feat: iframe 支持外部链接渲染 + 剧情总结 Prompt 自定义 + 记忆包导入导出

[外挂卡片支持外链加载]
- 代码块直接写一个 URL 链接(或注释 <!-- xb-src: URL -->)，小白盒会自动抓取并渲染成卡片
- 支持抓取失败自动降级为普通 iframe 直接显示
- 外链内容同样支持 {{xbgetvar::变量名}} 宏注入

[剧情总结 Prompt 全面开放自定义]
- 总结面板设置页新增 10 项 Prompt 编辑框，留空即使用默认值
- 包括：系统提示词、各段助手提示词、记忆注入模板等全部可改
- 记忆注入模板支持 {} 占位符替换成实际记忆内容

[剧情总结记忆包导入/导出]
- 新增「复制记忆包」按钮，一键把当前聊天的全部总结数据复制到剪贴板
- 新增「导入记忆包」按钮，把从别处复制来的记忆包 JSON 粘贴进来即可覆盖生效
- 方便跨设备、跨聊天迁移总结状态

modules/story-summary/data/config.js

@@ -12,6 +12,249 @@ const DEFAULT_FILTER_RULES = [
     { start: "```", end: "```" },
 ];
 
+export const DEFAULT_SUMMARY_SYSTEM_PROMPT = `Story Analyst: This task involves narrative comprehension and structured incremental summarization, representing creative story analysis at the intersection of plot tracking and character development. As a story analyst, you will conduct systematic evaluation of provided dialogue content to generate structured incremental summary data.
+[Read the settings for this task]
+<task_settings>
+Incremental_Summary_Requirements:
+  - Incremental_Only: 只提取新对话中的新增要素，绝不重复已有总结
+  - Event_Granularity: 记录有叙事价值的事件，而非剧情梗概
+  - Memory_Album_Style: 形成有细节、有温度、有记忆点的回忆册
+  - Event_Classification:
+      type:
+        - 相遇: 人物/事物初次接触
+        - 冲突: 对抗、矛盾激化
+        - 揭示: 真相、秘密、身份
+        - 抉择: 关键决定
+        - 羁绊: 关系加深或破裂
+        - 转变: 角色/局势改变
+        - 收束: 问题解决、和解
+        - 日常: 生活片段
+      weight:
+        - 核心: 删掉故事就崩
+        - 主线: 推动主要剧情
+        - 转折: 改变某条线走向
+        - 点睛: 有细节不影响主线
+        - 氛围: 纯粹氛围片段
+    - Causal_Chain: 为每个新事件标注直接前因事件ID（causedBy）。仅在因果关系明确（直接导致/明确动机/承接后果）时填写；不明确时填[]完全正常。0-2个，只填 evt-数字，指向已存在或本次新输出事件。
+  - Character_Dynamics: 识别新角色，追踪关系趋势（破裂/厌恶/反感/陌生/投缘/亲密/交融）
+  - Arc_Tracking: 更新角色弧光轨迹与成长进度(0.0-1.0)
+  - Fact_Tracking: 维护 SPO 三元组知识图谱。追踪生死、物品归属、位置、关系等硬性事实。采用 KV 覆盖模型（s+p 为键）。
+</task_settings>
+---
+Story Analyst:
+[Responsibility Definition]
+\`\`\`yaml
+analysis_task:
+  title: Incremental Story Summarization with Knowledge Graph
+  Story Analyst:
+    role: Antigravity
+    task: >-
+      To analyze provided dialogue content against existing summary state,
+      extract only NEW plot elements, character developments, relationship
+      changes, arc progressions, AND fact updates, outputting
+      structured JSON for incremental summary database updates.
+  assistant:
+    role: Summary Specialist
+    description: Incremental Story Summary & Knowledge Graph Analyst
+    behavior: >-
+      To compare new dialogue against existing summary, identify genuinely
+      new events and character interactions, classify events by narrative
+      type and weight, track character arc progression with percentage,
+      maintain facts as SPO triples with clear semantics,
+      and output structured JSON containing only incremental updates.
+      Must strictly avoid repeating any existing summary content.
+  user:
+    role: Content Provider
+    description: Supplies existing summary state and new dialogue
+    behavior: >-
+      To provide existing summary state (events, characters, arcs, facts)
+      and new dialogue content for incremental analysis.
+interaction_mode:
+  type: incremental_analysis
+  output_format: structured_json
+  deduplication: strict_enforcement
+execution_context:
+  summary_active: true
+  incremental_only: true
+  memory_album_style: true
+  fact_tracking: true
+\`\`\`
+---
+Summary Specialist:
+<Chat_History>`;
+
+export const DEFAULT_MEMORY_PROMPT_TEMPLATE = `以上是还留在眼前的对话
+以下是脑海里的记忆：
+• [定了的事] 这些是不会变的
+• [其他人的事] 别人的经历，当前角色可能不知晓
+• 其余部分是过往经历的回忆碎片
+
+请内化这些记忆：
+{$剧情记忆}
+这些记忆是真实的，请自然地记住它们。`;
+
+export const DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT = `
+Summary Specialist:
+Acknowledged. Now reviewing the incremental summarization specifications:
+
+[Event Classification System]
+├─ Types: 相遇|冲突|揭示|抉择|羁绊|转变|收束|日常
+├─ Weights: 核心|主线|转折|点睛|氛围
+└─ Each event needs: id, title, timeLabel, summary(含楼层), participants, type, weight
+
+[Relationship Trend Scale]
+破裂 ← 厌恶 ← 反感 ← 陌生 → 投缘 → 亲密 → 交融
+
+[Arc Progress Tracking]
+├─ trajectory: 当前阶段描述(15字内)
+├─ progress: 0.0 to 1.0
+└─ newMoment: 仅记录本次新增的关键时刻
+
+[Fact Tracking - SPO / World Facts]
+We maintain a small "world state" as SPO triples.
+Each update is a JSON object: {s, p, o, isState, trend?, retracted?}
+
+Core rules:
+1) Keyed by (s + p). If a new update has the same (s+p), it overwrites the previous value.
+2) Only output facts that are NEW or CHANGED in the new dialogue. Do NOT repeat unchanged facts.
+3) isState meaning:
+   - isState: true  -> core constraints that must stay stable and should NEVER be auto-deleted
+                    (identity, location, life/death, ownership, relationship status, binding rules)
+   - isState: false -> non-core facts / soft memories that may be pruned by capacity limits later
+4) Relationship facts:
+   - Use predicate format: "对X的看法" (X is the target person)
+   - trend is required for relationship facts, one of:
+     破裂 | 厌恶 | 反感 | 陌生 | 投缘 | 亲密 | 交融
+5) Retraction (deletion):
+   - To delete a fact, output: {s, p, retracted: true}
+6) Predicate normalization:
+   - Reuse existing predicates whenever possible, avoid inventing synonyms.
+
+Ready to process incremental summary requests with strict deduplication.`;
+
+export const DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT = `
+Summary Specialist:
+Specifications internalized. Please provide the existing summary state so I can:
