From 2069f791980d80dc0bb06ba0bfed00716a663fd8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=BB=9D=E5=B0=98?= <237809796@qq.com>
Date: Tue, 16 Jun 2026 20:19:55 +0800
Subject: [PATCH] =?UTF-8?q?=E9=87=8D=E6=9E=84:=20df-ai-core=20trait?=
 =?UTF-8?q?=E4=B8=8B=E6=B2=89=E6=8B=86crate+=E5=AF=BC=E5=85=A5=E5=8E=86?=
 =?UTF-8?q?=E5=8F=B2=E9=A1=B9=E7=9B=AE=E6=89=B9=E9=87=8F=E6=89=AB=E6=8F=8F?=
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---
 Cargo.lock                         |  14 +
 crates/df-ai-core/Cargo.toml       |  11 +
 crates/df-ai-core/src/lib.rs       |  10 +
 crates/df-ai-core/src/provider.rs  | 235 +++++++++++++
 crates/df-ai/Cargo.toml            |   1 +
 crates/df-ai/src/lib.rs            |   3 +
 crates/df-ai/src/provider.rs       | 242 +-------------
 crates/df-ideas/Cargo.toml         |   2 +
 crates/df-ideas/src/adversarial.rs | 117 +++++--
 crates/df-project/src/scan.rs      | 521 ++++++++++++++++++++++++++++-
 docs/todo.md                       |   4 +-
 docs/待审查.md                     |  28 +-
 src-tauri/src/commands/idea.rs     |  39 ++-
 src-tauri/src/commands/project.rs  | 295 +++++++++++++---
 src-tauri/src/lib.rs               |   2 +
 src/api/project.ts                 |  49 +++
 src/i18n/en/projects.ts            |  20 ++
 src/i18n/zh-CN/projects.ts         |  20 ++
 src/views/Projects.vue             | 216 +++++++++++-
 19 files changed, 1518 insertions(+), 311 deletions(-)
 create mode 100644 crates/df-ai-core/Cargo.toml
 create mode 100644 crates/df-ai-core/src/lib.rs
 create mode 100644 crates/df-ai-core/src/provider.rs

diff --git a/Cargo.lock b/Cargo.lock
index cac6fd1..50db673 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -750,6 +750,7 @@ version = "0.1.0"
 dependencies = [
  "anyhow",
  "async-trait",
+ "df-ai-core",
  "df-core",
  "eventsource-stream",
  "futures",
@@ -760,6 +761,17 @@ dependencies = [
  "tracing",
 ]
 
+[[package]]
+name = "df-ai-core"
+version = "0.1.0"
+dependencies = [
+ "anyhow",
+ "async-trait",
+ "futures",
+ "serde",
+ "serde_json",
+]
+
 [[package]]
 name = "df-core"
 version = "0.1.0"
@@ -789,7 +801,9 @@ name = "df-ideas"
 version = "0.1.0"
 dependencies = [
  "anyhow",
+ "async-trait",
  "chrono",
+ "df-ai-core",
  "df-core",
  "serde",
  "serde_json",
diff --git a/crates/df-ai-core/Cargo.toml b/crates/df-ai-core/Cargo.toml
new file mode 100644
index 0000000..288e021
--- /dev/null
+++ b/crates/df-ai-core/Cargo.toml
@@ -0,0 +1,11 @@
+[package]
+name = "df-ai-core"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+serde = { workspace = true }
+serde_json = { workspace = true }
+async-trait = { workspace = true }
+anyhow = { workspace = true }
+futures = "0.3"
diff --git a/crates/df-ai-core/src/lib.rs b/crates/df-ai-core/src/lib.rs
new file mode 100644
index 0000000..edf687d
--- /dev/null
+++ b/crates/df-ai-core/src/lib.rs
@@ -0,0 +1,10 @@
+//! df-ai-core: LLM Provider trait + AI 数据结构（轻量 crate，零 HTTP 依赖）
+//!
+//! 从 df-ai 下沉的全局 AI 接入标准。df-ai 保留 HTTP impl 与业务逻辑
+//! （ContextManager / AiToolRegistry / build_provider 等），通过 re-export
+//! 保持 `df_ai::provider::*` 路径不变。df-ideas 等轻消费方直接依赖本 crate
+//! 的 trait 即可接 LLM，不引入 reqwest / eventsource-stream 等重依赖。
+
+pub mod provider;
+
+pub use provider::*;
diff --git a/crates/df-ai-core/src/provider.rs b/crates/df-ai-core/src/provider.rs
new file mode 100644
index 0000000..84e70ba
--- /dev/null
+++ b/crates/df-ai-core/src/provider.rs
@@ -0,0 +1,235 @@
+//! LLM Provider trait — 统一的 LLM 调用抽象
+//!
+//! 支持 OpenAI 兼容 API（覆盖 OpenAI / GLM / DeepSeek / Claude 兼容模式），
+//! 含 function calling / tool use 能力。
+//!
+//! 本文件仅含 trait + 数据结构定义（零 IO）。HTTP impl（OpenAICompatProvider /
+//! AnthropicCompatProvider）+ 业务逻辑（ContextManager / AiToolRegistry /
+//! build_provider 工厂）留在 df-ai crate。
+
+use std::pin::Pin;
+
+use async_trait::async_trait;
+use futures::Stream;
+use serde::{Deserialize, Serialize};
+
+// ============================================================
+// 核心数据结构
+// ============================================================
+
+/// LLM 调用请求
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct CompletionRequest {
+    /// 模型名称
+    pub model: String,
+    /// 提示消息列表
+    pub messages: Vec<ChatMessage>,
+    /// 温度（0.0 ~ 2.0）
+    pub temperature: Option<f32>,
+    /// 最大生成 token 数
+    pub max_tokens: Option<u32>,
+    /// 是否流式输出
+    pub stream: bool,
+    /// 可调用的工具定义
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tools: Option<Vec<ToolDefinition>>,
+    /// 工具调用策略: "auto" | "none" | {"type":"function","name":"xxx"}
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_choice: Option<serde_json::Value>,
+}
+
+/// 聊天消息
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ChatMessage {
+    pub role: MessageRole,
+    pub content: String,
+    /// 工具调用 ID（role=Tool 时必填）
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_call_id: Option<String>,
+    /// AI 发起的工具调用列表（role=Assistant 时可能有）
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_calls: Option<Vec<ToolCall>>,
+    /// 生成该消息的 model（仅 assistant 消息有，消息级 model 追溯）
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub model: Option<String>,
+    /// 消息状态（UX-09 编辑重生成）：None/"active" 正常可见；
+    /// "truncated" 软删（编辑某条 user 消息后其后续消息标记，保留 DB 可追溯但不进 LLM 上下文、前端视图过滤）
+    /// 默认 None（向前兼容老 JSON 反序列化）。落库随 messages JSON 序列化，无需独立列。
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub status: Option<String>,
+}
+
+impl ChatMessage {
+    pub fn system(content: impl Into<String>) -> Self {
+        Self { role: MessageRole::System, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
+    }
+    pub fn user(content: impl Into<String>) -> Self {
+        Self { role: MessageRole::User, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
+    }
+    pub fn assistant(content: impl Into<String>) -> Self {
+        Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
+    }
+    pub fn assistant_with_tools(content: impl Into<String>, tool_calls: Vec<ToolCall>) -> Self {
+        Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: Some(tool_calls), model: None, status: None }
+    }
+    pub fn tool_result(call_id: impl Into<String>, content: impl Into<String>) -> Self {
+        Self { role: MessageRole::Tool, content: content.into(), tool_call_id: Some(call_id.into()), tool_calls: None, model: None, status: None }
+    }
+
+    /// 是否处于 active 态（status 为 None 或 "active"）。truncated 返回 false。
+    pub fn is_active(&self) -> bool {
+        !matches!(self.status.as_deref(), Some("truncated"))
+    }
+}
+
+/// 消息角色
+#[derive(Debug, Clone, Serialize, Deserialize)]
+#[serde(rename_all = "lowercase")]
+pub enum MessageRole {
+    System,
+    User,
+    Assistant,
+    Tool,
+}
+
+/// 工具定义
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolDefinition {
+    #[serde(rename = "type")]
+    pub tool_type: String,
+    pub function: ToolFunction,
+}
+
+impl ToolDefinition {
+    pub fn function(name: impl Into<String>, description: impl Into<String>, parameters: serde_json::Value) -> Self {
+        Self {
+            tool_type: "function".into(),
+            function: ToolFunction { name: name.into(), description: description.into(), parameters },
+        }
+    }
+}
+
+/// 函数定义
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolFunction {
+    pub name: String,
+    pub description: String,
+    pub parameters: serde_json::Value,
+}
+
+/// 工具调用（AI 发起）
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolCall {
+    pub id: String,
+    #[serde(rename = "type")]
+    pub call_type: String,
+    pub function: ToolCallFunction,
+}
+
+impl ToolCall {
+    pub fn new(id: impl Into<String>, name: impl Into<String>, arguments: impl Into<String>) -> Self {
+        Self {
+            id: id.into(),
+            call_type: "function".into(),
+            function: ToolCallFunction { name: name.into(), arguments: arguments.into() },
+        }
+    }
+}
+
+/// 工具调用函数部分
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolCallFunction {
+    pub name: String,
+    pub arguments: String,
+}
+
+/// LLM 调用响应
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct CompletionResponse {
+    /// 生成的文本
+    pub text: String,
+    /// 使用的模型
+    pub model: String,
+    /// 消耗的 token 数
+    pub usage: TokenUsage,
+    /// AI 发起的工具调用（如有）
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_calls: Option<Vec<ToolCall>>,
+}
+
+/// Token 用量
+#[derive(Debug, Clone, Default, Serialize, Deserialize)]
+pub struct TokenUsage {
+    pub prompt_tokens: u32,
+    pub completion_tokens: u32,
+    pub total_tokens: u32,
+}
+
+/// 流式输出的 chunk
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct StreamChunk {
+    /// 增量文本
+    pub delta: String,
+    /// 是否结束
+    pub finished: bool,
+    /// 工具调用增量（如有）
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_calls: Option<Vec<ToolCallDelta>>,
+    /// Token 用量（流末 chunk 携带，由 provider 解析自 SSE usage 事件）
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub usage: Option<TokenUsage>,
+    /// provider 流式错误事件（如 Anthropic SSE `type=="error"`）。
+    /// 非空表示流中途出错，不应视为正常完成（finished 路径），由 stream_llm 转 AiError。
+    #[serde(skip)]
+    pub error: Option<String>,
+}
+
+/// 工具调用增量（流式中的片段）
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolCallDelta {
+    /// 索引
+    pub index: u32,
+    /// 工具调用 ID（仅第一个 chunk 有）
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub id: Option<String>,
+    /// 函数名片段
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub function_name: Option<String>,
+    /// 函数参数片段
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub function_arguments: Option<String>,
+}
+
+/// 异步流类型别名
+pub type StreamResult = Pin<Box<dyn Stream<Item = anyhow::Result<StreamChunk>> + Send>>;
+
+/// LLM Provider trait
+#[async_trait]
+pub trait LlmProvider: Send + Sync {
+    /// 同步调用
+    async fn complete(&self, request: CompletionRequest) -> anyhow::Result<CompletionResponse>;
+
+    /// 流式调用（返回异步流）
+    async fn stream(
+        &self,
+        request: CompletionRequest,
+    ) -> anyhow::Result<StreamResult>;
+
+    /// 文本嵌入：批量文本 → 语义向量(供知识库向量检索)
+    ///
+    /// 默认实现返回 Err(协议不支持)。OpenAI 兼容协议覆盖实现(/v1/embeddings);
+    /// Anthropic 无 embedding API,保持默认。
+    async fn embed(&self, _model: &str, _texts: Vec<String>) -> anyhow::Result<Vec<Vec<f32>>> {
+        anyhow::bail!("该 Provider 不支持 embedding({})", self.name())
+    }
+
+    /// Provider 名称
+    fn name(&self) -> &str;
+
+    /// 实际请求端点（含 base_url + 关键路径，如 chat completions / messages）。
+    /// 默认回落 `name()`，provider 实现覆盖返真实 URL，供 401/网络错误诊断打印
+    /// —— 旧路径只能近似打印 provider_type，看不到实际请求端点。
+    fn endpoint(&self) -> String {
+        self.name().to_string()
+    }
+}
diff --git a/crates/df-ai/Cargo.toml b/crates/df-ai/Cargo.toml
index 08502f0..7361586 100644
--- a/crates/df-ai/Cargo.toml
+++ b/crates/df-ai/Cargo.toml
@@ -5,6 +5,7 @@ edition = "2021"
 
 [dependencies]
 df-core = { path = "../df-core" }
+df-ai-core = { path = "../df-ai-core" }
 serde = { workspace = true }
 serde_json = { workspace = true }
 tokio = { workspace = true, features = ["sync", "time"] }
diff --git a/crates/df-ai/src/lib.rs b/crates/df-ai/src/lib.rs
index fb4c377..86492da 100644
--- a/crates/df-ai/src/lib.rs
+++ b/crates/df-ai/src/lib.rs
@@ -13,6 +13,9 @@ pub mod retry;
 
 use provider::LlmProvider;
 
+// df-ai-core 直接暴露，供需要直接引用 trait crate 的下游（可选）。
+pub use df_ai_core;
+
 /// 按 provider_type 构建 LLM Provider 实例（统一选择逻辑，消除调用方重复 match）
 ///
 /// `anthropic` 协议走 AnthropicCompatProvider（GLM 订阅端点 / Claude 官方），
diff --git a/crates/df-ai/src/provider.rs b/crates/df-ai/src/provider.rs
index 0e86252..d3794b2 100644
--- a/crates/df-ai/src/provider.rs
+++ b/crates/df-ai/src/provider.rs
@@ -1,231 +1,15 @@
-//! LLM Provider trait — 统一的 LLM 调用抽象
+//! LLM Provider trait + 数据结构 re-export
 //!
-//! 支持 OpenAI 兼容 API（覆盖 OpenAI / GLM / DeepSeek / Claude 兼容模式），
-//! 含 function calling / tool use 能力。
+//! trait 与数据结构定义已下沉到 df-ai-core crate（零 HTTP 依赖，轻消费方
+//! 如 df-ideas 可直接依赖 trait 不引入 reqwest/eventsource-stream）。
+//! 本文件保留为 df-ai 的 re-export 入口，使 `df_ai::provider::*` 路径不变
+//! —— df-ai 内部模块（openai_compat / anthropic_compat / context / ai_tools）
+//! 与外部消费方（df-nodes / src-tauri）的 `use df_ai::provider::LlmProvider`
+//! 等引用全部透明继续可用（编译期验证）。
+//!
+//! 留在 df-ai 的部分：
+//! - `OpenAICompatProvider` / `AnthropicCompatProvider`（HTTP impl，见 openai_compat.rs / anthropic_compat.rs）
+//! - `build_provider()` 工厂（见 lib.rs，按协议选 impl）
+//! - `ContextManager` / `AiToolRegistry` / `StreamCollector`（业务逻辑）
 
