feat(flywheel): 梦核 prompt 注入跨项目高风险 scope 数据段

- risk.rs 新增 high_risk_scopes：样本 ≥5 且有非 CI 返工的 scope
  按返工率降序 top 10，项目名 LEFT JOIN project_profiles（缺失回退 id）
- generate_muhe_prompt 新增「跨项目高风险 scope」表格段，附分析指引
  （反复返工 scope = 结构性脆弱点 → 固化环节输出预防性规范）
- rule_hits 段 pool() 改 try_pool()：原实现测试环境必 panic，
  generate_muhe_prompt 因此不可测；改后两段在无 DB 时走占位文案
- prompt 构建测试断言两个数据段存在 + 聚合排序/最小样本过滤测试，92 全绿

Rules-Applied: R06,R07

.blueprint/modules/flywheel-intelligence.md

@@ -175,8 +175,9 @@
 - files: src-tauri/src/commands/risk.rs, src-tauri/src/commands/mod.rs, src-tauri/src/mcp_tools.rs
 - acceptance: "[MCP tool] predict_task_risk(project_id, scope 或 files) → JSON 含 risk_level/rework_rate/sample_size/confidence/hot_files，无 error 字段；sample_size<5 时 confidence=low；[cargo test] 风险聚合纯函数（conn 注入 + in-memory DB）覆盖高返工 scope、小样本、脏历史（conventional 率低标不可信）三种输入；tools_list_result 含 predict_task_risk 断言"
 
-### 📋 P3-D. 梦核 prompt 高风险 scope 数据段
-- status: todo
+### ✅ P3-D. 梦核 prompt 高风险 scope 数据段
+- status: done
+- done_note: 2026-07-04 完成。risk.rs 新增 high_risk_scopes（样本 ≥5 且有非 CI 返工，返工率降序，项目名从 project_profiles 解析缺失回退 id）；generate_muhe_prompt 注入「跨项目高风险 scope」表格段（含分析指引：反复返工 scope=结构性脆弱点→固化环节输出预防性规范）；顺手把 rule_hits 段的 pool() 改 try_pool()（原实现测试环境必 panic，generate_muhe_prompt 因此不可测）；prompt 构建测试 + 聚合排序过滤测试，92 全绿
 - complexity: S
 - depends: flywheel-intelligence（卡 P3-C）
 - files: src-tauri/src/commands/blueprint.rs

src-tauri/src/commands/blueprint.rs

@@ -1964,9 +1964,9 @@ pub fn generate_muhe_prompt(stats: FlywheelStats) -> String {
     let notes_total = stats.project_notes.len();
 
     // ── 规则命中 × 返工交叉（Rules-Applied trailer，飞轮 R1）─────────────────
-    let rule_hits_summary = crate::db::pool()
-        .get()
-        .ok()
+    // try_pool：测试环境 pool 未初始化时走占位文案，不 panic
+    let rule_hits_summary = crate::db::try_pool()
+        .and_then(|p| p.get().ok())
         .and_then(|conn| crate::commands::commit_metrics::rule_hit_stats(&conn).ok())
         .filter(|v| !v.is_empty())
         .map(|v| {
@@ -1980,6 +1980,22 @@ pub fn generate_muhe_prompt(stats: FlywheelStats) -> String {
         })
         .unwrap_or_else(|| "（暂无数据——commit message 附带 Rules-Applied trailer 后自动积累）".to_string());
 
+    // ── 跨项目高风险 scope（飞轮阶段三预测层，样本 ≥5 且有返工）─────────────
+    let high_risk_summary = crate::db::try_pool()
+        .and_then(|p| p.get().ok())
+        .and_then(|conn| crate::commands::risk::high_risk_scopes(&conn, 5, 10).ok())
+        .filter(|v| !v.is_empty())
+        .map(|v| {
+            let header = "| 项目 | scope | commit 数 | 返工数 | 返工率 |";
+            let sep    = "|------|-------|-----------|--------|--------|";
+            let rows: Vec<String> = v.iter().map(|s| {
+                format!("| {} | {} | {} | {} | {:.0}% |",
+                    s.project_name, s.scope, s.commits, s.rework, s.rework_rate * 100.0)
+            }).collect();
+            format!("{header}\n{sep}\n{}", rows.join("\n"))
+        })
+        .unwrap_or_else(|| "（暂无数据——需 scope 样本 ≥5 且存在非 CI 返工）".to_string());
+
     format!(r#"你是炼境（Dev Manager）的蓝图架构师，负责对飞轮运转数据进行记忆巩固分析。
 
 ## 飞轮运转数据（来自炼境跨项目聚合）
@@ -2027,6 +2043,13 @@ pub fn generate_muhe_prompt(stats: FlywheelStats) -> String {
 
 {rule_hits_summary}
 
+### 跨项目高风险 scope（飞轮阶段三预测层）
+
+> 历史返工率最高的模块热区。反复返工的 scope = 结构性脆弱点，
+> 是「固化」环节输出预防性规范、或提示该模块需要重构/补测试的信号。
+
+{high_risk_summary}
+
 ---
 