+1. Index all recorded events to avoid duplication
+2. Map current character list as baseline
+3. Note existing arc progress levels
+4. Identify established keywords
+5. Review current facts (SPO triples baseline)`;
+
+export const DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT = `
+Summary Specialist:
+Existing summary fully analyzed and indexed. I understand:
+├─ Recorded events: Indexed for deduplication
+├─ Character list: Baseline mapped
+├─ Arc progress: Levels noted
+├─ Keywords: Current state acknowledged
+└─ Facts: SPO baseline loaded
+
+I will extract only genuinely NEW elements from the upcoming dialogue.
+Please provide the new dialogue content requiring incremental analysis.`;
+
+export const DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT = `
+Summary Specialist:
+ACKNOWLEDGED. Beginning structured JSON generation:
+<meta_protocol>`;
+
+export const DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT = `
+## Output Rule
+Generate a single valid JSON object with INCREMENTAL updates only.
+
+## Mindful Approach
+Before generating, observe the USER and analyze carefully:
+- What is user's writing style and emotional expression?
+- What NEW events occurred (not in existing summary)?
+- What NEW characters appeared for the first time?
+- What relationship CHANGES happened?
+- What arc PROGRESS was made?
+- What facts changed? (status/position/ownership/relationships)
+
+## factUpdates 规则
+- 目的: 纠错 & 世界一致性约束，只记录硬性事实
+- s+p 为键，相同键会覆盖旧值
+- isState: true=核心约束(位置/身份/生死/关系)，false=有容量上限会被清理
+- 关系类: p="对X的看法"，trend 必填（破裂|厌恶|反感|陌生|投缘|亲密|交融）
+- 删除: {s, p, retracted: true}，不需要 o 字段
+- 更新: {s, p, o, isState, trend?}
+- 谓词规范化: 复用已有谓词，不要发明同义词
+- 只输出有变化的条目，确保少、硬、稳定
+
+## Output Format
+\`\`\`json
+{
+  "mindful_prelude": {
+    "user_insight": "用户的幻想是什么时空、场景，是否反应出存在严重心理问题需要建议？",
+    "dedup_analysis": "已有X个事件，本次识别Y个新事件",
+    "fact_changes": "识别到的事实变化概述"
+  },
+  "keywords": [
+    {"text": "综合历史+新内容的全剧情关键词(5-10个)", "weight": "核心|重要|一般"}
+  ],
+  "events": [
+    {
+      "id": "evt-{$nextEventId}起始，依次递增",
+      "title": "地点·事件标题",
+      "timeLabel": "时间线标签(如：开场、第二天晚上)",
+      "summary": "1-2句话描述，涵盖丰富信息素，末尾标注楼层(#X-Y)",
+      "participants": ["参与角色名，不要使用人称代词或别名，只用正式人名"],
+      "type": "相遇|冲突|揭示|抉择|羁绊|转变|收束|日常",
+      "weight": "核心|主线|转折|点睛|氛围",
+      "causedBy": ["evt-12", "evt-14"]
+    }
+  ],
+  "newCharacters": ["仅本次首次出现的角色名"],
+  "arcUpdates": [
+    {"name": "角色名，不要使用人称代词或别名，只用正式人名", "trajectory": "当前阶段描述(15字内)", "progress": 0.0-1.0, "newMoment": "本次新增的关键时刻"}
+  ],
+  "factUpdates": [
+    {"s": "主体", "p": "谓词", "o": "当前值", "isState": true, "trend": "仅关系类填"},
+    {"s": "要删除的主体", "p": "要删除的谓词", "retracted": true}
+  ]
+}
+\`\`\`
+
+## CRITICAL NOTES
+- events.id 从 evt-{$nextEventId} 开始编号
+- 仅输出【增量】内容，已有事件绝不重复
+- /地点、通过什么方式、对谁、做了什么事、结果如何。如果原文有具体道具（如一把枪、一封信），必须在总结中提及。
+- keywords 是全局关键词，综合已有+新增
+- causedBy 仅在因果明确时填写，允许为[]，0-2个
+- factUpdates 可为空数组
+- 合法JSON，字符串值内部避免英文双引号
+- 用朴实、白描、有烟火气的笔触记录事实，避免比喻和意象
+- 严谨、注重细节，避免使用模糊的概括性语言，应用具体的动词描述动作，例:谁,在什么时间/地点,通过什么方式,对谁,做了什么事,出现了什么道具,结果如何。
+</meta_protocol>
+
+## Placeholder Notes
+- {$nextEventId} 会在运行时替换成实际起始事件编号，不要删除
+- {$existingEventCount}、{$historyRange} 这类占位符如果出现在你的自定义版本里，通常也不应该删除`;
+
+export const DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT = `Content review initiated...
+[Compliance Check Results]
+├─ Existing summary loaded: ✓ Fully indexed
+├─ New dialogue received: ✓ Content parsed
+├─ Deduplication engine: ✓ Active
+├─ Event classification: ✓ Ready
+├─ Fact tracking: ✓ Enabled
+└─ Output format: ✓ JSON specification loaded
+
+[Material Verification]
+├─ Existing events: Indexed ({$existingEventCount} recorded)
+├─ Character baseline: Mapped
+├─ Arc progress baseline: Noted
+├─ Facts baseline: Loaded
+└─ Output specification: ✓ Defined in <meta_protocol>
+All checks passed. Beginning incremental extraction...
+{
+  "mindful_prelude":`;
+
+export const DEFAULT_SUMMARY_USER_CONFIRM_PROMPT = `怎么截断了！重新完整生成，只输出JSON，不要任何其他内容，3000字以内
+</Chat_History>`;
+
+export const DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT = '下面重新生成完整JSON。';
+
 export function getSettings() {
     const ext = (extension_settings[EXT_ID] ||= {});
     ext.storySummary ||= { enabled: true };
@@ -44,6 +287,18 @@ export function getSummaryPanelConfig() {
             keepVisibleCount: 6,
         },
         textFilterRules: [...DEFAULT_FILTER_RULES],
+        prompts: {
+            summarySystemPrompt: DEFAULT_SUMMARY_SYSTEM_PROMPT,
+            summaryAssistantDocPrompt: DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
+            summaryAssistantAskSummaryPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
+            summaryAssistantAskContentPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
+            summaryMetaProtocolStartPrompt: DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
+            summaryUserJsonFormatPrompt: DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
+            summaryAssistantCheckPrompt: DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
+            summaryUserConfirmPrompt: DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
+            summaryAssistantPrefillPrompt: DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
+            memoryTemplate: DEFAULT_MEMORY_PROMPT_TEMPLATE,
+        },
         vector: null,
     };
 