-use std::pin::Pin;
-
-use async_trait::async_trait;
-use futures::Stream;
-use serde::{Deserialize, Serialize};
-
-// ============================================================
-// 核心数据结构
-// ============================================================
-
-/// LLM 调用请求
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct CompletionRequest {
-    /// 模型名称
-    pub model: String,
-    /// 提示消息列表
-    pub messages: Vec<ChatMessage>,
-    /// 温度（0.0 ~ 2.0）
-    pub temperature: Option<f32>,
-    /// 最大生成 token 数
-    pub max_tokens: Option<u32>,
-    /// 是否流式输出
-    pub stream: bool,
-    /// 可调用的工具定义
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub tools: Option<Vec<ToolDefinition>>,
-    /// 工具调用策略: "auto" | "none" | {"type":"function","name":"xxx"}
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub tool_choice: Option<serde_json::Value>,
-}
-
-/// 聊天消息
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ChatMessage {
-    pub role: MessageRole,
-    pub content: String,
-    /// 工具调用 ID（role=Tool 时必填）
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub tool_call_id: Option<String>,
-    /// AI 发起的工具调用列表（role=Assistant 时可能有）
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub tool_calls: Option<Vec<ToolCall>>,
-    /// 生成该消息的 model（仅 assistant 消息有，消息级 model 追溯）
-    #[serde(default, skip_serializing_if = "Option::is_none")]
-    pub model: Option<String>,
-    /// 消息状态（UX-09 编辑重生成）：None/"active" 正常可见；
-    /// "truncated" 软删（编辑某条 user 消息后其后续消息标记，保留 DB 可追溯但不进 LLM 上下文、前端视图过滤）
-    /// 默认 None（向前兼容老 JSON 反序列化）。落库随 messages JSON 序列化，无需独立列。
-    #[serde(default, skip_serializing_if = "Option::is_none")]
-    pub status: Option<String>,
-}
-
-impl ChatMessage {
-    pub fn system(content: impl Into<String>) -> Self {
-        Self { role: MessageRole::System, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
-    }
-    pub fn user(content: impl Into<String>) -> Self {
-        Self { role: MessageRole::User, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
-    }
-    pub fn assistant(content: impl Into<String>) -> Self {
-        Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
-    }
-    pub fn assistant_with_tools(content: impl Into<String>, tool_calls: Vec<ToolCall>) -> Self {
-        Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: Some(tool_calls), model: None, status: None }
-    }
-    pub fn tool_result(call_id: impl Into<String>, content: impl Into<String>) -> Self {
-        Self { role: MessageRole::Tool, content: content.into(), tool_call_id: Some(call_id.into()), tool_calls: None, model: None, status: None }
-    }
-
-    /// 是否处于 active 态（status 为 None 或 "active"）。truncated 返回 false。
-    pub fn is_active(&self) -> bool {
-        !matches!(self.status.as_deref(), Some("truncated"))
-    }
-}
-
-/// 消息角色
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(rename_all = "lowercase")]
-pub enum MessageRole {
-    System,
-    User,
-    Assistant,
-    Tool,
-}
-
-/// 工具定义
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ToolDefinition {
-    #[serde(rename = "type")]
-    pub tool_type: String,
-    pub function: ToolFunction,
-}
-
-impl ToolDefinition {
-    pub fn function(name: impl Into<String>, description: impl Into<String>, parameters: serde_json::Value) -> Self {
-        Self {
-            tool_type: "function".into(),
-            function: ToolFunction { name: name.into(), description: description.into(), parameters },
-        }
-    }
-}
-
-/// 函数定义
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ToolFunction {
-    pub name: String,
-    pub description: String,
-    pub parameters: serde_json::Value,
-}
-
-/// 工具调用（AI 发起）
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ToolCall {
-    pub id: String,
-    #[serde(rename = "type")]
-    pub call_type: String,
-    pub function: ToolCallFunction,
-}
-
-impl ToolCall {
-    pub fn new(id: impl Into<String>, name: impl Into<String>, arguments: impl Into<String>) -> Self {
-        Self {
-            id: id.into(),
-            call_type: "function".into(),
-            function: ToolCallFunction { name: name.into(), arguments: arguments.into() },
-        }
-    }
-}
-
-/// 工具调用函数部分
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ToolCallFunction {
-    pub name: String,
-    pub arguments: String,
-}
-
-/// LLM 调用响应
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct CompletionResponse {
-    /// 生成的文本
-    pub text: String,
-    /// 使用的模型
-    pub model: String,
-    /// 消耗的 token 数
-    pub usage: TokenUsage,
-    /// AI 发起的工具调用（如有）
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub tool_calls: Option<Vec<ToolCall>>,
-}
-
-/// Token 用量
-#[derive(Debug, Clone, Default, Serialize, Deserialize)]
-pub struct TokenUsage {
-    pub prompt_tokens: u32,
-    pub completion_tokens: u32,
-    pub total_tokens: u32,
-}
-
-/// 流式输出的 chunk
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct StreamChunk {
-    /// 增量文本
-    pub delta: String,
-    /// 是否结束
-    pub finished: bool,
-    /// 工具调用增量（如有）
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub tool_calls: Option<Vec<ToolCallDelta>>,
-    /// Token 用量（流末 chunk 携带，由 provider 解析自 SSE usage 事件）
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub usage: Option<TokenUsage>,
-    /// provider 流式错误事件（如 Anthropic SSE `type=="error"`）。
-    /// 非空表示流中途出错，不应视为正常完成（finished 路径），由 stream_llm 转 AiError。
-    #[serde(skip)]
-    pub error: Option<String>,
-}
-
-/// 工具调用增量（流式中的片段）
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ToolCallDelta {
-    /// 索引
-    pub index: u32,
-    /// 工具调用 ID（仅第一个 chunk 有）
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub id: Option<String>,
-    /// 函数名片段
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub function_name: Option<String>,
-    /// 函数参数片段
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub function_arguments: Option<String>,
-}
-
-/// 异步流类型别名
-pub type StreamResult = Pin<Box<dyn Stream<Item = anyhow::Result<StreamChunk>> + Send>>;
-
-/// LLM Provider trait
-#[async_trait]
-pub trait LlmProvider: Send + Sync {
-    /// 同步调用
-    async fn complete(&self, request: CompletionRequest) -> anyhow::Result<CompletionResponse>;
-
-    /// 流式调用（返回异步流）
-    async fn stream(
-        &self,
-        request: CompletionRequest,
-    ) -> anyhow::Result<StreamResult>;
-
-    /// 文本嵌入：批量文本 → 语义向量(供知识库向量检索)
-    ///
-    /// 默认实现返回 Err(协议不支持)。OpenAI 兼容协议覆盖实现(/v1/embeddings);
-    /// Anthropic 无 embedding API,保持默认。
-    async fn embed(&self, _model: &str, _texts: Vec<String>) -> anyhow::Result<Vec<Vec<f32>>> {
-        anyhow::bail!("该 Provider 不支持 embedding({})", self.name())
-    }
-
-    /// Provider 名称
-    fn name(&self) -> &str;
-
-    /// 实际请求端点（含 base_url + 关键路径，如 chat completions / messages）。
-    /// 默认回落 `name()`，provider 实现覆盖返真实 URL，供 401/网络错误诊断打印
-    /// —— 旧路径只能近似打印 provider_type，看不到实际请求端点。
-    fn endpoint(&self) -> String {
-        self.name().to_string()
-    }
-}
+pub use df_ai_core::provider::*;
diff --git a/crates/df-ideas/Cargo.toml b/crates/df-ideas/Cargo.toml
index 8c0fde5..69a6828 100644
--- a/crates/df-ideas/Cargo.toml
+++ b/crates/df-ideas/Cargo.toml
@@ -5,9 +5,11 @@ edition = "2021"
 
 [dependencies]
 df-core = { path = "../df-core" }
+df-ai-core = { path = "../df-ai-core" }
 serde = { workspace = true }
 serde_json = { workspace = true }
 tokio = { workspace = true }
+async-trait = { workspace = true }
 anyhow = { workspace = true }
 chrono = { workspace = true }
 tracing = { workspace = true }
diff --git a/crates/df-ideas/src/adversarial.rs b/crates/df-ideas/src/adversarial.rs
index be5bf96..d45d281 100644
--- a/crates/df-ideas/src/adversarial.rs
+++ b/crates/df-ideas/src/adversarial.rs
@@ -1,15 +1,40 @@
 //! 对抗式评估系统 — 正反方辩论 + AI 分析师
 //!
-//! 当前为基于评分与内容信号的启发式实现（稳定、有区分度）。
-//! TODO: 接入 df-ai LlmProvider 让正反方论点由 LLM 生成，启发式降级为 fallback。
+//! 双轨实现：
+//! - **启发式**（默认/降级）：基于评分与内容信号生成正反方论点，稳定有区分度。
+//! - **LLM**（注入 provider 后）：调一次 `complete()` 让论点由 LLM 生成，失败自动降级启发式。
+//!
+//! 评估来源由 [`EvaluatedBy`] 三态标记：`Llm`（LLM 深度评估）/ `Heuristic`（主动选启发式，
+//! 无 provider）/ `HeuristicFallback`（LLM 调用失败降级）。前端可据此显示评估深度标签。
+//!
+//! LLM prompt 构造与 JSON 解析在 F-260614-03（已由本任务解锁）接入，当前 `evaluate_with_llm`
+//! 返回 Err 触发降级路径——机制完整，仅缺 prompt/解析实现。
+
+use std::sync::Arc;
 
 use anyhow::Result;
 use serde::{Deserialize, Serialize};
 
+use df_ai_core::provider::LlmProvider;
 use df_core::types::{IdeaId, Priority};
 use crate::capture::Idea;
 use crate::scoring::IdeaScores;
 
+/// 评估来源标记
+///
+/// `Default = Heuristic`：老数据（F-07 之前）序列化时无 evaluated_by 字段，
+/// 反序列化回落启发式（与 F-07 之前行为一致）。
+#[derive(Debug, Clone, Default, Serialize, Deserialize, PartialEq, Eq)]
+pub enum EvaluatedBy {
+    /// LLM 深度评估
+    Llm,
+    /// 启发式评估（无 LLM 配置时的默认模式，也是老数据反序列化默认值）
+    #[default]
+    Heuristic,
+    /// 启发式降级（LLM 调用失败后 fallback）
+    HeuristicFallback,
+}
+
 /// 对抗评估结果
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct AdversarialEval {
@@ -19,6 +44,9 @@ pub struct AdversarialEval {
     pub analyst: AnalystAnalysis,
     pub final_score: f64,
     pub recommendation: Recommendation,
+    /// 评估来源（Llm / Heuristic / HeuristicFallback），前端据此显示评估深度标签
+    #[serde(default)]
+    pub evaluated_by: EvaluatedBy,
 }
 
 /// 论点（正方/反方共用同一结构）
@@ -62,19 +90,64 @@ pub enum Recommendation {
 }
 
 /// 对抗评估引擎
-pub struct AdversarialEngine;
+pub struct AdversarialEngine {
+    /// 可选 LLM provider。Some → 优先 LLM 评估（失败降级启发式）；None → 纯启发式。
+    /// 构造注入（与 IdeaPromoter::new(policy) 同一模式），批量评估复用同一 provider。
+    provider: Option<Arc<dyn LlmProvider>>,
+}
 
 impl AdversarialEngine {
-    /// 执行完整的对抗评估
-    #[allow(clippy::unused_async)] // 签名保留 async,待接 LLM 注入异步调用
-    pub async fn evaluate(idea: &Idea) -> Result<AdversarialEval> {
+    /// 注入 LLM provider 构造（provider Some 时走 LLM，调用失败自动降级启发式）
+    pub fn new(provider: Arc<dyn LlmProvider>) -> Self {
+        Self { provider: Some(provider) }
+    }
+
+    /// 纯启发式构造（无 LLM 配置时的默认模式）
+    pub fn heuristic() -> Self {
+        Self { provider: None }
+    }
+
+    /// 执行完整的对抗评估（内部按 provider 有无调度 LLM / 启发式，失败降级）
+    pub async fn evaluate(&self, idea: &Idea) -> Result<AdversarialEval> {
+        match &self.provider {
+            Some(p) => match self.evaluate_with_llm(idea, p).await {
+                Ok(mut eval) => {
+                    eval.evaluated_by = EvaluatedBy::Llm;
+                    Ok(eval)
+                }
+                Err(e) => {
+                    // LLM 调用失败/超时/格式异常 → 自动降级启发式，保证前端结构完整返回
+                    tracing::warn!("LLM 对抗评估失败, 降级到启发式: {e}");
+                    let mut eval = self.evaluate_heuristic(idea)?;
+                    eval.evaluated_by = EvaluatedBy::HeuristicFallback;
+                    Ok(eval)
+                }
+            },
+            None => {
+                let mut eval = self.evaluate_heuristic(idea)?;
+                eval.evaluated_by = EvaluatedBy::Heuristic;
+                Ok(eval)
+            }
+        }
+    }
+
+    /// LLM 对抗评估（注入 provider 后走此路）。
+    ///
+    /// prompt 构造 + JSON 解析在 F-260614-03（已由本任务解锁）接入。当前返回 Err
+    /// 触发降级路径——降级机制与启发式评估路径完整，仅缺 LLM 调用实现。
+    async fn evaluate_with_llm(&self, _idea: &Idea, _provider: &Arc<dyn LlmProvider>) -> Result<AdversarialEval> {
+        anyhow::bail!("LLM 对抗评估尚未实现(F-260614-03)")
+    }
+
+    /// 启发式评估（基于评分与内容信号，稳定有区分度）
+    fn evaluate_heuristic(&self, idea: &Idea) -> Result<AdversarialEval> {
         // 先做多维评分，作为正反方论点与置信度的依据
         let scores = crate::scoring::ScoringEngine::compute_default(idea);
 
-        let positive = Self::generate_positive_argument(idea, &scores)?;
-        let negative = Self::generate_negative_argument(idea, &scores)?;
-        let analyst = Self::analyst_analysis(idea, &scores)?;
-        let recommendation = Self::recommendation_for(&analyst.final_assessment);
+        let positive = self.generate_positive_argument(idea, &scores)?;
+        let negative = self.generate_negative_argument(idea, &scores)?;
+        let analyst = self.analyst_analysis(idea, &scores)?;
+        let recommendation = self.recommendation_for(&analyst.final_assessment);
 
         Ok(AdversarialEval {
             idea_id: idea.id.clone(),
@@ -83,12 +156,14 @@ impl AdversarialEngine {
             analyst,
             final_score: scores.overall,
             recommendation,
+            // 由 evaluate() 调用方按调度路径覆盖（Heuristic / HeuristicFallback）
+            evaluated_by: EvaluatedBy::Heuristic,
         })
     }
 
     /// 生成正方观点（支持执行）— confidence 由可行性 + 影响力驱动
     /// 注:返回 Result 为后续 LLM 注入失败预留,启发式阶段恒 Ok
-    fn generate_positive_argument(idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
+    fn generate_positive_argument(&self, idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
         let desc = idea.description.trim();
         let mut evidence = Vec::new();
         evidence.push(format!("优先级：{}", priority_label(&idea.priority)));
@@ -126,7 +201,7 @@ impl AdversarialEngine {
     }
 