 ## 当前 CONVENTIONS.md（v{conventions_version}）规则摘要
@@ -2731,5 +2754,38 @@ mod tests {
 
         let _ = std::fs::remove_dir_all(&dir);
     }
+
+    // ── generate_muhe_prompt（P3-D 梦核数据段）───────────────────────────────
+
+    #[test]
+    fn muhe_prompt_contains_high_risk_scope_section() {
+        let stats = FlywheelStats {
+            cohorts: vec![],
+            top_blocked_reasons: vec![],
+            stalled_projects: vec![],
+            ai_doc_stale_projects: vec![],
+            iteration_jumps: vec![],
+            stalled_modules: vec![],
+            total_projects_scanned: 0,
+            projects_with_data: 0,
+            project_notes: vec![],
+            execution_quality: ExecutionQuality {
+                complexity_dist: Default::default(),
+                total_rework_count: 0,
+                avg_rounds_m: None,
+            },
+        };
+        let prompt = generate_muhe_prompt(stats);
+        assert!(
+            prompt.contains("### 跨项目高风险 scope"),
+            "梦核 prompt 应含高风险 scope 数据段"
+        );
+        assert!(
+            prompt.contains("### 规则命中 × 返工交叉"),
+            "规则置信度数据段应保留"
+        );
+        // 测试环境 pool 未初始化 → 两段都应走占位文案而非 panic
+        assert!(prompt.contains("暂无数据"));
+    }
 }
 

src-tauri/src/commands/risk.rs

@@ -111,6 +111,62 @@ fn file_risks(conn: &Connection, project_id: &str, files: &[String]) -> Result<V
     Ok(result)
 }
 
+/// 跨项目高风险 scope（梦核数据段）。
+#[derive(Debug, Serialize)]
+pub struct HighRiskScope {
+    pub project_name: String,
+    pub scope: String,
+    pub commits: u32,
+    pub rework: u32,
+    pub rework_rate: f64,
+}
+
+/// 跨项目聚合：样本 ≥ min_commits 且至少 1 次返工的 scope，按返工率降序取 top limit。
+/// 项目名从 project_profiles 解析，登记缺失时回退 project_id。
+pub fn high_risk_scopes(
+    conn: &Connection,
+    min_commits: u32,
+    limit: usize,
+) -> Result<Vec<HighRiskScope>, String> {
+    let mut stmt = conn
+        .prepare(
+            "SELECT COALESCE(pp.name, cm.project_id) AS pname, cm.scope,
+                    COUNT(*) AS c,
+                    COALESCE(SUM(CASE WHEN cm.is_rework = 1 AND cm.is_ci_auto = 0 THEN 1 ELSE 0 END), 0) AS rw
+             FROM commit_metrics cm
+             LEFT JOIN project_workspaces pw ON pw.id = cm.project_id
+             LEFT JOIN project_profiles pp ON pp.id = pw.profile_id
+             WHERE cm.scope IS NOT NULL
+             GROUP BY cm.project_id, cm.scope
+             HAVING c >= ?1 AND rw > 0
+             ORDER BY CAST(rw AS REAL) / c DESC, c DESC
+             LIMIT ?2",
+        )
+        .map_err(|e| e.to_string())?;
+    let rows = stmt
+        .query_map(rusqlite::params![min_commits, limit as i64], |r| {
+            Ok((
+                r.get::<_, String>(0)?,
+                r.get::<_, String>(1)?,
+                r.get::<_, u32>(2)?,
+                r.get::<_, u32>(3)?,
+            ))
+        })
+        .map_err(|e| e.to_string())?;
+    let mut result = Vec::new();
+    for row in rows {
+        let (project_name, scope, commits, rework) = row.map_err(|e| e.to_string())?;
+        result.push(HighRiskScope {
+            project_name,
+            scope,
+            rework_rate: rate(rework, commits),
+            commits,
+            rework,
+        });
+    }
+    Ok(result)
+}
+
 /// 核心入口：按 scope 和/或文件清单预测任务风险。
 pub fn predict_task_risk(
     conn: &Connection,
@@ -280,6 +336,27 @@ mod tests {
         assert_eq!(r.risk_level, "high", "hot.rs 返工率 0.5 应分级 high");
     }
 
+    #[test]
+    fn high_risk_scopes_orders_by_rate_with_min_sample() {
+        let conn = crate::db::conn_with_schema();
+        // proj-a/hot：4 commits 2 fix（rate 0.5）；proj-b/warm：5 commits 1 fix（rate 0.2）
+        // proj-a/tiny：2 commits 1 fix——低于 min_commits=3 应被过滤
+        for (sha, subj) in [
+            ("a1", "fix(hot): x"), ("a2", "fix(hot): y"), ("a3", "feat(hot): z"), ("a4", "chore(hot): w"),
+            ("b1", "fix(warm): x"), ("b2", "feat(warm): a"), ("b3", "feat(warm): b"),
+            ("b4", "feat(warm): c"), ("b5", "feat(warm): d"),
+            ("t1", "fix(tiny): x"), ("t2", "feat(tiny): y"),
+        ] {
+            let pid = if sha.starts_with('a') || sha.starts_with('t') { "proj-a" } else { "proj-b" };
+            seed(&conn, pid, sha, subj, &[]);
+        }
+        let top = high_risk_scopes(&conn, 3, 10).unwrap();
+        assert_eq!(top.len(), 2, "tiny 样本不足应被过滤");
+        assert_eq!(top[0].scope, "hot", "返工率高的排前");
+        assert_eq!(top[0].project_name, "proj-a", "无登记项目回退 project_id");
+        assert_eq!(top[1].scope, "warm");
+    }
+
     #[test]
     fn requires_scope_or_files() {
         let conn = crate::db::conn_with_schema();