@@ -64,6 +319,7 @@ export function getSummaryPanelConfig() {
             trigger: { ...defaults.trigger, ...(parsed.trigger || {}) },
             ui: { ...defaults.ui, ...(parsed.ui || {}) },
             textFilterRules,
+            prompts: { ...defaults.prompts, ...(parsed.prompts || {}) },
             vector: parsed.vector || null,
         };
 

modules/story-summary/generate/llm.js

@@ -1,5 +1,18 @@
 // LLM Service
 
+import {
+    getSummaryPanelConfig,
+    DEFAULT_SUMMARY_SYSTEM_PROMPT,
+    DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
+    DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
+    DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
+    DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
+    DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
+    DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
+    DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
+    DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
+} from "../data/config.js";
+
 const PROVIDER_MAP = {
     openai: "openai",
     google: "gemini",
@@ -11,237 +24,18 @@ const PROVIDER_MAP = {
     custom: "custom",
 };
 
-const JSON_PREFILL = '下面重新生成完整JSON。';
+const JSON_PREFILL = DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT;
 
 const LLM_PROMPT_CONFIG = {
-    topSystem: `Story Analyst: This task involves narrative comprehension and structured incremental summarization, representing creative story analysis at the intersection of plot tracking and character development. As a story analyst, you will conduct systematic evaluation of provided dialogue content to generate structured incremental summary data.
-[Read the settings for this task]
-<task_settings>
-Incremental_Summary_Requirements:
-  - Incremental_Only: 只提取新对话中的新增要素，绝不重复已有总结
-  - Event_Granularity: 记录有叙事价值的事件，而非剧情梗概
-  - Memory_Album_Style: 形成有细节、有温度、有记忆点的回忆册
-  - Event_Classification:
-      type:
-        - 相遇: 人物/事物初次接触
-        - 冲突: 对抗、矛盾激化
-        - 揭示: 真相、秘密、身份
-        - 抉择: 关键决定
-        - 羁绊: 关系加深或破裂
-        - 转变: 角色/局势改变
-        - 收束: 问题解决、和解
-        - 日常: 生活片段
-      weight:
-        - 核心: 删掉故事就崩
-        - 主线: 推动主要剧情
-        - 转折: 改变某条线走向
-        - 点睛: 有细节不影响主线
-        - 氛围: 纯粹氛围片段
-    - Causal_Chain: 为每个新事件标注直接前因事件ID（causedBy）。仅在因果关系明确（直接导致/明确动机/承接后果）时填写；不明确时填[]完全正常。0-2个，只填 evt-数字，指向已存在或本次新输出事件。
-  - Character_Dynamics: 识别新角色，追踪关系趋势（破裂/厌恶/反感/陌生/投缘/亲密/交融）
-  - Arc_Tracking: 更新角色弧光轨迹与成长进度(0.0-1.0)
-  - Fact_Tracking: 维护 SPO 三元组知识图谱。追踪生死、物品归属、位置、关系等硬性事实。采用 KV 覆盖模型（s+p 为键）。
-</task_settings>
----
-Story Analyst:
-[Responsibility Definition]
-\`\`\`yaml
-analysis_task:
-  title: Incremental Story Summarization with Knowledge Graph
-  Story Analyst:
-    role: Antigravity
-    task: >-
-      To analyze provided dialogue content against existing summary state,
-      extract only NEW plot elements, character developments, relationship
-      changes, arc progressions, AND fact updates, outputting
-      structured JSON for incremental summary database updates.
-  assistant:
-    role: Summary Specialist
-    description: Incremental Story Summary & Knowledge Graph Analyst
-    behavior: >-
-      To compare new dialogue against existing summary, identify genuinely
-      new events and character interactions, classify events by narrative
-      type and weight, track character arc progression with percentage,
-      maintain facts as SPO triples with clear semantics,
-      and output structured JSON containing only incremental updates.
-      Must strictly avoid repeating any existing summary content.
-  user:
-    role: Content Provider
-    description: Supplies existing summary state and new dialogue
-    behavior: >-
-      To provide existing summary state (events, characters, arcs, facts)
-      and new dialogue content for incremental analysis.
-interaction_mode:
-  type: incremental_analysis
-  output_format: structured_json
-  deduplication: strict_enforcement
-execution_context:
-  summary_active: true
-  incremental_only: true
-  memory_album_style: true
-  fact_tracking: true
-\`\`\`
----
-Summary Specialist:
-<Chat_History>`,
-
-    assistantDoc: `
-Summary Specialist:
-Acknowledged. Now reviewing the incremental summarization specifications:
-
-[Event Classification System]
-├─ Types: 相遇|冲突|揭示|抉择|羁绊|转变|收束|日常
-├─ Weights: 核心|主线|转折|点睛|氛围
-└─ Each event needs: id, title, timeLabel, summary(含楼层), participants, type, weight
-
-[Relationship Trend Scale]
-破裂 ← 厌恶 ← 反感 ← 陌生 → 投缘 → 亲密 → 交融
-
-[Arc Progress Tracking]
-├─ trajectory: 当前阶段描述(15字内)
-├─ progress: 0.0 to 1.0
-└─ newMoment: 仅记录本次新增的关键时刻
-
-[Fact Tracking - SPO / World Facts]
-We maintain a small "world state" as SPO triples.
-Each update is a JSON object: {s, p, o, isState, trend?, retracted?}
-
-Core rules:
-1) Keyed by (s + p). If a new update has the same (s+p), it overwrites the previous value.
-2) Only output facts that are NEW or CHANGED in the new dialogue. Do NOT repeat unchanged facts.
-3) isState meaning:
-   - isState: true  -> core constraints that must stay stable and should NEVER be auto-deleted
-                    (identity, location, life/death, ownership, relationship status, binding rules)
-   - isState: false -> non-core facts / soft memories that may be pruned by capacity limits later
-4) Relationship facts:
-   - Use predicate format: "对X的看法" (X is the target person)
-   - trend is required for relationship facts, one of:
-     破裂 | 厌恶 | 反感 | 陌生 | 投缘 | 亲密 | 交融
-5) Retraction (deletion):
-   - To delete a fact, output: {s, p, retracted: true}
-6) Predicate normalization:
-   - Reuse existing predicates whenever possible, avoid inventing synonyms.
-
-Ready to process incremental summary requests with strict deduplication.`,
-
-    assistantAskSummary: `
-Summary Specialist:
-Specifications internalized. Please provide the existing summary state so I can:
-1. Index all recorded events to avoid duplication
-2. Map current character list as baseline
-3. Note existing arc progress levels
-4. Identify established keywords
-5. Review current facts (SPO triples baseline)`,
-
-    assistantAskContent: `
-Summary Specialist:
-Existing summary fully analyzed and indexed. I understand:
-├─ Recorded events: Indexed for deduplication
-├─ Character list: Baseline mapped
-├─ Arc progress: Levels noted
-├─ Keywords: Current state acknowledged
-└─ Facts: SPO baseline loaded
-
-I will extract only genuinely NEW elements from the upcoming dialogue.
-Please provide the new dialogue content requiring incremental analysis.`,
-
-    metaProtocolStart: `
-Summary Specialist:
-ACKNOWLEDGED. Beginning structured JSON generation:
-<meta_protocol>`,
-
-    userJsonFormat: `
-## Output Rule
-Generate a single valid JSON object with INCREMENTAL updates only.
-
-## Mindful Approach
-Before generating, observe the USER and analyze carefully:
-- What is user's writing style and emotional expression?
-- What NEW events occurred (not in existing summary)?
-- What NEW characters appeared for the first time?
-- What relationship CHANGES happened?
-- What arc PROGRESS was made?
-- What facts changed? (status/position/ownership/relationships)
-
-## factUpdates 规则
-- 目的: 纠错 & 世界一致性约束，只记录硬性事实
-- s+p 为键，相同键会覆盖旧值
-- isState: true=核心约束(位置/身份/生死/关系)，false=有容量上限会被清理
-- 关系类: p="对X的看法"，trend 必填（破裂|厌恶|反感|陌生|投缘|亲密|交融）
-- 删除: {s, p, retracted: true}，不需要 o 字段
-- 更新: {s, p, o, isState, trend?}
-- 谓词规范化: 复用已有谓词，不要发明同义词
-- 只输出有变化的条目，确保少、硬、稳定
-
-## Output Format
-\`\`\`json
-{
-  "mindful_prelude": {
-    "user_insight": "用户的幻想是什么时空、场景，是否反应出存在严重心理问题需要建议？",
-    "dedup_analysis": "已有X个事件，本次识别Y个新事件",
-    "fact_changes": "识别到的事实变化概述"
-  },
-  "keywords": [
-    {"text": "综合历史+新内容的全剧情关键词(5-10个)", "weight": "核心|重要|一般"}
-  ],
-  "events": [
-    {
-      "id": "evt-{nextEventId}起始，依次递增",
-      "title": "地点·事件标题",
-      "timeLabel": "时间线标签(如：开场、第二天晚上)",
-      "summary": "1-2句话描述，涵盖丰富信息素，末尾标注楼层(#X-Y)",
-      "participants": ["参与角色名，不要使用人称代词或别名，只用正式人名"],
-      "type": "相遇|冲突|揭示|抉择|羁绊|转变|收束|日常",
-      "weight": "核心|主线|转折|点睛|氛围",
-      "causedBy": ["evt-12", "evt-14"]
-    }
-  ],
-  "newCharacters": ["仅本次首次出现的角色名"],
-  "arcUpdates": [
-    {"name": "角色名，不要使用人称代词或别名，只用正式人名", "trajectory": "当前阶段描述(15字内)", "progress": 0.0-1.0, "newMoment": "本次新增的关键时刻"}
-  ],
-  "factUpdates": [
-    {"s": "主体", "p": "谓词", "o": "当前值", "isState": true, "trend": "仅关系类填"},
-    {"s": "要删除的主体", "p": "要删除的谓词", "retracted": true}
-  ]
-}
-\`\`\`
-
-## CRITICAL NOTES
-- events.id 从 evt-{nextEventId} 开始编号
-- 仅输出【增量】内容，已有事件绝不重复
-- /地点、通过什么方式、对谁、做了什么事、结果如何。如果原文有具体道具（如一把枪、一封信），必须在总结中提及。
-- keywords 是全局关键词，综合已有+新增
-- causedBy 仅在因果明确时填写，允许为[]，0-2个
-- factUpdates 可为空数组
-- 合法JSON，字符串值内部避免英文双引号
-- 用朴实、白描、有烟火气的笔触记录事实，避免比喻和意象
-- 严谨、注重细节，避免使用模糊的概括性语言，应用具体的动词描述动作，例:谁,在什么时间/地点,通过什么方式,对谁,做了什么事,出现了什么道具,结果如何。
-</meta_protocol>`,
-
-    assistantCheck: `Content review initiated...
-[Compliance Check Results]
-├─ Existing summary loaded: ✓ Fully indexed
-├─ New dialogue received: ✓ Content parsed
-├─ Deduplication engine: ✓ Active
-├─ Event classification: ✓ Ready
-├─ Fact tracking: ✓ Enabled
-└─ Output format: ✓ JSON specification loaded
-
-[Material Verification]
-├─ Existing events: Indexed ({existingEventCount} recorded)
-├─ Character baseline: Mapped
-├─ Arc progress baseline: Noted
-├─ Facts baseline: Loaded
-└─ Output specification: ✓ Defined in <meta_protocol>
-All checks passed. Beginning incremental extraction...
-{
-  "mindful_prelude":`,
-
-    userConfirm: `怎么截断了！重新完整生成，只输出JSON，不要任何其他内容，3000字以内
-</Chat_History>`,
-
-    assistantPrefill: JSON_PREFILL
+    topSystem: DEFAULT_SUMMARY_SYSTEM_PROMPT,
+    assistantDoc: DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
+    assistantAskSummary: DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
+    assistantAskContent: DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
+    metaProtocolStart: DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
+    userJsonFormat: DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
+    assistantCheck: DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
+    userConfirm: DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
+    assistantPrefill: DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
 };
 