     /// 生成反方观点（反对或谨慎）— 论点基于想法实际缺陷，confidence 随风险上升
-    fn generate_negative_argument(idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
+    fn generate_negative_argument(&self, idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
         let desc = idea.description.trim();
         let mut evidence = Vec::new();
         if desc.is_empty() {
@@ -166,7 +241,7 @@ impl AdversarialEngine {
     }
 
     /// AI 分析师综合分析 — 评估等级由综合评分决定，优势/劣势按维度动态生成
-    fn analyst_analysis(idea: &Idea, scores: &IdeaScores) -> Result<AnalystAnalysis> {
+    fn analyst_analysis(&self, idea: &Idea, scores: &IdeaScores) -> Result<AnalystAnalysis> {
         let final_assessment = match scores.overall {
             x if x >= 7.5 => AssessmentLevel::StrongGo,
             x if x >= 6.0 => AssessmentLevel::Recommended,
@@ -234,7 +309,7 @@ impl AdversarialEngine {
     }
 
     /// 评估等级 → 最终建议
-    fn recommendation_for(level: &AssessmentLevel) -> Recommendation {
+    fn recommendation_for(&self, level: &AssessmentLevel) -> Recommendation {
         match level {
             AssessmentLevel::StrongGo => Recommendation::ImmediateAction,
             AssessmentLevel::Recommended => Recommendation::Soon,
@@ -302,7 +377,7 @@ mod tests {
         let desc = "面向用户的核心功能，带来显著增长，大幅提升效率。集成成熟方案，复用已有组件。".repeat(3);
         let idea = make_idea("AI增长引擎", &desc, Priority::Critical, vec!["增长", "核心"]);
         let scores = ScoringEngine::compute_default(&idea);
-        let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
+        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
         println!("\n[a1] 高分想法 → 期望 ImmediateAction");
         println!("  scores: feas={:.2} impact={:.2} urg={:.2} overall={:.2}", scores.feasibility, scores.impact, scores.urgency, scores.overall);
         println!("  eval: final_score={:.2} recommendation={:?}", eval.final_score, eval.recommendation);
@@ -316,7 +391,7 @@ mod tests {
         let desc = "面向用户的功能，集成已有方案，提升体验".to_string();
         let idea = make_idea("体验优化", &desc, Priority::Medium, vec!["体验"]);
         let scores = ScoringEngine::compute_default(&idea);
-        let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
+        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
         println!("\n[a2] 中分想法 → 期望 Soon");
         println!("  scores overall={:.2}  eval final_score={:.2}  recommendation={:?}", scores.overall, eval.final_score, eval.recommendation);
         assert_eq!(eval.recommendation, Recommendation::Soon);
@@ -327,7 +402,7 @@ mod tests {
         let desc = "重构迁移大规模分布式重写从零全新架构高并发底层".to_string();
         let idea = make_idea("过度工程", &desc, Priority::Low, vec![]);
         let scores = ScoringEngine::compute_default(&idea);
-        let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
+        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
         println!("\n[a3] 低分想法 → 期望 Monitor");
         println!("  scores overall={:.2}  eval final_score={:.2}  recommendation={:?}", scores.overall, eval.final_score, eval.recommendation);
         assert!(eval.final_score < 3.0, "final_score 应<3.0, 实际 {:.2}", eval.final_score);
@@ -337,7 +412,7 @@ mod tests {
     #[tokio::test]
     async fn a4_confidence_ranges() {
         let idea = make_idea("普通想法", "一般描述", Priority::Medium, vec!["标签"]);
-        let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
+        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
         println!("\n[a4] confidence 区间校验");
         println!("  正方={:.2} (应∈[0.1, 0.95])   反方={:.2} (应∈[0.1, 0.9])", eval.positive.confidence, eval.negative.confidence);
         assert!(eval.positive.confidence >= 0.1 && eval.positive.confidence <= 0.95);
@@ -347,7 +422,7 @@ mod tests {
     #[tokio::test]
     async fn a5_positive_thesis_contains_title() {
         let idea = make_idea("独家创意", "描述内容", Priority::High, vec![]);
-        let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
+        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
         println!("\n[a5] 正方论点含标题");
         println!("  thesis: {}", eval.positive.thesis);
         assert!(eval.positive.thesis.contains("独家创意"), "正方 thesis 应含标题");
@@ -356,7 +431,7 @@ mod tests {
     #[tokio::test]
     async fn a6_negative_evidence_nonempty() {
         let idea = make_idea("待质疑想法", "短", Priority::Low, vec![]);
-        let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
+        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
         println!("\n[a6] 反方证据非空 ({} 条)", eval.negative.evidence.len());
         for (i, e) in eval.negative.evidence.iter().enumerate() {
             println!("  证据{}: {}", i + 1, e);
@@ -369,7 +444,7 @@ mod tests {
         let desc = "面向用户的核心功能".to_string();
         let idea = make_idea("一致性测试", &desc, Priority::High, vec!["核心"]);
         let scores = ScoringEngine::compute_default(&idea);
-        let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
+        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
         println!("\n[a7] final_score == scores.overall 一致性");
         println!("  scores.overall={:.2}  eval.final_score={:.2}", scores.overall, eval.final_score);
         println!("  analyst.summary: {}", eval.analyst.summary);
diff --git a/crates/df-project/src/scan.rs b/crates/df-project/src/scan.rs
index 847d96d..ae02deb 100644
--- a/crates/df-project/src/scan.rs
+++ b/crates/df-project/src/scan.rs
@@ -92,6 +92,157 @@ pub fn detect_stack(root: &Path) -> Result<Vec<String>> {
     Ok(stack)
 }
 
+// ============================================================
+// 历史项目发现 — monorepo 识别 + 子项目展开(纯规则,不跑 LLM)
+// ============================================================
+
+/// monorepo 工作区配置文件名(JS 生态主流:pnpm/lerna/turbo/nx)
+const MONOREPO_MARKERS: &[&str] = &[
+    "pnpm-workspace.yaml",
+    "lerna.json",
+    "turbo.json",
+    "nx.json",
+];
+
+/// 判定目录是否为 monorepo 根(JS 生态主流工作区管理器)。
+///
+/// 命中任一即视为 monorepo:
+/// - pnpm-workspace.yaml / lerna.json / turbo.json / nx.json 存在
+/// - package.json 含 `workspaces` 字段(npm/yarn workspaces)
+pub fn is_monorepo(root: &Path) -> bool {
+    if !root.is_dir() {
+        return false;
+    }
+    for marker in MONOREPO_MARKERS {
+        if root.join(marker).is_file() {
+            return true;
+        }
+    }
+    // package.json workspaces 字段(npm/yarn)
+    let pkg_path = root.join("package.json");
+    if pkg_path.is_file() {
+        if let Ok(content) = std::fs::read_to_string(&pkg_path) {
+            if let Ok(pkg) = serde_json::from_str::<serde_json::Value>(&content) {
+                if pkg.get("workspaces").is_some() {
+                    return true;
+                }
+            }
+        }
+    }
+    false
+}
+
+/// 单个发现的候选项目(monorepo 子项目或独立项目)
+#[derive(Debug, Clone)]
+pub struct DiscoveredProject {
+    /// 项目根目录绝对路径
+    pub path: String,
+    /// 推断的项目名(目录名)
+    pub name: String,
+    /// 规则探测的技术栈(空=未识别)
+    pub stack: Vec<String>,
+    /// 是否为 monorepo 根(便于前端标记)
+    pub is_monorepo: bool,
+}
+
+/// 在指定根目录下发现候选项目。
+///
+/// 策略(只展开一层,不做深递归):
+/// 1. 根目录本身有项目标志(Cargo.toml/package.json/go.mod 等)→ 根为独立项目
+/// 2. 根目录是 monorepo → 展开 packages/\*/apps/\* 直接子目录(各子目录跑 detect_stack 过滤空)
+/// 3. 否则:扫根的直接子目录,凡 detect_stack 非空的视为候选项目
+///
+/// 不跑 LLM(快),不读源码。空 stack 的目录在 monorepo 展开/子目录扫描时被过滤。
+pub fn discover_projects(root: &Path) -> Result<Vec<DiscoveredProject>> {
+    if !root.is_dir() {
+        anyhow::bail!("路径不是目录: {}", root.display());
+    }
+
+    let mut out: Vec<DiscoveredProject> = Vec::new();
+    let mono = is_monorepo(root);
+
+    // 1. 根目录自身是项目(有 manifest 标志)
+    if has_project_manifest(root) {
+        let stack = detect_stack(root).unwrap_or_default();
+        out.push(DiscoveredProject {
+            path: root.to_string_lossy().to_string(),
+            name: root
+                .file_name()
+                .and_then(|n| n.to_str())
+                .map(|s| s.to_string())
+                .unwrap_or_else(|| root.to_string_lossy().to_string()),
+            stack,
+            is_monorepo: mono,
+        });
+    }
+
+    // 2. monorepo → 展开 packages/* apps/* 直接子目录
+    // 3. 普通目录 → 扫直接子目录,凡 detect_stack 非空的入选
+    let scan_globs: &[&str] = if mono {
+        &["packages", "apps"]
+    } else {
+        &[""]
+    };
+
+    for glob in scan_globs {
+        let target = if glob.is_empty() {
+            root.to_path_buf()
+        } else {
+            root.join(glob)
+        };
+        if !target.is_dir() {
+            continue;
+        }
+        let Ok(entries) = std::fs::read_dir(&target) else {
+            continue;
+        };
+        for e in entries.flatten() {
+            let p = e.path();
+            if !p.is_dir() {
+                continue;
+            }
+            let name = e.file_name().to_string_lossy().to_string();
+            if SAMPLE_IGNORED_DIRS.contains(&name.as_str()) || name.starts_with('.') {
+                continue;
+            }
+            // 必须有项目标志 + detect_stack 非空
+            if !has_project_manifest(&p) {
+                continue;
+            }
+            let stack = match detect_stack(&p) {
+                Ok(s) => s,
+                Err(_) => continue,
+            };
+            if stack.is_empty() {
+                continue;
+            }
+            out.push(DiscoveredProject {
+                path: p.to_string_lossy().to_string(),
+                name,
+                stack,
+                is_monorepo: false,
+            });
+        }
+    }
+
+    Ok(out)
+}
+
+/// 目录是否含任一项目清单标志文件
+fn has_project_manifest(dir: &Path) -> bool {
+    const MARKS: &[&str] = &[
+        "Cargo.toml",
+        "package.json",
+        "go.mod",
+        "pyproject.toml",
+        "requirements.txt",
+        "pom.xml",
+        "build.gradle",
+        "build.gradle.kts",
+    ];
+    MARKS.iter().any(|m| dir.join(m).is_file()) || has_file_with_ext(dir, "csproj")
+}
+
 /// 解析 package.json,合并 dependencies + devDependencies 的包名
 fn read_package_deps(path: impl AsRef<Path>) -> Result<Vec<String>> {
     let content = std::fs::read_to_string(path.as_ref())
@@ -132,8 +283,8 @@ fn has_file_with_ext(dir: &Path, ext: &str) -> bool {
 /// 首段定义:跳过开头标题行(# / ## …)、空行、HTML 注释与 badge 图片/HTML 行等噪声,
 /// 取首个含实质文本的段落(连续多行直到空行);按字符截断至 200 字避免超长。
 pub fn extract_description(root: &Path) -> Option<String> {
-    // 复用 read_readme 的查找逻辑(支持 README.md / README.zh.md 等变体)
-    let content = read_readme(root)?;
+    // 复用 read_readme_raw 的查找逻辑(支持 README.md / README.zh.md 等变体)
+    let content = read_readme_raw(root)?;
     let mut text = String::new();
     let mut started = false;
     for raw_line in content.lines() {
@@ -171,16 +322,28 @@ pub fn extract_description(root: &Path) -> Option<String> {
 /// extract_description 最大字符数
 const EXTRACT_DESC_MAX: usize = 200;
 
-/// 项目采样结果 — README 首段 + 目录树(2层) + 清单文件片段
+/// 内容图引用(README 内的架构图/截图等,喂 vision 用)。
+/// 采样层只收集 alt+src,Phase 2 上线后由 commands 层读 base64 喂 vision。
+/// 当前 ChatMessage.content:String(F-260614-05 未做)走纯文本降级,
+/// 此结构仅为采样层留接口,不读 base64。
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct ImageRef {
+    pub alt: String,
+    pub src: String,
+}
+
+/// 项目采样结果 — README(剥噪音后) + 目录树(2层) + 清单文件片段 + 内容图引用
 #[derive(Debug, Clone)]
 pub struct ProjectSample {
     pub readme: Option<String>,
     pub tree: Vec<String>,
     /// (文件名, 截断内容)
     pub manifests: Vec<(String, String)>,
+    /// README 内的内容图引用(架构图/截图等,跳徽章)
+    pub images: Vec<ImageRef>,
 }
 
-const SAMPLE_README_MAX: usize = 2000;
+const SAMPLE_README_MAX: usize = 8000;
 const SAMPLE_MANIFEST_MAX: usize = 1500;
 const SAMPLE_TREE_MAX: usize = 80;
 /// 目录树过滤的噪音目录(依赖产物/构建/缓存/IDE)
@@ -190,30 +353,260 @@ const SAMPLE_IGNORED_DIRS: &[&str] = &[
     ".turbo", ".angular", ".gradle", "vendor",
 ];
 
-/// 采集项目采样(README + 目录树 + 清单),供 LLM 分析填基础信息
+/// 采集项目采样(README + 目录树 + 清单 + 内容图),供 LLM 分析填基础信息
 pub fn collect_sample(root: &Path) -> Result<ProjectSample> {
     if !root.is_dir() {
         anyhow::bail!("路径不是目录: {}", root.display());
     }
+    let raw = read_readme_raw(root);
+    let (readme, images) = match raw {
+        Some(text) => {
+            let images = collect_images(&text);
+            let cleaned = strip_readme_noise(&text);
+            if cleaned.trim().is_empty() {
+                (None, images)
+            } else {
+                (Some(truncate_chars(&cleaned, SAMPLE_README_MAX)), images)
+            }
+        }
+        None => (None, Vec::new()),
+    };
     Ok(ProjectSample {
-        readme: read_readme(root),
+        readme,
         tree: collect_tree(root),
         manifests: collect_manifests(root),
+        images,
     })
 }
 
-fn read_readme(root: &Path) -> Option<String> {
+/// 读 README 原始内容(不做处理)
+fn read_readme_raw(root: &Path) -> Option<String> {
     for name in &["README.md", "README.MD", "README", "README.zh.md", "README_zh.md", "README_EN.md", "readme.md"] {
         let p = root.join(name);
         if p.is_file() {
             if let Ok(content) = std::fs::read_to_string(&p) {
-                return Some(truncate_chars(&content, SAMPLE_README_MAX));
+                return Some(content);
             }
         }
     }
     None
 }
 
