.rs

@@ -2,8 +2,21 @@
 
 use serde::{Deserialize, Serialize};
 use serde_json::Value;
+use std::pin::Pin;
 use zclaw_types::{SkillId, Result};
 
+/// Type-erased LLM completion interface.
+///
+/// Defined here (in zclaw-skills) to avoid a circular dependency on zclaw-runtime.
+/// Implementations live in zclaw-kernel where both crates are available.
+pub trait LlmCompleter: Send + Sync {
+    /// Complete a simple prompt → response (no system prompt, no tools).
+    fn complete(
+        &self,
+        prompt: &str,
+    ) -> Pin<Box<dyn std::future::Future<Output = std::result::Result<String, String>> + Send + '_>>;
+}
+
 /// Skill manifest definition
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct SkillManifest {
@@ -63,7 +76,7 @@ pub enum SkillMode {
 }
 
 /// Skill execution context
-#[derive(Debug, Clone)]
+#[derive(Clone)]
 pub struct SkillContext {
     /// Agent ID executing the skill
     pub agent_id: String,
@@ -79,6 +92,22 @@ pub struct SkillContext {
     pub network_allowed: bool,
     /// Whether to allow file system access
     pub file_access_allowed: bool,
+    /// Optional LLM completer for skills that need AI generation (e.g. PromptOnly)
+    pub llm: Option<std::sync::Arc<dyn LlmCompleter>>,
+}
+
+impl std::fmt::Debug for SkillContext {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("SkillContext")
+            .field("agent_id", &self.agent_id)
+            .field("session_id", &self.session_id)
+            .field("working_dir", &self.working_dir)
+            .field("timeout_secs", &self.timeout_secs)
+            .field("network_allowed", &self.network_allowed)
+            .field("file_access_allowed", &self.file_access_allowed)
+            .field("llm", &self.llm.as_ref().map(|_| "Arc<dyn LlmCompleter>"))
+            .finish()
+    }
 }
 
 impl Default for SkillContext {
@@ -91,6 +120,7 @@ impl Default for SkillContext {
             timeout_secs: 60,
             network_allowed: false,
             file_access_allowed: false,
+            llm: None,
         }
     }
 }