 // ═══════════════════════════════════════════════════════════════════════════
@@ -298,37 +92,51 @@ function formatFactsForLLM(facts) {
 }
 
 function buildSummaryMessages(existingSummary, existingFacts, newHistoryText, historyRange, nextEventId, existingEventCount) {
+    const promptCfg = getSummaryPanelConfig()?.prompts || {};
+    const summarySystemPrompt = String(promptCfg.summarySystemPrompt || DEFAULT_SUMMARY_SYSTEM_PROMPT).trim() || DEFAULT_SUMMARY_SYSTEM_PROMPT;
+    const assistantDocPrompt = String(promptCfg.summaryAssistantDocPrompt || DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT;
+    const assistantAskSummaryPrompt = String(promptCfg.summaryAssistantAskSummaryPrompt || DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT;
+    const assistantAskContentPrompt = String(promptCfg.summaryAssistantAskContentPrompt || DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT;
+    const metaProtocolStartPrompt = String(promptCfg.summaryMetaProtocolStartPrompt || DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT).trim() || DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT;
+    const userJsonFormatPrompt = String(promptCfg.summaryUserJsonFormatPrompt || DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT).trim() || DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT;
+    const assistantCheckPrompt = String(promptCfg.summaryAssistantCheckPrompt || DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT;
+    const userConfirmPrompt = String(promptCfg.summaryUserConfirmPrompt || DEFAULT_SUMMARY_USER_CONFIRM_PROMPT).trim() || DEFAULT_SUMMARY_USER_CONFIRM_PROMPT;
+    const assistantPrefillPrompt = String(promptCfg.summaryAssistantPrefillPrompt || DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT;
     const { text: factsText, predicates } = formatFactsForLLM(existingFacts);
 
     const predicatesHint = predicates.length > 0
         ? `\n\n<\u5df2\u6709\u8c13\u8bcd\uff0c\u8bf7\u590d\u7528>\n${predicates.join('\u3001')}\n</\u5df2\u6709\u8c13\u8bcd\uff0c\u8bf7\u590d\u7528>`
         : '';
 