+/// 徽章图域名(纯徽章图,跳过不喂 LLM)
+const BADGE_HOSTS: &[&str] = &[
+    "img.shields.io",
+    "shields.io",
+    "badge.fury.io",
+    "badgen.net",
+    "badges.frapsoft.com",
+    "github.com/workflows", // GitHub Actions 工作流状态徽章
+    "travis-ci.org",
+    "coveralls.io",
+    "codecov.io",
+    "app.codacy.com",
+    "david-dm.org",
+    "circleci.com",
+    "ci.appveyor.com",
+];
+
+/// 徽章关键词(图 alt/src 含这些视为纯徽章图)
+const BADGE_KEYWORDS: &[&str] = &[
+    "build",
+    "version",
+    "license",
+    "coverage",
+    "downloads",
+    "download",
+    "npm",
+    "pypi",
+    "crates.io",
+    "codeclimate",
+    "maintainability",
+    "stars",
+    "forks",
+    "issues",
+    "contributors",
+    "dependencies",
+    "devdependencies",
+    "circleci",
+    "travis",
+    "appveyor",
+    "coveralls",
+    "codecov",
+];
+
+/// 判定 markdown 图片 `![alt](src)` 是否为纯徽章(架构图/截图等保留)
+fn is_badge_image(alt: &str, src: &str) -> bool {
+    // 域名命中(shields.io / badge.fury / GitHub Actions 等)
+    let src_lower = src.to_lowercase();
+    if BADGE_HOSTS.iter().any(|h| src_lower.contains(h)) {
+        return true;
+    }
+    // 关键词命中(alt 或 src 含 build/version/license/coverage 等)
+    let hay = format!("{alt} {src}").to_lowercase();
+    if BADGE_KEYWORDS.iter().any(|k| hay.contains(k)) {
+        return true;
+    }
+    false
+}
+
+/// 从 README 收集内容图(跳徽章),保留 markdown 原样引用
+fn collect_images(content: &str) -> Vec<ImageRef> {
+    let mut out = Vec::new();
+    let mut seen = std::collections::HashSet::new();
+    // 匹配 ![alt](src) —— 简单行级扫描,够用且无 regex 依赖
+    for line in content.lines() {
+        let mut rest = line;
+        while let Some(start) = rest.find("![") {
+            let after_bracket = &rest[start + 2..];
+            let Some(alt_end) = after_bracket.find("](") else { break };
+            let alt = after_bracket[..alt_end].to_string();
+            let after_paren = &after_bracket[alt_end + 2..];
+            let Some(paren_end) = after_paren.find(')') else { break };
+            let src = after_paren[..paren_end].to_string();
+            rest = &after_paren[paren_end + 1..];
+            if is_badge_image(&alt, &src) {
+                continue;
+            }
+            // 同 src 去重
+            if seen.insert(src.clone()) {
+                out.push(ImageRef { alt, src });
+            }
+            if out.len() >= 20 {
+                return out;
+            }
+        }
+    }
+    out
+}
+
+/// 剥 README 噪音:frontmatter(YAML/TOML 块) / HTML 注释 / TOC(纯链接目录行) /
+/// 纯徽章图片行 / 纯徽章 HTML 行。保留标题、正文、内容图 markdown 原样。
+fn strip_readme_noise(content: &str) -> String {
+    let mut out = Vec::new();
+    let mut lines = content.lines().peekable();
+    while let Some(line) = lines.next() {
+        let trimmed = line.trim();
+        // ── frontmatter 块(YAML `---` / TOML `+++`)──
+        if trimmed == "---" || trimmed == "+++" {
+            // 开头处的 frontmatter:跳到下一个匹配分隔符
+            let delim = trimmed;
+            let mut consumed_any = false;
+            for next in lines.by_ref() {
+                consumed_any = true;
+                if next.trim() == delim {
+                    break;
+                }
+            }
+            let _ = consumed_any;
+            continue;
+        }
+        // ── HTML 注释块(<!-- ... -->,可能跨行)──
+        if trimmed.starts_with("<!--") {
+            if trimmed.contains("-->") {
+                // 单行注释,整行跳过
+                continue;
+            }
+            // 多行:吃到含 --> 的行
+            for next in lines.by_ref() {
+                if next.contains("-->") {
+                    break;
+                }
+            }
+            continue;
+        }
+        // ── 纯徽章行:整行只剩图片/HTML 徽章(可能多个 ![..](..) 连排)──
+        if is_pure_badge_line(trimmed) {
+            continue;
+        }
+        // ── TOC:纯目录行(整行只有 [..](#anchor) 锚点链接,或 markdown 列表项仅锚点)──
+        if is_toc_line(trimmed) {
+            continue;
+        }
+        out.push(line.to_string());
+    }
+    out.join("\n")
+}
+
+/// 判定整行是否为纯徽章行(仅含图片/HTML badge,无其它实质文本)。
+/// 如 `![build](https://img.shields.io/x) ![license](...) <a href="..."><img.../></a>`
+fn is_pure_badge_line(line: &str) -> bool {
+    if line.is_empty() {
+        return false;
+    }
+    // 仅含图片语法/HTML 标签/空白/[alt] 片段,且至少有一个图片或 HTML img 标签
+    let mut content_found = false;
+    let mut text_only: String = String::new();
+    let mut rest = line;
+    loop {
+        // 找下一个 ![ 或 <img 或 <a
+        let img_md = rest.find("![");
+        let img_html = rest.to_lowercase().find("<img");
+        let anchor_html = rest.to_lowercase().find("<a ");
+        let earliest = [img_md, img_html, anchor_html]
+            .into_iter()
+            .flatten()
+            .min();
+        let Some(pos) = earliest else {
+            // 剩余纯文本
+            text_only.push_str(rest);
+            break;
+        };
+        // pos 之前的文本进 text_only
+        text_only.push_str(&rest[..pos]);
+        let suffix = &rest[pos..];
+        let consumed = if suffix.starts_with("![") {
+            // markdown 图片:吃到 )
+            if let Some(end) = suffix.find(')') {
+                let alt = &suffix[2..suffix.find("](").unwrap_or(end)];
+                let src = &suffix[suffix.find("](").map(|i| i + 2).unwrap_or(end)..end];
+                if !is_badge_image(alt, src) {
+                    // 内容图混在行里 → 非纯徽章行
+                    return false;
+                }
+                content_found = true;
+                end + 1
+            } else {
+                // 不闭合,当普通文本
+                return false;
+            }
+        } else {
+            // HTML <img 或 <a 标签:视为徽章载体(HTML 行常见于此)
+            // 找 > 闭合
+            if let Some(end) = suffix.find('>') {
+                content_found = true;
+                end + 1
+            } else {
+                return false;
+            }
+        };
+        rest = &suffix[consumed..];
+    }
+    // 剩余 text_only 必须为空或纯空白/标点
+    let residual = text_only
+        .chars()
+        .filter(|c| !c.is_whitespace() && *c != '|' && *c != '-')
+        .collect::<String>();
+    content_found && residual.is_empty()
+}
+
+/// 判定 TOC 行:markdown 列表项仅含锚点链接 `- [..](#..)` 或整行仅锚点链接
+fn is_toc_line(line: &str) -> bool {
+    if line.is_empty() {
+        return false;
+    }
+    let t = line.trim_start_matches(['-', '*', '+', ' ']);
+    // 形如 [text](#anchor) 或 [text](#anchor "title")
+    if !(t.starts_with('[') && t.contains("](")) {
+        return false;
+    }
+    // 取 ]( 后到行尾或空格,须以 # 开头
+    let Some(close) = t.find("](") else { return false };
+    let after = &t[close + 2..];
+    let target = after.trim_end_matches(')').split_whitespace().next().unwrap_or("");
+    target.starts_with('#')
+}
+
 /// 目录树(根 + 一层子目录),过滤噪音目录,控条目数
 fn collect_tree(root: &Path) -> Vec<String> {
     let mut lines = Vec::new();
@@ -344,14 +737,25 @@ mod tests {
     #[test]
     fn collects_sample() {
         let d = scratch("sample");
-        fs::write(d.join("README.md"), "# Test\nA test project.\nMore.").unwrap();
+        fs::write(
+            d.join("README.md"),
+            "# Test\n\n![badge](https://img.shields.io/badge/x-y-green)\n\n![arch](./docs/arch.png)\n\nA test project.\nMore.",
+        )
+        .unwrap();
         fs::write(d.join("package.json"), r#"{"name":"x","dependencies":{"vue":"3"}}"#).unwrap();
         fs::create_dir(d.join("src")).unwrap();
         fs::write(d.join("src/main.ts"), "x").unwrap();
         fs::create_dir(d.join("node_modules")).unwrap();
         fs::write(d.join("node_modules/junk.json"), "x").unwrap();
         let s = collect_sample(&d).unwrap();
-        assert!(s.readme.as_deref().unwrap_or("").contains("test project"));
+        // 徽章行剥,内容图 + 正文保留
+        let readme = s.readme.as_deref().unwrap_or("");
+        assert!(readme.contains("test project"), "readme={readme}");
+        assert!(!readme.contains("shields.io"), "徽章未剥: {readme}");
+        assert!(readme.contains("docs/arch.png"), "内容图丢失: {readme}");
+        // 内容图收集(badge 跳,arch 留)
+        assert_eq!(s.images.len(), 1);
+        assert_eq!(s.images[0].src, "./docs/arch.png");
         assert!(s.manifests.iter().any(|(n, _)| n == "package.json"));
         assert!(s.tree.iter().any(|t| t.contains("src")));
         // node_modules 应被过滤
@@ -359,6 +763,103 @@ mod tests {
         fs::remove_dir_all(&d).ok();
     }
 
+    #[test]
+    fn strips_frontmatter_and_toc_and_badges() {
+        let d = scratch("noise");
+        let readme = "---\ntitle: Foo\n---\n\n# Foo\n\n<!-- hidden comment -->\n\n[Install](#install)\n\n- [Usage](#usage)\n\n![build](https://img.shields.io/badge/build-passing)\n\nThis is the real intro.\n";
+        fs::write(d.join("README.md"), readme).unwrap();
+        let s = collect_sample(&d).unwrap();
+        let r = s.readme.as_deref().unwrap_or("");
+        assert!(r.contains("# Foo"), "标题应保留: {r}");
+        assert!(r.contains("real intro"), "正文应保留: {r}");
+        assert!(!r.contains("hidden comment"), "HTML 注释未剥: {r}");
+        assert!(!r.contains("shields.io"), "徽章未剥: {r}");
+        assert!(!r.contains("[Install](#install)"), "TOC 锚点未剥: {r}");
+        assert!(!r.contains("[Usage](#usage)"), "TOC 列表项未剥: {r}");
+        assert!(!r.contains("title: Foo"), "frontmatter 未剥: {r}");
+        fs::remove_dir_all(&d).ok();
+    }
+
+    #[test]
+    fn image_collection_skips_badges() {
+        let content = "![build](https://img.shields.io/x)\n![arch](./a.png)\n![version](https://badge.fury.io/js/y)\n![screenshot](screens/b.png)\n";
+        let imgs = collect_images(content);
+        // 只有 arch + screenshot,build/version 跳
+        assert_eq!(imgs.len(), 2);
+        assert!(imgs.iter().any(|i| i.src == "./a.png"));
+        assert!(imgs.iter().any(|i| i.src == "screens/b.png"));
+        assert!(imgs.iter().all(|i| !i.src.contains("shields.io")));
+        assert!(imgs.iter().all(|i| !i.src.contains("badge.fury")));
+    }
+
+    #[test]
+    fn detects_monorepo_pnpm() {
+        let d = scratch("mono-pnpm");
+        fs::write(d.join("pnpm-workspace.yaml"), "packages:\n  - packages/*\n").unwrap();
+        assert!(is_monorepo(&d));
+        fs::remove_dir_all(&d).ok();
+    }
+
+    #[test]
+    fn detects_monorepo_npm_workspaces() {
+        let d = scratch("mono-npm");
+        fs::write(
+            d.join("package.json"),
+            r#"{"name":"root","workspaces":["packages/*"]}"#,
+        )
+        .unwrap();
+        assert!(is_monorepo(&d));
+        fs::remove_dir_all(&d).ok();
+    }
+
+    #[test]
+    fn detects_non_monorepo() {
+        let d = scratch("nonmono");
+        fs::write(d.join("package.json"), r#"{"name":"x"}"#).unwrap();
+        assert!(!is_monorepo(&d));
+        fs::remove_dir_all(&d).ok();
+    }
+
+    #[test]
+    fn discover_monorepo_children() {
+        let d = scratch("discover-mono");
+        fs::write(d.join("pnpm-workspace.yaml"), "packages:\n  - packages/*\n").unwrap();
+        // 子项目:packages/web(有 package.json + vue)、packages/cli(有 Cargo.toml)
+        fs::create_dir_all(d.join("packages/web")).unwrap();
+        fs::write(
+            d.join("packages/web/package.json"),
+            r#"{"name":"web","dependencies":{"vue":"3"}}"#,
+        )
+        .unwrap();
+        fs::create_dir_all(d.join("packages/cli")).unwrap();
+        fs::write(d.join("packages/cli/Cargo.toml"), "[package]\nname=\"cli\"\n").unwrap();
+        // 空 stack 子目录应过滤
+        fs::create_dir_all(d.join("packages/empty")).unwrap();
+        fs::write(d.join("packages/empty/x.txt"), "x").unwrap();
+        let found = discover_projects(&d).unwrap();
+        // 根自身无 manifest 不入选;packages/web + packages/cli 入选;empty 过滤
+        let names: Vec<_> = found.iter().map(|p| p.name.as_str()).collect();
+        assert!(names.contains(&"web"), "names={names:?}");
+        assert!(names.contains(&"cli"), "names={names:?}");
+        assert!(!names.contains(&"empty"), "空 stack 未过滤: {names:?}");
+        fs::remove_dir_all(&d).ok();
+    }
+
+    #[test]
+    fn discover_flat_children() {
+        // 非 monorepo:扫根直接子目录中 detect_stack 非空的
+        let d = scratch("discover-flat");
+        fs::create_dir_all(d.join("proj-a")).unwrap();
+        fs::write(d.join("proj-a/Cargo.toml"), "").unwrap();
+        fs::create_dir_all(d.join("not-a-project")).unwrap();
+        fs::write(d.join("not-a-project/readme.txt"), "x").unwrap();
+        let found = discover_projects(&d).unwrap();
+        let names: Vec<_> = found.iter().map(|p| p.name.as_str()).collect();
+        assert!(names.contains(&"proj-a"), "names={names:?}");
+        assert!(!names.contains(&"not-a-project"), "空 stack 未过滤: {names:?}");
+        fs::remove_dir_all(&d).ok();
+    }
+
     #[test]
     fn extract_desc_skips_title_badge() {
         let d = scratch("desc");
diff --git a/docs/todo.md b/docs/todo.md
index 5787edb..49ebd2d 100644
--- a/docs/todo.md
+++ b/docs/todo.md
@@ -575,10 +575,10 @@
 - [x] T-260614-04 — ~~路径校验根治~~ ✅ 已完成（resolve_workspace_path 加 canonicalize 防 symlink 逃逸 + 词法 starts_with 兜底；仅校验、返回词法路径保持前端友好；cargo check 0 err / 22 test pass）(06-14)
 - [ ] F-260614-04 — 多 Provider 负载均衡池 — 备用模型/多账号聚合，全局容量=min(各 provider 上限之和, global_cap) (06-14)
 - [ ] F-260614-05 — 模型能力系统 Phase 2 — 多模态消息支持：ChatMessage.content: String → Vec<ContentPart>(Text/Image)；前端粘贴/拖拽图片；vision 模型自动路由 (06-14)
-- [ ] F-260614-06 — 导入历史项目（scan 第二步） — [📐设计定稿 06-14] description 走 LLM（复用 scan_project_with_ai，非纯规则）+ 采样保留内容图（待 F-260614-05 多模态）+ monorepo 一层 + 批量并发；抽 create_with_binding 缓解 :211 TODO；详见功能决策记录（06-14）
+- [x] ✅(batch61·2026-06-16·workflow wwtn2knn6+主代核查,cargo 0err+vue-tsc 0err) F-260614-06 — 导入历史项目（scan 第二步） — 6 决策全落地:①description 走 LLM(scan.rs extract_description 复用 complete)②采样扩 `images:Vec<ImageRef{alt,src}>`(scan.rs:330/343)+readme 剥 frontmatter/TOC/纯徽章行截 8KB(SAMPLE_README_MAX 2000→8000:346)+徽章域黑名单 5 域(shields.io/badge.fury/badgen 等:397-401)+is_badge_image+is_pure_badge_line③image 多模态留接口待 F-260614-05④monorepo 一层(is_monorepo:112+discover_projects:156 展开 packages/*/apps/* detect_stack 空过滤)⑤批量(scan_directory_for_projects:343 纯规则发现标已绑定 + import_projects_batch:415 并发 LLM llm_concurrency permit 限流非原子逐项独立)⑥抽 create_with_binding(:59 create/import 共用 缓解 :211 TODO)。前端 Projects.vue 导入 modal+api/project.ts 两 API+i18n 双语+scan.rs 单测全。— crates/df-project/scan.rs + src-tauri/commands/project.rs + lib.rs + api/project.ts + Projects.vue + i18n。详见功能决策记录(06-14)
 - [x] T-260614-09 — ~~idea.rs 物理删不级联~~ ✅ WF-E 完成（idea.rs:149 delete→purge_with_descendants，1 行，签名兼容）(06-14, commit 89da9fa)
 - [x] T-260614-10 — ~~findBinding canonicalize~~ ✅ WF-E 判定已解决（normalize_path 已含 canonicalize 优先 + 词法回退，find_binding_conflict 两端对称已用，无需重复加；零改动）(06-14, commit 89da9fa)
-- [ ] F-260614-07 — **[架构前置]** df-ai-core trait 下沉拆 crate — F-03 注入方式前置（决策记录已收敛：原 F-03 的 A/B/C 选型作废，统一为全局 AI trait 下沉独立 crate，df-ideas/df-nodes 依赖 trait 而非 df-ai 具体 impl）。解锁 F-03 — source:功能决策记录 (06-14)
+- [x] ✅(batch61·2026-06-16·workflow wwtn2knn6+主代核查,cargo 0err+7单测) F-260614-07 — **[架构前置]** df-ai-core trait 下沉拆 crate — 4 决策全落地:①df-ai-core 新 crate(仅 trait+数据结构 LlmProvider+ChatMessage 等,ContextManager/TokenEstimator 留 df-ai)②构造注入 Engine::new(Arc<dyn LlmProvider>)+Engine::heuristic()(idea.rs Some/None 分支,语义等价决策② Option 参数)③LLM 失败降级 EvaluatedBy 三态(Llm/Heuristic/HeuristicFallback:28)+warn④provider 应用层装配 idea.rs build_default_provider(DB is_default→build_provider→Option<Box>→Arc::from:192)。df-ai provider.rs:15 `pub use df_ai_core::provider::*` re-export(外部 use 路径不变,df-nodes 零改动)+lib.rs:17 `pub use df_ai_core`。**解锁 F-03 注入**(其他 crate 依赖 df-ai-core trait 非 df-ai impl)。— crates/df-ai-core(新)+crates/df-ai+crates/df-ideas+src-tauri/commands/idea.rs。详见设计文档 F-07-df-ai-core-trait下沉设计
 - [x] ⏸️(待决策.md已决c暂缓·2026-06-16) F-260614-08 — 决策治理产品化 — 活契约/规格契约自检机制产品化为可操作功能（当前散落文档机制） — source:功能决策记录缺口
 - [x] ⏸️(待决策.md已决c暂缓·2026-06-16) F-260614-09 — 项目 status 字段治理 — status 状态机规范化（planning/active/archived 等枚举约束） — source:功能决策记录缺口
 - [x] ⏸️(待决策.md已决c暂缓·2026-06-16) F-260614-10 — 知识库 MCP Server + Tier 2/3 — 对外 MCP 暴露 + 分层存储（当前仅 Tier 1 全栈） — source:PROGRESS Sprint15
diff --git a/docs/待审查.md b/docs/待审查.md
index 533d7a4..4c158a9 100644
--- a/docs/待审查.md
+++ b/docs/待审查.md
@@ -27,15 +27,33 @@
 
 - **范围**: 3 agent 合并批(workflow whae812z5,主代独立核查全过 + cargo check --workspace EXIT 0 + vue-tsc EXIT 0)。①**F-09 A 路线**(单例软隔离补漏清字段,不动 B 多会话架构):前端 `useAiConversations.ts:42-45` newConversation 补清 `queue=[]/generatingConvId=null/agentRound=0/searchQuery=''`(防旧会话排队消息带进新会话 + 后台事件错路由 + 侧栏旧搜索过滤);后端 `commands.rs:842-843` ai_conversation_create 补清 `stop_flag.store(false,SeqCst)`(关键:上方生成中分支置 true 停旧 loop 不复位则新会话 loop 启动即见 stop 退出) + `agent_language=None`(防沿用旧语言设置)。②**F-13 性能**:`agentic.rs:173` system_prompt token loop 外算一次缓存为 sys_tokens(整个 loop 不变参数,原每轮+每次重试重算);`:219-225` 外层 messages 构建用 sys_tokens;`:262-270` 重试循环改 `messages: messages.clone()` 复用外层 Vec(删每次重试的 session_arc.lock + build_for_request 全量 clone + estimate_text)。安全前提核验:stream_llm 不接 session_arc + 重试块到 process_tool_calls 间无 push,重试内 session.messages 与外层一致,重建等价复用。③**AE-03 路径 B**(审批 payload 加 diff,非路径 A):`tool_registry.rs:32` generate_diff fn→pub(crate)(复用 F-260615-10 LCS);`audit.rs:508-520` 新增 build_write_file_diff(从 args 取 path+content → 预读旧文件 tokio::fs::read_to_string → 无变化/旧文件缺失/读失败均 None → generate_diff);`audit.rs:588-609` 仅 write_file draft 挂起审批前预计算 approval_diff 注入 PendingApproval.diff(:600 clone)+AiApprovalRequired.diff(:609 move);恢复路径 diff=None(文件可能已变);`mod.rs:101/269` AiApprovalRequired+PendingApproval 加 `diff: Option<String>`;`types.ts:198` AiApprovalRequired 加 `diff?: string`;`useAiEvents.ts` event.diff 传 toolCall 信息;`ToolCard.vue:40-41` 模板 `v-if="tc.diff"` diff 块 + :394 diffLines computed(按 +/-/space 前缀拆分)+ :677-705 样式(add 绿 del 红 ctx 灰 + token 复用)。
 - **维度**: ①F-09 单例字段补清完整性(stop_flag 复位是否覆盖所有生成中→新建路径/agent_language 影响面) + B 路线边界(确认未误动 AiSession 单例) ②F-13 行为不变性核验(sys_tokens 缓存是否真不变/messages.clone() 复用是否等价重建/stream_llm 不持 session_arc 前提是否成立) + 锁持有期缩短收益确认 ③AE-03 路径 B 安全性(预读旧文件只读不改/审批拒绝不执行 handler/路径不校验风险——恶意路径读失败仅 None) + diff 注入完整性(实时挂起+恢复路径双覆盖/前端消费链 event→toolCall→模板) + generate_diff pub(crate) 暴露面。
-- **commit**: 待提交(累积 ~12 文件攒批 + 4 决策回填)。
+- **commit**: d00b30f(重构: AI聊天可靠性批次)。
+- **主代独立核查**: ✅ 全过(useAiConversations:42-45 补清 / commands:842-843 stop_flag.store(false)+agent_language=None / agentic:173 sys_tokens loop 外缓存 + :219-225 外层用 + :262-270 重试 messages.clone() 复用 + 保文退避逻辑未动(Partial 不重试/retry_deadline 30s) / tool_registry:32 generate_diff pub(crate) / audit:508-520 build_write_file_diff 预读+无变化/缺失 None+复用 generate_diff / audit:588-609 仅 write_file 生成 diff 注入 PendingApproval(clone)+AiApprovalRequired(move) / mod:101+269 diff 字段 / types:198 diff? / ToolCard:40-41 模板 + :394 diffLines + :677-705 样式 / cargo+vue-tsc 双 EXIT 0)。
+- **审查 agent 待复审重点**: ①F-13 重试 messages.clone() 复用等价性——核验 stream_llm 签名(stream_recv.rs:137 不接 session_arc) + 重试块到 process_tool_calls 间确实无 session.messages.push(若遗漏则重试复用旧 messages 致 tool_calls 丢失) ②F-13 sys_tokens 缓存——核验 system_prompt 确为 run_agentic_loop 不变参数(整个 loop 期间无 mutate) ③AE-03 build_write_file_diff 安全——预读旧文件 `tokio::fs::read_to_string(path)` 路径未校验(恶意 path 读失败仅 None 不产生危害,但核验无 symlink 逃逸读敏感文件风险——审批只读,write_file handler 自身 validate_path 执行时兜底) ④AE-03 diff 注入双路径——实时挂起(audit:588-609 有 diff)+恢复路径(PendingApproval diff 字段是否持久化,若仅内存则重启恢复审批无 diff) ⑤文件锁独立性——3 agent 改动文件无重叠(F-09: useAiConversations/commands / F-13: agentic / AE-03: mod/tool_registry/audit/types/useAiEvents/ToolCard),useAiEvents.ts Agent C 独占(A/B 未碰)确认无冲突。
 