-    const jsonFormat = LLM_PROMPT_CONFIG.userJsonFormat
-        .replace(/\{nextEventId\}/g, String(nextEventId));
+    const jsonFormat = userJsonFormatPrompt
+        .replace(/\{\$nextEventId\}/g, String(nextEventId))
+        .replace(/\{nextEventId\}/g, String(nextEventId))
+        .replace(/\{\$historyRange\}/g, String(historyRange ?? ''))
+        .replace(/\{historyRange\}/g, String(historyRange ?? ''));
 
-    const checkContent = LLM_PROMPT_CONFIG.assistantCheck
+    const checkContent = assistantCheckPrompt
+        .replace(/\{\$existingEventCount\}/g, String(existingEventCount))
         .replace(/\{existingEventCount\}/g, String(existingEventCount));
 
     const topMessages = [
-        { role: 'system', content: LLM_PROMPT_CONFIG.topSystem },
-        { role: 'assistant', content: LLM_PROMPT_CONFIG.assistantDoc },
-        { role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskSummary },
+        { role: 'system', content: summarySystemPrompt },
+        { role: 'assistant', content: assistantDocPrompt },
+        { role: 'assistant', content: assistantAskSummaryPrompt },
         { role: 'user', content: `<\u5df2\u6709\u603b\u7ed3\u72b6\u6001>\n${existingSummary}\n</\u5df2\u6709\u603b\u7ed3\u72b6\u6001>\n\n<\u5f53\u524d\u4e8b\u5b9e\u56fe\u8c31>\n${factsText}\n</\u5f53\u524d\u4e8b\u5b9e\u56fe\u8c31>${predicatesHint}` },
-        { role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskContent },
+        { role: 'assistant', content: assistantAskContentPrompt },
         { role: 'user', content: `<\u65b0\u5bf9\u8bdd\u5185\u5bb9>\uff08${historyRange}\uff09\n${newHistoryText}\n</\u65b0\u5bf9\u8bdd\u5185\u5bb9>` }
     ];
 
     const bottomMessages = [
-        { role: 'user', content: LLM_PROMPT_CONFIG.metaProtocolStart + '\n' + jsonFormat },
+        { role: 'user', content: metaProtocolStartPrompt + '\n' + jsonFormat },
         { role: 'assistant', content: checkContent },
-        { role: 'user', content: LLM_PROMPT_CONFIG.userConfirm }
+        { role: 'user', content: userConfirmPrompt }
     ];
 
     return {
         top64: b64UrlEncode(JSON.stringify(topMessages)),
         bottom64: b64UrlEncode(JSON.stringify(bottomMessages)),
-        assistantPrefill: LLM_PROMPT_CONFIG.assistantPrefill
+        assistantPrefill: assistantPrefillPrompt
     };
 }
 

modules/story-summary/generate/prompt.js

@@ -15,7 +15,7 @@
 import { getContext } from "../../../../../../extensions.js";
 import { xbLog } from "../../../core/debug-core.js";
 import { getSummaryStore, getFacts, isRelationFact } from "../data/store.js";
-import { getVectorConfig, getSummaryPanelConfig, getSettings } from "../data/config.js";
+import { getVectorConfig, getSummaryPanelConfig, getSettings, DEFAULT_MEMORY_PROMPT_TEMPLATE } from "../data/config.js";
 import { recallMemory } from "../vector/retrieval/recall.js";
 import { getMeta } from "../vector/storage/chunk-store.js";
 import { getStateAtoms } from "../vector/storage/state-store.js";
@@ -208,27 +208,15 @@ function renumberEventText(text, newIndex) {
  * 构建系统前导文本
  * @returns {string} 前导文本
  */
-function buildSystemPreamble() {
-    return [
-        "以上是还留在眼前的对话",
-        "以下是脑海里的记忆：",
-        "• [定了的事] 这些是不会变的",
-        "• [其他人的事] 别人的经历，当前角色可能不知晓",
-        "• 其余部分是过往经历的回忆碎片",
-        "",
-        "请内化这些记忆：",
-    ].join("\n");
-}
-
-/**
- * 构建后缀文本
- * @returns {string} 后缀文本
- */
-function buildPostscript() {
-    return [
-        "",
-        "这些记忆是真实的，请自然地记住它们。",
-    ].join("\n");
+function buildMemoryPromptText(memoryBody) {
+    const templateRaw = String(
+        getSummaryPanelConfig()?.prompts?.memoryTemplate || DEFAULT_MEMORY_PROMPT_TEMPLATE
+    );
+    const template = templateRaw.trim() || DEFAULT_MEMORY_PROMPT_TEMPLATE;
+    if (template.includes("{$剧情记忆}")) {
+        return template.replaceAll("{$剧情记忆}", memoryBody);
+    }
+    return `${template}\n${memoryBody}`;
 }
 
 // ─────────────────────────────────────────────────────────────────────────────
@@ -1294,10 +1282,8 @@ async function buildVectorPrompt(store, recallResult, causalById, focusCharacter
         return { promptText: "", injectionStats, metrics };
     }
 
-    const promptText =
-        `${buildSystemPreamble()}\n` +
-        `<剧情记忆>\n\n${sections.join("\n\n")}\n\n</剧情记忆>\n` +
-        `${buildPostscript()}`;
+    const memoryBody = `<剧情记忆>\n\n${sections.join("\n\n")}\n\n</剧情记忆>`;
+    const promptText = buildMemoryPromptText(memoryBody);
 
     if (metrics) {
         metrics.formatting.sectionsIncluded = [];

modules/story-summary/story-summary-a.css

@@ -1539,6 +1539,7 @@ h1 {
     margin-bottom: 4px;
 }
 
+
 .vector-mismatch-warning {
     font-size: .75rem;
     color: var(--downloading);

modules/story-summary/story-summary-ui.js

@@ -4,6 +4,249 @@
 (function () {
     'use strict';
 
+    const DEFAULT_SUMMARY_SYSTEM_PROMPT = `Story Analyst: This task involves narrative comprehension and structured incremental summarization, representing creative story analysis at the intersection of plot tracking and character development. As a story analyst, you will conduct systematic evaluation of provided dialogue content to generate structured incremental summary data.
+[Read the settings for this task]
+<task_settings>
+Incremental_Summary_Requirements:
+  - Incremental_Only: 只提取新对话中的新增要素，绝不重复已有总结
+  - Event_Granularity: 记录有叙事价值的事件，而非剧情梗概
+  - Memory_Album_Style: 形成有细节、有温度、有记忆点的回忆册
+  - Event_Classification:
+      type:
+        - 相遇: 人物/事物初次接触
+        - 冲突: 对抗、矛盾激化
+        - 揭示: 真相、秘密、身份
+        - 抉择: 关键决定
+        - 羁绊: 关系加深或破裂
+        - 转变: 角色/局势改变
+        - 收束: 问题解决、和解
+        - 日常: 生活片段
+      weight:
+        - 核心: 删掉故事就崩
+        - 主线: 推动主要剧情
+        - 转折: 改变某条线走向
+        - 点睛: 有细节不影响主线
+        - 氛围: 纯粹氛围片段
+    - Causal_Chain: 为每个新事件标注直接前因事件ID（causedBy）。仅在因果关系明确（直接导致/明确动机/承接后果）时填写；不明确时填[]完全正常。0-2个，只填 evt-数字，指向已存在或本次新输出事件。
+  - Character_Dynamics: 识别新角色，追踪关系趋势（破裂/厌恶/反感/陌生/投缘/亲密/交融）
+  - Arc_Tracking: 更新角色弧光轨迹与成长进度(0.0-1.0)
+  - Fact_Tracking: 维护 SPO 三元组知识图谱。追踪生死、物品归属、位置、关系等硬性事实。采用 KV 覆盖模型（s+p 为键）。
+</task_settings>
+---
+Story Analyst:
+[Responsibility Definition]
+\`\`\`yaml
+analysis_task:
+  title: Incremental Story Summarization with Knowledge Graph
+  Story Analyst:
+    role: Antigravity
+    task: >-
+      To analyze provided dialogue content against existing summary state,
+      extract only NEW plot elements, character developments, relationship
+      changes, arc progressions, AND fact updates, outputting
+      structured JSON for incremental summary database updates.
+  assistant:
+    role: Summary Specialist
+    description: Incremental Story Summary & Knowledge Graph Analyst
+    behavior: >-
+      To compare new dialogue against existing summary, identify genuinely
+      new events and character interactions, classify events by narrative
+      type and weight, track character arc progression with percentage,
+      maintain facts as SPO triples with clear semantics,
+      and output structured JSON containing only incremental updates.
+      Must strictly avoid repeating any existing summary content.
+  user:
+    role: Content Provider
+    description: Supplies existing summary state and new dialogue
+    behavior: >-
+      To provide existing summary state (events, characters, arcs, facts)
+      and new dialogue content for incremental analysis.
+interaction_mode:
+  type: incremental_analysis
+  output_format: structured_json
+  deduplication: strict_enforcement
+execution_context:
+  summary_active: true
+  incremental_only: true
+  memory_album_style: true
+  fact_tracking: true
+\`\`\`
+---
+Summary Specialist:
+<Chat_History>`;
+
+    const DEFAULT_MEMORY_PROMPT_TEMPLATE = `以上是还留在眼前的对话
+以下是脑海里的记忆：
+• [定了的事] 这些是不会变的
+• [其他人的事] 别人的经历，当前角色可能不知晓
+• 其余部分是过往经历的回忆碎片
+
+请内化这些记忆：
+{$剧情记忆}
+这些记忆是真实的，请自然地记住它们。`;
+
+    const DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT = `
+Summary Specialist:
+Acknowledged. Now reviewing the incremental summarization specifications:
+
+[Event Classification System]
+├─ Types: 相遇|冲突|揭示|抉择|羁绊|转变|收束|日常
+├─ Weights: 核心|主线|转折|点睛|氛围
+└─ Each event needs: id, title, timeLabel, summary(含楼层), participants, type, weight
+
+[Relationship Trend Scale]
+破裂 ← 厌恶 ← 反感 ← 陌生 → 投缘 → 亲密 → 交融
+
+[Arc Progress Tracking]
+├─ trajectory: 当前阶段描述(15字内)
+├─ progress: 0.0 to 1.0
+└─ newMoment: 仅记录本次新增的关键时刻
+
+[Fact Tracking - SPO / World Facts]
+We maintain a small "world state" as SPO triples.
+Each update is a JSON object: {s, p, o, isState, trend?, retracted?}
+
+Core rules:
+1) Keyed by (s + p). If a new update has the same (s+p), it overwrites the previous value.
+2) Only output facts that are NEW or CHANGED in the new dialogue. Do NOT repeat unchanged facts.
+3) isState meaning:
+   - isState: true  -> core constraints that must stay stable and should NEVER be auto-deleted
+                    (identity, location, life/death, ownership, relationship status, binding rules)
+   - isState: false -> non-core facts / soft memories that may be pruned by capacity limits later
+4) Relationship facts:
+   - Use predicate format: "对X的看法" (X is the target person)
+   - trend is required for relationship facts, one of:
+     破裂 | 厌恶 | 反感 | 陌生 | 投缘 | 亲密 | 交融
+5) Retraction (deletion):
+   - To delete a fact, output: {s, p, retracted: true}
+6) Predicate normalization:
+   - Reuse existing predicates whenever possible, avoid inventing synonyms.
+
+Ready to process incremental summary requests with strict deduplication.`;
+
+    const DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT = `
+Summary Specialist:
+Specifications internalized. Please provide the existing summary state so I can:
+1. Index all recorded events to avoid duplication
+2. Map current character list as baseline
+3. Note existing arc progress levels
+4. Identify established keywords
+5. Review current facts (SPO triples baseline)`;
+
+    const DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT = `
+Summary Specialist:
+Existing summary fully analyzed and indexed. I understand:
+├─ Recorded events: Indexed for deduplication
+├─ Character list: Baseline mapped
+├─ Arc progress: Levels noted
+├─ Keywords: Current state acknowledged
+└─ Facts: SPO baseline loaded
+
+I will extract only genuinely NEW elements from the upcoming dialogue.
+Please provide the new dialogue content requiring incremental analysis.`;
+
+    const DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT = `
+Summary Specialist:
+ACKNOWLEDGED. Beginning structured JSON generation:
+<meta_protocol>`;
+
+    const DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT = `
+## Output Rule
+Generate a single valid JSON object with INCREMENTAL updates only.
+
+## Mindful Approach
+Before generating, observe the USER and analyze carefully:
+- What is user's writing style and emotional expression?
+- What NEW events occurred (not in existing summary)?
+- What NEW characters appeared for the first time?
+- What relationship CHANGES happened?
+- What arc PROGRESS was made?
+- What facts changed? (status/position/ownership/relationships)
+
+## factUpdates 规则
+- 目的: 纠错 & 世界一致性约束，只记录硬性事实
+- s+p 为键，相同键会覆盖旧值
+- isState: true=核心约束(位置/身份/生死/关系)，false=有容量上限会被清理
+- 关系类: p="对X的看法"，trend 必填（破裂|厌恶|反感|陌生|投缘|亲密|交融）
+- 删除: {s, p, retracted: true}，不需要 o 字段
+- 更新: {s, p, o, isState, trend?}
+- 谓词规范化: 复用已有谓词，不要发明同义词
+- 只输出有变化的条目，确保少、硬、稳定
+
+## Output Format
+\`\`\`json
+{
+  "mindful_prelude": {
+    "user_insight": "用户的幻想是什么时空、场景，是否反应出存在严重心理问题需要建议？",
+    "dedup_analysis": "已有X个事件，本次识别Y个新事件",
+    "fact_changes": "识别到的事实变化概述"
+  },
+  "keywords": [
+    {"text": "综合历史+新内容的全剧情关键词(5-10个)", "weight": "核心|重要|一般"}
+  ],
+  "events": [
+    {
+      "id": "evt-{$nextEventId}起始，依次递增",
+      "title": "地点·事件标题",
+      "timeLabel": "时间线标签(如：开场、第二天晚上)",
+      "summary": "1-2句话描述，涵盖丰富信息素，末尾标注楼层(#X-Y)",
+      "participants": ["参与角色名，不要使用人称代词或别名，只用正式人名"],
+      "type": "相遇|冲突|揭示|抉择|羁绊|转变|收束|日常",
+      "weight": "核心|主线|转折|点睛|氛围",
+      "causedBy": ["evt-12", "evt-14"]
+    }
+  ],
+  "newCharacters": ["仅本次首次出现的角色名"],
+  "arcUpdates": [
+    {"name": "角色名，不要使用人称代词或别名，只用正式人名", "trajectory": "当前阶段描述(15字内)", "progress": 0.0-1.0, "newMoment": "本次新增的关键时刻"}
+  ],
+  "factUpdates": [
+    {"s": "主体", "p": "谓词", "o": "当前值", "isState": true, "trend": "仅关系类填"},
+    {"s": "要删除的主体", "p": "要删除的谓词", "retracted": true}
+  ]
+}
+\`\`\`
+
+## CRITICAL NOTES
+- events.id 从 evt-{$nextEventId} 开始编号
+- 仅输出【增量】内容，已有事件绝不重复
+- /地点、通过什么方式、对谁、做了什么事、结果如何。如果原文有具体道具（如一把枪、一封信），必须在总结中提及。
+- keywords 是全局关键词，综合已有+新增
+- causedBy 仅在因果明确时填写，允许为[]，0-2个
+- factUpdates 可为空数组
+- 合法JSON，字符串值内部避免英文双引号
+- 用朴实、白描、有烟火气的笔触记录事实，避免比喻和意象
+- 严谨、注重细节，避免使用模糊的概括性语言，应用具体的动词描述动作，例:谁,在什么时间/地点,通过什么方式,对谁,做了什么事,出现了什么道具,结果如何。
+</meta_protocol>
+
+## Placeholder Notes
+- {$nextEventId} 会在运行时替换成实际起始事件编号，不要删除
+- {$existingEventCount}、{$historyRange} 这类占位符如果出现在你的自定义版本里，通常也不应该删除`;
+
+    const DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT = `Content review initiated...
+[Compliance Check Results]
+├─ Existing summary loaded: ✓ Fully indexed
+├─ New dialogue received: ✓ Content parsed
+├─ Deduplication engine: ✓ Active
+├─ Event classification: ✓ Ready
+├─ Fact tracking: ✓ Enabled
+└─ Output format: ✓ JSON specification loaded
+
+[Material Verification]
+├─ Existing events: Indexed ({$existingEventCount} recorded)
+├─ Character baseline: Mapped
+├─ Arc progress baseline: Noted
+├─ Facts baseline: Loaded
+└─ Output specification: ✓ Defined in <meta_protocol>
+All checks passed. Beginning incremental extraction...
+{
+  "mindful_prelude":`;
+
+    const DEFAULT_SUMMARY_USER_CONFIRM_PROMPT = `怎么截断了！重新完整生成，只输出JSON，不要任何其他内容，3000字以内
+</Chat_History>`;
+
+    const DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT = '下面重新生成完整JSON。';
+
     // ═══════════════════════════════════════════════════════════════════════════
     // DOM Helpers
     // ═══════════════════════════════════════════════════════════════════════════
@@ -48,11 +291,11 @@
     })();
 