 ### CR-260616-37 F-11 审批续跑 iteration 累计计数(跨审批不重置) — ⏳ 待审
 
-- **范围**: F-260616-11 决策a 落地(与 batch60 三项合批 commit)。`mod.rs:215/230` AiSession 加 `iteration_used:usize` 字段+new() init 0;`agentic.rs:114` run_agentic_loop 加 `start_iteration:usize` 参数 +`:176` loop 边界改 `start_iteration..max_iterations` +`:210` 一致性校验块更新 `session.iteration_used=iteration+1` +`:578` try_continue_agent_loop 加 start_iteration 参数 +`:675` spawn 透传 +`:499-501` 注释区分两路径;`commands.rs:55/152/412` ai_chat_send/ai_regenerate/ai_edit_last reset iteration_used=0(新生命周期)+`:260-262` ai_approve 拒绝续跑读 session.iteration_used 累计传 +`:313-315` ai_approve 通过续跑累计传 +`:601` ai_continue_loop reset iteration_used=0 传 0(F-03 决策a 达max重计区分)。两路径严格区分:审批续跑(ai_approve 累计透传 iteration_used) vs 达max续跑(ai_continue_loop reset+传0,F-03 决策a 用户授权重来) vs 新消息(reset 0)。
+- **范围**: F-260616-11 决策a 落地(与 batch60 三项合批 commit d00b30f)。`mod.rs:215/230` AiSession 加 `iteration_used:usize` 字段+new() init 0;`agentic.rs:114` run_agentic_loop 加 `start_iteration:usize` 参数 +`:176` loop 边界改 `start_iteration..max_iterations` +`:210` 一致性校验块更新 `session.iteration_used=iteration+1` +`:578` try_continue_agent_loop 加 start_iteration 参数 +`:675` spawn 透传 +`:499-501` 注释区分两路径;`commands.rs:55/152/412` ai_chat_send/ai_regenerate/ai_edit_last reset iteration_used=0(新生命周期)+`:260-262` ai_approve 拒绝续跑读 session.iteration_used 累计传 +`:313-315` ai_approve 通过续跑累计传 +`:601` ai_continue_loop reset iteration_used=0 传 0(F-03 决策a 达max重计区分)。两路径严格区分:审批续跑(ai_approve 累计透传 iteration_used) vs 达max续跑(ai_continue_loop reset+传0,F-03 决策a 用户授权重来) vs 新消息(reset 0)。
 - **维度**: ①iteration_used 字段生命周期完备性(所有会话生命周期入口 reset 覆盖——create/send/regenerate/edit_last/continue_loop 是否齐全无遗漏路径) ②start_iteration 透传链完整(agentic run_agentic_loop←try_continue_agent_loop←ai_approve 两个调用点,无断链) ③两路径区分正确性(ai_approve 累计 vs ai_continue_loop 重计,F-11/F-03 决策 a 分别归属无混淆) ④边界 case(start≥max loop 空区间→converged=false→AiMaxRoundsReached,防无限审批烧 token,符合 F-11 语义) ⑤并发安全(iteration_used 在一致性 lock 块更新,与现有 lock 模式一致)。
-- **commit**: 待提交(同 CR-36 合批)。
-- **主代独立核查**: ✅ 全过(useAiConversations:42-45 补清 / commands:842-843 stop_flag.store(false)+agent_language=None / agentic:173 sys_tokens loop 外缓存 + :219-225 外层用 + :262-270 重试 messages.clone() 复用 + 保文退避逻辑未动(Partial 不重试/retry_deadline 30s) / tool_registry:32 generate_diff pub(crate) / audit:508-520 build_write_file_diff 预读+无变化/缺失 None+复用 generate_diff / audit:588-609 仅 write_file 生成 diff 注入 PendingApproval(clone)+AiApprovalRequired(move) / mod:101+269 diff 字段 / types:198 diff? / ToolCard:40-41 模板 + :394 diffLines + :677-705 样式 / cargo+vue-tsc 双 EXIT 0)。
-- **审查 agent 待复审重点**: ①F-13 重试 messages.clone() 复用等价性——核验 stream_llm 签名(stream_recv.rs:137 不接 session_arc) + 重试块到 process_tool_calls 间确实无 session.messages.push(若遗漏则重试复用旧 messages 致 tool_calls 丢失) ②F-13 sys_tokens 缓存——核验 system_prompt 确为 run_agentic_loop 不变参数(整个 loop 期间无 mutate) ③AE-03 build_write_file_diff 安全——预读旧文件 `tokio::fs::read_to_string(path)` 路径未校验(恶意 path 读失败仅 None 不产生危害,但核验无 symlink 逃逸读敏感文件风险——审批只读,write_file handler 自身 validate_path 执行时兜底) ④AE-03 diff 注入双路径——实时挂起(audit:588-609 有 diff)+恢复路径(PendingApproval diff 字段是否持久化,若仅内存则重启恢复审批无 diff) ⑤文件锁独立性——3 agent 改动文件无重叠(F-09: useAiConversations/commands / F-13: agentic / AE-03: mod/tool_registry/audit/types/useAiEvents/ToolCard),useAiEvents.ts Agent C 独占(A/B 未碰)确认无冲突。
+- **commit**: d00b30f(同 CR-36 合批)。
+- **主代独立核查**: ✅ 全过(mod.rs:215/230 iteration_used 字段+init / agentic.rs:114 start_iteration 参数+:176 loop 边界+:210 一致性块 iteration+1+:578 try_continue 加参+:675 spawn 透传+:499-501 注释两路径 / commands.rs:55/152/412 reset 0+ai_approve:260/313 累计读 iteration_used+ai_continue_loop:601 reset 传0 / cargo check --workspace EXIT 0)。
+- **审查 agent 待复审重点**: ①iteration_used 生命周期完备性(所有会话生命周期入口 reset 覆盖无遗漏——create/send/regenerate/edit_last/continue_loop/ai_approve 全路径核验) ②start_iteration 透传链(run_agentic_loop←try_continue_agent_loop←ai_approve 两调用点无断链) ③两路径区分(ai_approve 累计 vs ai_continue_loop 重计,F-11/F-03 决策a 归属无混) ④边界 start≥max loop 空区间→converged false→AiMaxRoundsReached(防无限审批烧 token) ⑤并发安全(一致性 lock 块更新)。
+
+### CR-260616-38 F-07 df-ai-core trait 下沉拆 crate(解锁 F-03 注入) — ⏳ 待审
+
+- **范围**: F-260614-07 4 决策落地(workflow wwtn2knn6)。①df-ai-core 新 crate:`crates/df-ai-core/`(Cargo.toml serde+async-trait+futures + src/lib.rs `pub mod provider` + src/provider.rs 迁移 trait+数据结构 LlmProvider/ChatMessage 等,不含 HTTP impl);②df-ai re-export:`crates/df-ai/Cargo.toml` 加 df-ai-core 依赖 + `src/provider.rs:15` `pub use df_ai_core::provider::*` + `src/lib.rs:17` `pub use df_ai_core`(外部 use df_ai::provider 路径不变,df-nodes 零改动,workspace Cargo.toml 零改动 members=crates/*);③df-ideas 构造注入:`crates/df-ideas/Cargo.toml` 加 df-ai-core+async-trait + `src/adversarial.rs:18` use df_ai_core::provider::LlmProvider + struct 加 `provider:Option<Arc<dyn LlmProvider>>`(:96) + `new(provider:Arc<dyn LlmProvider>)`(:101 调用方 Some/None 分支语义等价 Option)+ `heuristic()`(:107) + `evaluate(&self)`(:111) + `evaluate_with_llm`(:138) + EvaluatedBy 三态 enum(:28) + AdversarialEval.evaluated_by 字段(:49) + LLM 失败→warn+HeuristicFallback 降级(:122);④src-tauri 装配:`idea.rs:190` build_default_provider(DB is_default→build_provider→Option<Box<dyn LlmProvider>>:265) + :192 Some→`AdversarialEngine::new(Arc::from(p))` / None→`heuristic()`(:193)。
+- **维度**: ①re-export 透明性(df_ai::provider::LlmProvider 路径 df-nodes/df-project 等外部 use 无断裂) ②trait 下沉边界(LlmProvider trait+纯数据结构下沉,ContextManager/TokenEstimator/AiToolRegistry 业务逻辑确留 df-ai 未误迁) ③构造注入语义(new(Arc) vs 决策② Option<Arc>——调用方分支等价性,无遗漏 Some/None) ④EvaluatedBy 三态正确(Llm/Heuristic/HeuristicFallback,provider None 全走 Heuristic) ⑤Box→Arc 转换(idea.rs:192 Arc::from(Box<dyn LlmProvider>) 编译通过) ⑥build_default_provider DB 读(is_default 查询+无默认兜底 heuristic) ⑦adversarial 7 单测改 heuristic() 构造后全过。
+- **commit**: 待提交(batch61 合批)。
+- **主代独立核查**: ✅ 全过(df-ai-core 新建 Cargo.toml+lib.rs+provider.rs / df-ai provider.rs:15+lib.rs:17 re-export+Cargo.toml 加依赖 / df-ideas adversarial.rs:18 use df_ai_core:96 provider 字段+:101 new(Arc)+:107 heuristic+:111 evaluate(&self)+:138 evaluate_with_llm+:28 EvaluatedBy+:49 evaluated_by+:122 HeuristicFallback 降级 / idea.rs:190 build_default_provider+:192 new(Arc::from)+:193 heuristic+:265 fn / cargo check --workspace EXIT 0(5 warning 全 pre-existing dead_code))。
+- **审查 agent 待复审重点**: ①provider.rs 迁移完整性(df-ai 原 trait+数据结构全部下沉,df-ai/src/provider.rs 仅剩 re-export,retry/openai_compat/anthropic_compat HTTP impl 确留 df-ai 未断) ②df-nodes/df-project 外部 use df_ai::provider 编译验证(re-export 透明无断裂,实际 grep df_ai::provider 消费点) ③EvaluatedBy 三态语义(provider Some+LLM 成功=Llm / Some+LLM 失败=HeuristicFallback / None=Heuristic,evaluate_internal 调度正确) ④build_default_provider DB is_default 查询正确+build_provider 复用现有工厂 ⑤Box→Arc::from 编译期验证(cargo EXIT 0 已证)。
+
+### CR-260616-39 F-06 导入历史项目 scan 第二步(LLM description+monorepo+批量并发) — ⏳ 待审
+
+- **范围**: F-260614-06 6 决策落地(workflow wwtn2knn6)。①description 走 LLM(scan.rs extract_description 复用 complete,非纯规则);②采样改进(ProjectSample 扩 `images:Vec<ImageRef{alt,src}>` scan.rs:330/343 + readme 剥 frontmatter/TOC/纯徽章行截 8KB `SAMPLE_README_MAX=2000→8000`:346 + 保留内容图 markdown);③image 多模态条件化(ImageRef 数据结构+collect_images 收集:454,本批不读 base64 留接口待 F-260614-05);④monorepo 一层(is_monorepo:112 检 pnpm-workspace/lerna/turbo/nx/package.json workspaces + discover_projects:156 展开 packages/*/apps/* detect_stack 空过滤);⑤批量流程(scan_directory_for_projects project.rs:343 纯规则发现标已绑定 + import_projects_batch:415 并发 LLM llm_concurrency permit 限流复用绑定入库非原子逐项独立);⑥对称改进(抽 create_with_binding project.rs:59 create/import 共用校验+防重+探测+insert 缓解 :211 TODO,relocate 不并入)。前端 Projects.vue 导入 modal+api/project.ts 两 API+i18n 双语+scan.rs 单测(剥 badges/image 收集/monorepo/discover/extract_desc)。
+- **维度**: ①is_monorepo 判定完备(pnpm-workspace/lerna/turbo/nx+package.json workspaces 全覆盖) ②discover_projects 展开(detect_stack 空过滤误剔合法项目风险/monorepo 子目录一层不递归过深) ③collect_sample images 收集(徽章域黑名单 5 域+is_badge_image alt+src 双判+is_pure_badge_line 整行纯徽章,内容图 arch/screenshot 留) ④SAMPLE_README_MAX 8000 截断(truncate_chars 不破坏 markdown/UTF-8 边界) ⑤create_with_binding 抽取(create+import 共用一致,relocate 确未并入) ⑥import_projects_batch 并发(llm_concurrency 双层 permit 限流/非原子逐项独立失败不阻塞) ⑦scan_directory_for_projects 纯规则不跑 LLM(标已绑定正确) ⑧df-project use df_ai::provider 路径不变(F-07 re-export 透明) ⑨前端预览只读+toast 汇总(导入 N/跳过 M)。
+- **commit**: 待提交(batch61 合批)。
+- **主代独立核查**: ✅ 全过(scan.rs:112 is_monorepo+:156 discover_projects+:330 ImageRef+:343 images 字段+:346 SAMPLE_README_MAX 8000+:357 collect_sample+:397-401 徽章域 5 域+:439 is_badge_image+:454 collect_images+:533 is_pure_badge_line+:742-876 单测 / project.rs:59 create_with_binding+:343 scan_directory_for_projects+:415 import_projects_batch / lib.rs:85-86 注册两 IPC / api/project.ts:117/125 两 API / Projects.vue:111 mono-tag / cargo check --workspace EXIT 0+vue-tsc EXIT 0)。
+- **审查 agent 待复审重点**: ①is_monorepo/discover_projects 边界(nested monorepo/无 workspace 配置/detect_stack 空过滤误剔) ②徽章过滤完整(5 域黑名单+alt/src 双判是否漏内容图误剔或漏 badge 误留) ③create_with_binding 与原 create_project 行为一致(校验+防重+探测+insert 路径无回归) ④import_projects_batch 并发安全(llm_concurrency permit 限流正确+非原子逐项失败隔离) ⑤scan_directory_for_projects 性能(纯规则不跑 LLM,目录扫描深浅/大目录性能) ⑥前端 modal 预览表格只读+勾选+toast 汇总交互完整。
 
 ---
 
diff --git a/src-tauri/src/commands/idea.rs b/src-tauri/src/commands/idea.rs
index 952c5f5..b2753ff 100644
--- a/src-tauri/src/commands/idea.rs
+++ b/src-tauri/src/commands/idea.rs
@@ -1,8 +1,11 @@
 //! 灵感相关命令
 
+use std::sync::Arc;
+
 use serde::Deserialize;
 use tauri::State;
 
+use df_ai::provider::LlmProvider;
 use df_core::types::{new_id, Priority};
 use df_ideas::capture::Idea;
 use df_storage::models::{IdeaRecord, ProjectRecord};
@@ -183,10 +186,13 @@ pub async fn evaluate_idea(
     // 多维评分（0-10，IPC 层 *10 缩放为 0-100）
     let scores = df_ideas::scoring::ScoringEngine::compute_default(&idea);
 
-    // 对抗式评估
-    let eval = df_ideas::adversarial::AdversarialEngine::evaluate(&idea)
-        .await
-        .map_err(err_str)?;
+    // 对抗式评估（构造注入：从 DB 读默认 provider 装配 LLM，无 provider/构造失败 → 启发式兜底）
+    let provider = build_default_provider(&state).await;
+    let engine = match provider {
+        Some(p) => df_ideas::adversarial::AdversarialEngine::new(Arc::from(p)),
+        None => df_ideas::adversarial::AdversarialEngine::heuristic(),
+    };
+    let eval = engine.evaluate(&idea).await.map_err(err_str)?;
 
     // 组装前端扁平结构（与 Ideas.vue 的 AdversarialEval interface 对齐）
     let positive_strength = eval.positive.confidence;
@@ -248,6 +254,31 @@ pub async fn evaluate_idea(
     Ok(updated)
 }
 
+/// 从 DB 读取默认 provider 配置（is_default 优先，否则首个）+ build_provider 构造实例。
+///
+/// 返回 `None` 的两种情况（统一走启发式评估兜底）：
+/// - DB 未配置任何 provider（`list_all` 空或全无 is_default 且无首条）
+/// - provider 密钥不可用（keyring 无记录 / 纯空白），`build_provider_for` 返 Err
+///
+/// 复用 `commands::ai::secret::build_provider_for`（resolve→ensure→build 三步），
+/// 与 AI Chat / 项目扫描的 provider 构造路径统一（FR-S1 密钥解析一致）。
+async fn build_default_provider(state: &State<'_, AppState>) -> Option<Box<dyn LlmProvider>> {
+    let providers = state.ai_providers.list_all().await.ok()?;
+    let pc = providers
+        .iter()
+        .find(|p| p.is_default)
+        .cloned()
+        .or_else(|| providers.into_iter().next())?;
+    match crate::commands::ai::secret::build_provider_for(&pc) {
+        Ok(p) => Some(p),
+        Err(e) => {
+            // 密钥不可用:启发式兜底,不阻断评估（与 evaluate_idea LLM 失败降级语义一致）
+            tracing::warn!("默认 provider 密钥不可用,对抗评估走启发式: {e}");
+            None
+        }
+    }
+}
+
 /// IdeaRecord → df_ideas::Idea（评估用，status/time 不影响评分）
 fn record_to_idea(record: &IdeaRecord) -> Idea {
     let tags: Vec<String> = record
diff --git a/src-tauri/src/commands/project.rs b/src-tauri/src/commands/project.rs
index 16faf7f..c3600e1 100644
--- a/src-tauri/src/commands/project.rs
+++ b/src-tauri/src/commands/project.rs
@@ -7,7 +7,10 @@ use tauri::State;
 
 use df_ai::provider::{ChatMessage, CompletionRequest};
 use df_core::types::new_id;
-use df_project::scan::{collect_sample, detect_stack, extract_description, normalize_path};
+use df_project::scan::{
+    collect_sample, detect_stack, discover_projects, extract_description, is_monorepo,
+    normalize_path, DiscoveredProject,
+};
 use df_storage::models::ProjectRecord;
 
 use crate::state::AppState;
@@ -42,18 +45,36 @@ pub async fn list_projects(state: State<'_, AppState>) -> Result<Vec<ProjectReco
 pub async fn create_project(
     state: State<'_, AppState>,
     input: CreateProjectInput,
+) -> Result<ProjectRecord, String> {
+    create_with_binding(&state, input.name, input.description, input.idea_id, input.path, input.stack).await
+}
+
+/// 共用「校验 + 防重 + 探测 + insert」核心 — create_project 与 import_projects_batch 共用。
+///
+/// 对称收敛(决策记录:217 create/bind 去重):绑定逻辑单一实现,
+/// 绑定目录时统一走「校验存在 + 防重复 + 自动探测 stack(stack 入参为空时)」。
+/// relocate 不并入(走 update_field 非 insert)。
+///
+/// 返回 insert 后的完整记录。
+async fn create_with_binding(
+    state: &AppState,
+    name: String,
+    description: String,
+    idea_id: Option<String>,
+    path: Option<String>,
+    stack: Option<String>,
 ) -> Result<ProjectRecord, String> {
     // 绑定目录:校验存在 + 防重复 + 自动探测技术栈
-    let (path, stack) = match input.path.as_deref().map(str::trim).filter(|p| !p.is_empty()) {
+    let (path, stack) = match path.as_deref().map(str::trim).filter(|p| !p.is_empty()) {
         Some(p) => {
             if !Path::new(p).is_dir() {
                 return Err(format!("目录不存在: {p}"));
             }
-            if let Some(conflict) = find_binding_conflict(&state, p, None).await? {
+            if let Some(conflict) = find_binding_conflict(state, p, None).await? {
                 return Err(format!("目录已被项目「{}」绑定", conflict.name));
             }
             // stack 优先用入参,否则自动探测(spawn_blocking 防 IO 阻塞 tokio runtime)
-            let stack_json = match input.stack.as_deref().map(str::trim).filter(|s| !s.is_empty()) {
+            let stack_json = match stack.as_deref().map(str::trim).filter(|s| !s.is_empty()) {
                 Some(s) => s.to_string(),
                 None => {
                     let root = std::path::PathBuf::from(p);
@@ -72,20 +93,16 @@ pub async fn create_project(
     let now = now_millis();
     let record = ProjectRecord {
         id: new_id(),
-        name: input.name,
-        description: input.description,
+        name,
+        description,
         status: "planning".to_string(),
-        idea_id: input.idea_id,
+        idea_id,
         path,
         stack,
         created_at: now.clone(),
         updated_at: now,
     };
-    state
-        .projects
-        .insert(record.clone())
-        .await
-        .map_err(err_str)?;
+    state.projects.insert(record.clone()).await.map_err(err_str)?;
     Ok(record)
 }
 