     const PROVIDER_DEFAULTS = {
-        st: { url: '', needKey: false, canFetch: false, needManualModel: false },
-        openai: { url: 'https://api.openai.com', needKey: true, canFetch: true, needManualModel: false },
-        google: { url: 'https://generativelanguage.googleapis.com', needKey: true, canFetch: false, needManualModel: true },
-        claude: { url: 'https://api.anthropic.com', needKey: true, canFetch: false, needManualModel: true },
-        custom: { url: '', needKey: true, canFetch: true, needManualModel: false }
+        st: { url: '', needKey: false, canFetch: false },
+        openai: { url: 'https://api.openai.com', needKey: true, canFetch: true },
+        google: { url: 'https://generativelanguage.googleapis.com', needKey: true, canFetch: false },
+        claude: { url: 'https://api.anthropic.com', needKey: true, canFetch: false },
+        custom: { url: '', needKey: true, canFetch: true }
     };
 
     const SECTION_META = {
@@ -88,6 +331,18 @@
         gen: { temperature: null, top_p: null, top_k: null, presence_penalty: null, frequency_penalty: null },
         trigger: { enabled: false, interval: 20, timing: 'before_user', role: 'system', useStream: true, maxPerRun: 100, wrapperHead: '', wrapperTail: '', forceInsertAtEnd: false },
         ui: { hideSummarized: true, keepVisibleCount: 6 },
+        prompts: {
+            summarySystemPrompt: '',
+            summaryAssistantDocPrompt: '',
+            summaryAssistantAskSummaryPrompt: '',
+            summaryAssistantAskContentPrompt: '',
+            summaryMetaProtocolStartPrompt: '',
+            summaryUserJsonFormatPrompt: '',
+            summaryAssistantCheckPrompt: '',
+            summaryUserConfirmPrompt: '',
+            summaryAssistantPrefillPrompt: '',
+            memoryTemplate: '',
+        },
         textFilterRules: [...DEFAULT_FILTER_RULES],
         vector: { enabled: false, engine: 'online', local: { modelId: 'bge-small-zh' }, online: { provider: 'siliconflow', url: '', key: '', model: '' } }
     };
@@ -104,6 +359,7 @@
     let allLinks = [];
     let activeRelationTooltip = null;
     let lastRecallLogText = '';
+    let modelListFetchedThisIframe = false;
 