@@ -111,7 +128,7 @@ pub struct ImportProjectInput {
 /// (可选)读 README 首段填 description 一次性完成,无需先建空项目再绑定。
 ///
 /// 流程:校验目录存在 → normalize_path 防重复绑定 → detect_stack + extract_description
-/// (spawn_blocking 防 IO 阻塞 tokio runtime)→ 拼记录 insert → 返回。
+/// (spawn_blocking 防 IO 阻塞 tokio runtime)→ 走 create_with_binding insert → 返回。
 #[tauri::command]
 pub async fn import_project(
     state: State<'_, AppState>,
@@ -124,18 +141,15 @@ pub async fn import_project(
     if !Path::new(&path).is_dir() {
         return Err(format!("目录不存在: {path}"));
     }
-    // 防重复绑定(normalize_path 规范化比较,防正反斜杠/末尾斜杠绕过)
-    if let Some(conflict) = find_binding_conflict(&state, &path, None).await? {
-        return Err(format!("目录已被项目「{}」绑定", conflict.name));
-    }
 
-    // 探测栈 + 读 description(spawn_blocking 防 IO 阻塞 tokio runtime)
+    // 解析 name/desc/stack(入参优先,缺省时从目录探测/读 README)。
+    // spawn_blocking 防 IO 阻塞 tokio runtime。stack 解析后透传给 create_with_binding
+    // (不再重复探测,与原行为一致)。
     let root = std::path::PathBuf::from(&path);
     let want_name = input.name.clone();
     let want_desc = input.description.clone();
     let want_stack = input.stack.clone();
     let (name, description, stack_json) = tokio::task::spawn_blocking(move || -> Result<_, String> {
-        // name: 入参优先,否则取目录名
         let name = match want_name.as_deref().map(str::trim).filter(|s| !s.is_empty()) {
             Some(n) => n.to_string(),
             None => root
@@ -144,12 +158,10 @@ pub async fn import_project(
                 .map(|s| s.to_string())
                 .ok_or_else(|| "无法从路径解析项目名".to_string())?,
         };
-        // description: 入参优先,否则读 README 首段
         let description = match want_desc.as_deref().map(str::trim).filter(|s| !s.is_empty()) {
             Some(d) => d.to_string(),
             None => extract_description(&root).unwrap_or_default(),
         };
-        // stack: 入参优先,否则自动探测
         let stack_json = match want_stack.as_deref().map(str::trim).filter(|s| !s.is_empty()) {
             Some(s) => s.to_string(),
             None => {
@@ -162,24 +174,7 @@ pub async fn import_project(
     .await
     .map_err(err_str)??;
 
-    let now = now_millis();
-    let record = ProjectRecord {
-        id: new_id(),
-        name,
-        description,
-        status: "planning".to_string(),
-        idea_id: None,
-        path: Some(path),
-        stack: Some(stack_json),
-        created_at: now.clone(),
-        updated_at: now,
-    };
-    state
-        .projects
-        .insert(record.clone())
-        .await
-        .map_err(err_str)?;
-    Ok(record)
+    create_with_binding(&state, name, description, None, Some(path), Some(stack_json)).await
 }
 
 /// 按 ID 查询项目
@@ -325,6 +320,228 @@ pub async fn check_path_exists(path: String) -> Result<bool, String> {
     Ok(Path::new(&path).is_dir())
 }
 
+// ============================================================
+// 批量扫描/导入历史项目 — F-260614-06(scan 第二步)
+// ============================================================
+
+/// 扫描发现的候选项目(规则发现,无 LLM)。前端预览表格只读展示。
+#[derive(Debug, Serialize)]
+pub struct ScannedProjectItem {
+    pub path: String,
+    pub name: String,
+    pub stack: Vec<String>,
+    pub is_monorepo: bool,
+    /// 该目录是否已被某个项目绑定(防重复,前端标记禁选)
+    pub already_bound: bool,
+}
+
+/// 扫描根目录发现候选项目(规则发现,快、不跑 LLM)。
+///
+/// 调 `discover_projects`(monorepo 一层展开 + detect_stack 非空过滤),
+/// 标记每个候选是否已被项目绑定。前端用预览表格勾选后调 import_projects_batch。
+#[tauri::command]
+pub async fn scan_directory_for_projects(
+    state: State<'_, AppState>,
+    root_path: String,
+) -> Result<Vec<ScannedProjectItem>, String> {
+    let root = Path::new(&root_path);
+    if !root.is_dir() {
+        return Err(format!("目录不存在: {root_path}"));
+    }
+
+    // 1. 规则发现(spawn_blocking 防 IO 阻塞 tokio runtime)
+    let scan_root = std::path::PathBuf::from(&root_path);
+    let discovered: Vec<DiscoveredProject> = tokio::task::spawn_blocking(move || {
+        discover_projects(&scan_root)
+    })
+    .await
+    .map_err(err_str)?
+    .map_err(err_str)?;
+
+    // 2. 标已绑定项(逐项 normalize_path 查重)
+    let mut out = Vec::with_capacity(discovered.len());
+    for d in discovered {
+        let already_bound = find_binding_conflict(&state, &d.path, None)
+            .await?
+            .is_some();
+        out.push(ScannedProjectItem {
+            path: d.path,
+            name: d.name,
+            stack: d.stack,
+            is_monorepo: d.is_monorepo,
+            already_bound,
+        });
+    }
+    Ok(out)
+}
+
+/// 批量导入历史项目单条结果
+#[derive(Debug, Serialize)]
+pub struct ImportBatchItemResult {
+    /// 入参 path(回显,前端按 path 对齐结果)
+    pub path: String,
+    /// 成功:导入的项目名;失败:None
+    pub name: Option<String>,
+    /// 失败原因(成功为 None)
+    pub error: Option<String>,
+}
+
+/// 批量导入历史项目结果(前端 toast 汇总)
+#[derive(Debug, Serialize)]
+pub struct ImportBatchResult {
+    pub imported: usize,
+    pub skipped: usize,
+    pub items: Vec<ImportBatchItemResult>,
+}
+
+/// 单条批量导入入参
+#[derive(Debug, Deserialize)]
+pub struct ImportBatchItemInput {
+    pub path: String,
+    #[serde(default)]
+    pub name: Option<String>,
+}
+
+/// 批量导入历史项目 — 对用户勾选项并发 LLM 抽 description + 入库绑定。
+///
+/// F-260614-06 决策⑤:扫描(scan_directory_for_projects)纯规则发现;此命令对勾选项
+/// 并发跑 LLM(复用 scan_project_with_ai 的 complete 调用)抽 description。每项独立,
+/// 非原子 —— 单项失败不影响其它项,逐项结果回传。LLM 全失败 description 留空(不喂噪音),
+/// 用户可在详情页手填。
+///
+/// 限流:llm_concurrency 双层 permit(global + per_conv)防止批量扫描打满 provider。
+/// 默认 planning 状态(对齐 create_project),不关联 idea。
+#[tauri::command]
+pub async fn import_projects_batch(
+    state: State<'_, AppState>,
+    items: Vec<ImportBatchItemInput>,
+) -> Result<ImportBatchResult, String> {
+    if items.is_empty() {
+        return Ok(ImportBatchResult {
+            imported: 0,
+            skipped: 0,
+            items: Vec::new(),
+        });
+    }
+
+    // 取默认 provider(优先 is_default,否则首个)。无 provider 直接报错(批量无降级路径,
+    // 因为 description 是核心目的,无 LLM 与单 import_project 行为不同 —— 那走 import_project)
+    let providers = state.ai_providers.list_all().await.map_err(err_str)?;
+    let pc = providers
+        .iter()
+        .find(|p| p.is_default)
+        .cloned()
+        .or_else(|| providers.into_iter().next())
+        .ok_or_else(|| "未配置 AI 提供商,请先在设置中添加".to_string())?;
+    // build_provider_for 返回 Box<dyn LlmProvider>(非 Clone);多 future 共享需 Arc 包装。
+    // LlmProvider: Send + Sync + complete(&self) → Arc 共享安全。
+    let boxed = crate::commands::ai::secret::build_provider_for(&pc)
+        .map_err(|e| format!("provider 密钥不可用: {e}"))?;
+    let provider: std::sync::Arc<dyn df_ai::provider::LlmProvider> = std::sync::Arc::from(boxed);
+
+    // 每项独立 future,并发 join。失败逐项记录不影响其它。
+    // 注:provider 通过 Arc clone 在各 future 间共享(零拷贝,引用计数)。
+    let futures: Vec<_> = items
+        .into_iter()
+        .map(|item| {
+            let state_ref = state.inner();
+            let provider = provider.clone();
+            let pc = pc.clone();
+            async move {
+                let path = item.path.trim().to_string();
+                if path.is_empty() {
+                    return ImportBatchItemResult {
+                        path,
+                        name: None,
+                        error: Some("路径为空".to_string()),
+                    };
+                }
+                // 走 scan_project_with_ai 同款「探测+采样+LLM 抽 description」(轻量子代理)
+                let desc = match extract_description_via_llm(state_ref, &provider, &pc, &path).await {
+                    Ok(d) => d,
+                    Err(e) => {
+                        // LLM 失败/降级:description 留空,但仍入库(用户手填)。记录原因。
+                        tracing::warn!("批量导入 LLM 抽 description 失败 path={path} err={e}");
+                        String::new()
+                    }
+                };
+                let want_name = item.name.as_deref().map(str::trim).filter(|s| !s.is_empty()).map(String::from);
+                match create_with_binding(state_ref, resolve_name(&path, want_name), desc, None, Some(path.clone()), None).await {
+                    Ok(rec) => ImportBatchItemResult {
+                        path,
+                        name: Some(rec.name),
+                        error: None,
+                    },
+                    Err(e) => ImportBatchItemResult {
+                        path,
+                        name: None,
+                        error: Some(e),
+                    },
+                }
+            }
+        })
+        .collect();
+
+    let results = futures::future::join_all(futures).await;
+    let imported = results.iter().filter(|r| r.name.is_some()).count();
+    let skipped = results.len() - imported;
+    Ok(ImportBatchResult {
+        imported,
+        skipped,
+        items: results,
+    })
+}
+
+/// 名字解析:入参优先,否则取目录名
+fn resolve_name(path: &str, want: Option<String>) -> String {
+    if let Some(n) = want {
+        return n;
+    }
+    Path::new(path)
+        .file_name()
+        .and_then(|n| n.to_str())
+        .map(|s| s.to_string())
+        .unwrap_or_else(|| path.to_string())
+}
+
+/// 复用 scan_project_with_ai 路径抽 description(轻量子代理)。
+/// 双层 llm_concurrency permit 限流 + LLM 失败/解析失败返回空 description(不报错)。
+async fn extract_description_via_llm(
+    state: &AppState,
+    provider: &std::sync::Arc<dyn df_ai::provider::LlmProvider>,
+    pc: &df_storage::models::AiProviderRecord,
+    path: &str,
+) -> Result<String, String> {
+    let root = std::path::PathBuf::from(path);
+    let (rule_stack, sample) = tokio::task::spawn_blocking(move || {
+        let stack = detect_stack(&root)?;
+        let sample = collect_sample(&root)?;
+        Ok::<_, anyhow::Error>((stack, sample))
+    })
+    .await
+    .map_err(err_str)?
+    .map_err(err_str)?;
+
+    let request = CompletionRequest {
+        model: pc.default_model.clone(),
+        messages: build_scan_prompt(&sample, &rule_stack),
+        temperature: Some(0.2),
+        max_tokens: Some(400),
+        stream: false,
+        tools: None,
+        tool_choice: None,
+    };
+
+    let _g = state.llm_concurrency.acquire_global().await;
+    let _c = state.llm_concurrency.acquire_per_conv().await;
+    let resp = provider.complete(request).await.map_err(err_str)?;
+    // 只取 description,其它字段丢弃(批量场景不需要 project_type/stack 细化)
+    let desc = parse_scan_result(&resp.text)
+        .map(|p| p.description)
+        .unwrap_or_default();
+    Ok(desc)
+}
+
 // ============================================================
 // AI 扫描项目 — LLM 分析采样自动填基础信息
 // ============================================================
diff --git a/src-tauri/src/lib.rs b/src-tauri/src/lib.rs
index f82aa39..aa073ab 100644
--- a/src-tauri/src/lib.rs
+++ b/src-tauri/src/lib.rs
@@ -82,6 +82,8 @@ pub fn run() {
             commands::project::relocate_project_path,
             commands::project::check_path_exists,
             commands::project::scan_project_with_ai,
+            commands::project::scan_directory_for_projects,
+            commands::project::import_projects_batch,
             // 任务
             commands::task::list_tasks,
             commands::task::create_task,
diff --git a/src/api/project.ts b/src/api/project.ts
index 9ae140e..b217b37 100644
--- a/src/api/project.ts
+++ b/src/api/project.ts
@@ -13,6 +13,39 @@ export interface ImportProjectInput {
   stack?: string
 }
 
+/** 扫描发现的候选项目(规则发现,无 LLM)。前端预览表格只读展示。 */
+export interface ScannedProjectItem {
+  path: string
+  name: string
+  stack: string[]
+  is_monorepo: boolean
+  /** 该目录是否已被某项目绑定(防重复,前端标记禁选) */
+  already_bound: boolean
+}
+
+/** 批量导入单条入参 */
+export interface ImportBatchItemInput {
+  path: string
+  /** 项目名(可选,空=用目录名) */
+  name?: string
+}
+
+/** 批量导入单条结果 */
+export interface ImportBatchItemResult {
+  path: string
+  /** 成功:导入的项目名;失败:null */
+  name: string | null
+  /** 失败原因(成功为 null) */
+  error: string | null
+}
+
+/** 批量导入结果(前端 toast 汇总) */
+export interface ImportBatchResult {
+  imported: number
+  skipped: number
+  items: ImportBatchItemResult[]
+}
+
 export const projectApi = {
   list(): Promise<ProjectRecord[]> {
     return invoke('list_projects')
@@ -75,4 +108,20 @@ export const projectApi = {
   scanWithAi(path: string): Promise<AiScanResult> {
     return invoke('scan_project_with_ai', { path })
   },
+
+  /**
+   * 扫描根目录发现候选项目(规则发现,快、不跑 LLM)。
+   * 返回含 monorepo 子项目展开结果,前端预览表格勾选后调 importProjectsBatch。
+   */
+  scanDirectoryForProjects(rootPath: string): Promise<ScannedProjectItem[]> {
+    return invoke('scan_directory_for_projects', { rootPath })
+  },
+
+  /**
+   * 批量导入历史项目 — 对勾选项并发 LLM 抽 description + 入库绑定。
+   * 每项独立非原子,逐项结果回传。LLM 失败 description 留空(不喂噪音)。
+   */
+  importProjectsBatch(items: ImportBatchItemInput[]): Promise<ImportBatchResult> {
+    return invoke('import_projects_batch', { items })
+  },
 }
diff --git a/src/i18n/en/projects.ts b/src/i18n/en/projects.ts
index b9c1ca8..1934ac5 100644
--- a/src/i18n/en/projects.ts
+++ b/src/i18n/en/projects.ts
@@ -36,6 +36,26 @@ export default {
     aiScanNoDesc: 'AI could not generate a description, please fill manually',
     aiScanFailed: 'AI scan failed',
     dirConflict: 'This directory is already bound to project "{name}"',
+    // Import historical projects (F-260614-06 scan step 2)
+    importHistory: '📥 Import Projects',
+    importTitle: 'Import Historical Projects',
+    importSelectRoot: 'Select root directory to scan',
+    importScanning: 'Scanning…',
+    importRescan: 'Rescan',
+    importEmpty: 'No candidate projects found in this directory (need Cargo.toml/package.json/go.mod etc.)',
+    importColName: 'Name',
+    importColPath: 'Path',
+    importColStack: 'Stack',
+    importColBound: 'Bound',
+    importMonoHint: 'monorepo',
+    importSelectAll: 'Select all',
+    importSelected: '{n} selected',
+    importRun: 'Import selected',
+    importRunning: 'Importing…',
+    importDone: 'Import complete: {imported} succeeded, {skipped} skipped',
+    importNoSelection: 'Please select projects to import first',
+    importScanFailed: 'Failed to scan directory',
+    importFailed: 'Batch import failed',
     // Status labels (PROJECT_STATUS_LABELS values in constants/project.ts use these keys)
     status: {
       planning: '📐 Planning',
diff --git a/src/i18n/zh-CN/projects.ts b/src/i18n/zh-CN/projects.ts
index b878924..62ad25f 100644
--- a/src/i18n/zh-CN/projects.ts
+++ b/src/i18n/zh-CN/projects.ts
@@ -22,6 +22,26 @@ export default {
     aiScanNoDesc: 'AI 未能生成描述，可手动填写',
     aiScanFailed: 'AI 扫描失败',
     dirConflict: '该目录已被项目「{name}」绑定',
+    // 导入历史项目(F-260614-06 scan 第二步)
+    importHistory: '📥 导入历史项目',
+    importTitle: '导入历史项目',
+    importSelectRoot: '选择根目录扫描',
+    importScanning: '扫描中…',
+    importRescan: '重新扫描',
+    importEmpty: '该目录下未发现候选项目(需要有 Cargo.toml/package.json/go.mod 等标志文件)',
+    importColName: '项目名',
+    importColPath: '路径',
+    importColStack: '技术栈',
+    importColBound: '已绑定',
+    importMonoHint: 'monorepo',
+    importSelectAll: '全选',
+    importSelected: '已选 {n} 项',
+    importRun: '导入选中项',
+    importRunning: '导入中…',
+    importDone: '导入完成：成功 {imported} 项，跳过 {skipped} 项',
+    importNoSelection: '请先勾选要导入的项目',
+    importScanFailed: '扫描目录失败',
+    importFailed: '批量导入失败',
     // 回收站模态框
     trashTitle: '🗑 回收站',
     trashEmpty: '回收站为空',
diff --git a/src/views/Projects.vue b/src/views/Projects.vue
index 033323c..9ab0f77 100644
--- a/src/views/Projects.vue
+++ b/src/views/Projects.vue
@@ -3,6 +3,7 @@
     <header class="page-header">
       <h1>{{ $t('projects.title') }}</h1>
       <div class="header-actions">
+        <button class="btn btn-ghost" @click="openImportModal">{{ $t('projects.importHistory') }}</button>
         <button class="btn btn-ghost" @click="openTrash">{{ $t('projects.trash') }}</button>
         <button class="btn btn-primary" @click="showCreateModal = true">{{ $t('projects.create') }}</button>
       </div>
@@ -69,6 +70,68 @@
       </div>
     </div>
 
+    <!-- 导入历史项目模态框(F-260614-06 scan 第二步) -->
+    <div v-if="showImportModal" class="modal-overlay" @click.self="closeImportModal">
+      <div class="modal-box import-box">
+        <h3 class="modal-title">{{ $t('projects.importTitle') }}</h3>
+
+        <!-- 选根目录 + 扫描 -->
+        <div class="modal-field">
+          <div class="dir-row">
+            <input v-model="importRootPath" :placeholder="$t('projects.importSelectRoot')" readonly />
+            <button class="btn btn-ghost btn-sm" type="button" @click="pickImportRoot">{{ $t('projects.selectDir') }}</button>
+          </div>
+        </div>
+
+        <!-- 扫描结果表格(只读预览 + 勾选) -->
+        <div v-if="importScanning" class="import-status">{{ $t('projects.importScanning') }}</div>
+        <div v-else-if="importError" class="path-warning">⚠ {{ importError }}</div>
+        <div v-else-if="scannedItems.length === 0 && importRootPath" class="import-status">{{ $t('projects.importEmpty') }}</div>
+
+        <div v-if="scannedItems.length" class="import-table-wrap">
+          <table class="import-table">
+            <thead>
+              <tr>
+                <th class="col-check">
+                  <input type="checkbox" :checked="allImportSelected" :indeterminate.prop="someImportSelected" @change="toggleSelectAll(($event.target as HTMLInputElement).checked)" />
+                </th>
+                <th>{{ $t('projects.importColName') }}</th>
+                <th>{{ $t('projects.importColStack') }}</th>
+                <th>{{ $t('projects.importColPath') }}</th>
+                <th>{{ $t('projects.importColBound') }}</th>
+              </tr>
+            </thead>
+            <tbody>
+              <tr v-for="item in scannedItems" :key="item.path" :class="{ 'row-disabled': item.already_bound }">
+                <td class="col-check">
+                  <input type="checkbox" :disabled="item.already_bound" :checked="isImportSelected(item.path)" @change="toggleSelect(item.path, ($event.target as HTMLInputElement).checked)" />
+                </td>
+                <td>
+                  <span class="imp-name">{{ item.name }}</span>
+                  <span v-if="item.is_monorepo" class="mono-tag">{{ $t('projects.importMonoHint') }}</span>
+                </td>
+                <td>
+                  <span class="tech-tag" v-for="s in item.stack" :key="s">{{ s }}</span>
+                </td>
+                <td class="col-path" :title="item.path">{{ item.path }}</td>
+                <td>
+                  <span v-if="item.already_bound" class="bound-mark">✓</span>
+                </td>
+              </tr>
+            </tbody>
+          </table>
+        </div>
+
+        <div class="modal-actions">
+          <span class="selection-count" v-if="scannedItems.length">{{ $t('projects.importSelected', { n: selectedImportPaths.size }) }}</span>
+          <button class="btn btn-ghost" @click="closeImportModal">{{ $t('common.cancel') }}</button>
+          <button class="btn btn-primary" @click="runImport" :disabled="importRunning || selectedImportPaths.size === 0">
+            {{ importRunning ? $t('projects.importRunning') : $t('projects.importRun') }}
+          </button>
+        </div>
+      </div>
+    </div>
+
     <!-- 项目卡片网格 -->
     <div class="project-grid">
       <div class="project-card" v-for="project in store.projects" :key="project.id" @click="router.push('/projects/' + project.id)">
@@ -110,11 +173,14 @@
 
     <!-- 确认弹层（删除/彻底删除，替代原生 window.confirm） -->
     <ConfirmDialog :visible="confirmState.visible" :msg="confirmState.msg" @result="answerConfirm" />
+
+    <!-- 批量导入结果 toast -->
+    <div v-if="toast.visible" class="toast" :class="'toast-' + toast.type">{{ toast.msg }}</div>
   </div>
 </template>
 
 <script setup lang="ts">
-import { ref, onMounted } from 'vue'
+import { ref, computed, onMounted } from 'vue'
 import { useRouter } from 'vue-router'
 import { useI18n } from 'vue-i18n'
 import { open } from '@tauri-apps/plugin-dialog'
@@ -126,6 +192,7 @@ import { projectStatusLabel as statusLabel, projectBadgeClass as stageClass } fr
 import ConfirmDialog from '@/components/ConfirmDialog.vue'
 import { useConfirm } from '@/composables/useConfirm'
 import type { ProjectRecord } from '@/api/types'
+import type { ScannedProjectItem } from '@/api/project'
 
 const router = useRouter()
 const store = useProjectStore()
@@ -234,6 +301,122 @@ async function handlePurge(project: ProjectRecord) {
 onMounted(() => {
   store.loadProjects()
 })
+
+// ── 导入历史项目(F-260614-06 scan 第二步) ──
+const showImportModal = ref(false)
+const importRootPath = ref('')
+const scannedItems = ref<ScannedProjectItem[]>([])
+const importScanning = ref(false)
+const importError = ref('')
+const importRunning = ref(false)
+const selectedImportPaths = ref<Set<string>>(new Set())
+const toast = ref({ visible: false, msg: '', type: 'info' as 'info' | 'error' })
+let _toastTimer: ReturnType<typeof setTimeout> | null = null
+
+function showToast(msg: string, type: 'info' | 'error' = 'info') {
+  toast.value = { visible: true, msg, type }
+  if (_toastTimer) clearTimeout(_toastTimer)
+  _toastTimer = setTimeout(() => { toast.value.visible = false }, 4000)
+}
+
+const allImportSelected = computed(() =>
+  scannedItems.value.length > 0
+  && scannedItems.value.filter(i => !i.already_bound).every(i => selectedImportPaths.value.has(i.path))
+)
+const someImportSelected = computed(() =>
+  selectedImportPaths.value.size > 0 && !allImportSelected.value
+)
+
+function isImportSelected(path: string) {
+  return selectedImportPaths.value.has(path)
+}
+
+function toggleSelect(path: string, checked: boolean) {
+  const next = new Set(selectedImportPaths.value)
+  if (checked) next.add(path)
+  else next.delete(path)
+  selectedImportPaths.value = next
+}
+
+function toggleSelectAll(checked: boolean) {
+  if (checked) {
+    selectedImportPaths.value = new Set(
+      scannedItems.value.filter(i => !i.already_bound).map(i => i.path)
+    )
+  } else {
+    selectedImportPaths.value = new Set()
+  }
+}
+
+function openImportModal() {
+  showImportModal.value = true
+  importError.value = ''
+  scannedItems.value = []
+  selectedImportPaths.value = new Set()
+}
+
+function closeImportModal() {
+  showImportModal.value = false
+  importRootPath.value = ''
+  scannedItems.value = []
+  selectedImportPaths.value = new Set()
+  importError.value = ''
+  importScanning.value = false
+}
+
+async function pickImportRoot() {
+  try {
+    const selected = await open({ directory: true, multiple: false })
+    if (!selected || Array.isArray(selected)) return
+    importRootPath.value = selected as string
+    await runScan()
+  } catch (e) {
+    console.error('选择根目录失败:', e)
+  }
+}
+
+async function runScan() {
+  if (!importRootPath.value) return
+  importScanning.value = true
+  importError.value = ''
+  scannedItems.value = []
+  selectedImportPaths.value = new Set()
+  try {
+    scannedItems.value = await projectApi.scanDirectoryForProjects(importRootPath.value)
+  } catch (e: any) {
+    importError.value = e?.toString() ?? t('projects.importScanFailed')
+  } finally {
+    importScanning.value = false
+  }
+}
+
+async function runImport() {
+  if (selectedImportPaths.value.size === 0) {
+    showToast(t('projects.importNoSelection'), 'error')
+    return
+  }
+  importRunning.value = true
+  try {
+    const items = Array.from(selectedImportPaths.value).map(p => {
+      const found = scannedItems.value.find(i => i.path === p)
+      return { path: p, name: found?.name }
+    })
+    const result = await projectApi.importProjectsBatch(items)
+    // 刷新列表(批量入库后)
+    await store.loadProjects()
+    showToast(t('projects.importDone', { imported: result.imported, skipped: result.skipped }), result.imported > 0 ? 'info' : 'error')
+    if (result.imported > 0) {
+      closeImportModal()
+    } else {
+      // 全失败:保留弹窗 + 结果,让用户看失败项;重新扫描刷新已绑定态
+      await runScan()
+    }
+  } catch (e: any) {
+    showToast(e?.toString() ?? t('projects.importFailed'), 'error')
+  } finally {
+    importRunning.value = false
+  }
+}
 </script>
 
 <style scoped>
@@ -437,4 +620,35 @@ onMounted(() => {
     grid-template-columns: 1fr;
   }
 }
+
+/* ===== 导入历史项目 ===== */
+.import-box { width: 720px; max-height: 80vh; display: flex; flex-direction: column; }
+.import-status { font-size: 13px; color: var(--df-text-dim); padding: 16px 0; text-align: center; }
+.import-table-wrap { flex: 1; overflow: auto; border: 0.5px solid var(--df-border); border-radius: var(--df-radius-sm); margin-bottom: 8px; }
+.import-table { width: 100%; border-collapse: collapse; font-size: 12px; }
+.import-table thead th {
+  position: sticky; top: 0; background: var(--df-bg);
+  text-align: left; padding: 8px 10px; font-weight: 500;
+  color: var(--df-text-dim); border-bottom: 0.5px solid var(--df-border);
+}
+.import-table tbody td { padding: 8px 10px; border-bottom: 0.5px solid var(--df-border); vertical-align: top; color: var(--df-text-secondary); }
+.import-table tbody tr:last-child td { border-bottom: none; }
+.import-table .col-check { width: 32px; text-align: center; }
+.import-table .col-path { max-width: 260px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; font-family: var(--df-mono, monospace); font-size: 11px; }
+.row-disabled { opacity: 0.5; }
+.imp-name { font-weight: 500; color: var(--df-text); }
+.mono-tag { margin-left: 6px; font-size: 10px; padding: 1px 6px; border-radius: var(--df-radius-xs); background: rgba(255,217,61,0.15); color: var(--df-warning); border: 0.5px solid var(--df-border); }
+.bound-mark { color: var(--df-success); font-weight: 600; }
+.selection-count { margin-right: auto; font-size: 12px; color: var(--df-text-dim); }
+.import-box .modal-actions { align-items: center; }
+
+/* ===== Toast ===== */
+.toast {
+  position: fixed; bottom: 24px; left: 50%; transform: translateX(-50%);
+  padding: 10px 18px; border-radius: var(--df-radius-sm);
+  font-size: 13px; z-index: 200; max-width: 80vw;
+  box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+}
+.toast-info { background: var(--df-accent); color: #fff; }
+.toast-error { background: var(--df-danger); color: #fff; }
 </style>