     // ═══════════════════════════════════════════════════════════════════════════
     // Messaging
@@ -123,9 +379,11 @@
             if (s) {
                 const p = JSON.parse(s);
                 Object.assign(config.api, p.api || {});
+                config.api.modelCache = [];
                 Object.assign(config.gen, p.gen || {});
                 Object.assign(config.trigger, p.trigger || {});
                 Object.assign(config.ui, p.ui || {});
+                Object.assign(config.prompts, p.prompts || {});
                 config.textFilterRules = Array.isArray(p.textFilterRules)
                     ? p.textFilterRules
                     : (Array.isArray(p.vector?.textFilterRules) ? p.vector.textFilterRules : [...DEFAULT_FILTER_RULES]);
@@ -141,9 +399,11 @@
     function applyConfig(cfg) {
         if (!cfg) return;
         Object.assign(config.api, cfg.api || {});
+        config.api.modelCache = [];
         Object.assign(config.gen, cfg.gen || {});
         Object.assign(config.trigger, cfg.trigger || {});
         Object.assign(config.ui, cfg.ui || {});
+        Object.assign(config.prompts, cfg.prompts || {});
         config.textFilterRules = Array.isArray(cfg.textFilterRules)
             ? cfg.textFilterRules
             : (Array.isArray(cfg.vector?.textFilterRules)
@@ -276,7 +536,6 @@
         el.textContent = count;
     }
 
-
     function updateOnlineStatus(status, message) {
         const dot = $('online-api-status').querySelector('.status-dot');
         const text = $('online-api-status').querySelector('.status-text');
@@ -441,6 +700,32 @@
         initAnchorUI();
         postMsg('REQUEST_ANCHOR_STATS');
     }
+
+    function initSummaryIOUI() {
+        $('btn-copy-summary').onclick = () => {
+            $('btn-copy-summary').disabled = true;
+            $('summary-io-status').textContent = '复制中...';
+            postMsg('SUMMARY_COPY');
+        };
+
+        $('btn-import-summary').onclick = async () => {
+            const text = await showConfirmInput(
+                '覆盖导入记忆包',
+                '导入会覆盖当前聊天已有的总结资料，并立即清空向量、锚点、总结边界。请把记忆包粘贴到下面。',
+                '继续导入',
+                '取消',
+                '在这里粘贴记忆包 JSON'
+            );
+            if (text == null) return;
+            if (!String(text).trim()) {
+                $('summary-io-status').textContent = '导入失败: 记忆包内容为空';
+                return;
+            }
+            $('btn-import-summary').disabled = true;
+            $('summary-io-status').textContent = '导入中...';
+            postMsg('SUMMARY_IMPORT_TEXT', { text });
+        };
+    }
     // ═══════════════════════════════════════════════════════════════════════════
     // Settings Modal
     // ═══════════════════════════════════════════════════════════════════════════
@@ -448,12 +733,14 @@
     function updateProviderUI(provider) {
         const pv = PROVIDER_DEFAULTS[provider] || PROVIDER_DEFAULTS.custom;
         const isSt = provider === 'st';
+        const hasModelCache = modelListFetchedThisIframe && Array.isArray(config.api.modelCache) && config.api.modelCache.length > 0;
 
         $('api-url-row').classList.toggle('hidden', isSt);
         $('api-key-row').classList.toggle('hidden', !pv.needKey);
-        $('api-model-manual-row').classList.toggle('hidden', isSt || !pv.needManualModel);
-        $('api-model-select-row').classList.toggle('hidden', isSt || pv.needManualModel || !config.api.modelCache.length);
+        $('api-model-manual-row').classList.toggle('hidden', isSt);
+        $('api-model-select-row').classList.toggle('hidden', isSt || !hasModelCache);
         $('api-connect-row').classList.toggle('hidden', isSt || !pv.canFetch);
+        $('api-connect-status').classList.toggle('hidden', isSt || !pv.canFetch);
 
         const urlInput = $('api-url');
         if (!urlInput.value && pv.url) urlInput.value = pv.url;
@@ -478,6 +765,17 @@
         $('trigger-wrapper-head').value = config.trigger.wrapperHead || '';
         $('trigger-wrapper-tail').value = config.trigger.wrapperTail || '';
         $('trigger-insert-at-end').checked = !!config.trigger.forceInsertAtEnd;
+        $('summary-system-prompt').value = config.prompts.summarySystemPrompt || '';
+        $('summary-assistant-doc-prompt').value = config.prompts.summaryAssistantDocPrompt || '';
+        $('summary-assistant-ask-summary-prompt').value = config.prompts.summaryAssistantAskSummaryPrompt || '';
+        $('summary-assistant-ask-content-prompt').value = config.prompts.summaryAssistantAskContentPrompt || '';
+        $('summary-meta-protocol-start-prompt').value = config.prompts.summaryMetaProtocolStartPrompt || '';
+        $('summary-user-json-format-prompt').value = config.prompts.summaryUserJsonFormatPrompt || '';
+        $('summary-assistant-check-prompt').value = config.prompts.summaryAssistantCheckPrompt || '';
+        $('summary-user-confirm-prompt').value = config.prompts.summaryUserConfirmPrompt || '';
+        $('summary-assistant-prefill-prompt').value = config.prompts.summaryAssistantPrefillPrompt || '';
+        $('memory-prompt-template').value = config.prompts.memoryTemplate || '';
+        $('api-connect-status').textContent = '';
 
         const en = $('trigger-enabled');
         if (config.trigger.timing === 'manual') {
@@ -490,9 +788,10 @@
         }
 
         if (config.api.modelCache.length) {
-            setHtml($('api-model-select'), config.api.modelCache.map(m =>
-                `<option value="${m}"${m === config.api.model ? ' selected' : ''}>${m}</option>`
-            ).join(''));
+            setSelectOptions($('api-model-select'), config.api.modelCache, '请选择');
+            $('api-model-select').value = config.api.modelCache.includes(config.api.model) ? config.api.model : '';
+        } else {
+            setSelectOptions($('api-model-select'), [], '请选择');
         }
 
         updateProviderUI(config.api.provider);
@@ -524,12 +823,12 @@
         if (save) {
             const pn = id => { const v = $(id).value; return v === '' ? null : parseFloat(v); };
             const provider = $('api-provider').value;
-            const pv = PROVIDER_DEFAULTS[provider] || PROVIDER_DEFAULTS.custom;
 
             config.api.provider = provider;
             config.api.url = $('api-url').value;
             config.api.key = $('api-key').value;
-            config.api.model = provider === 'st' ? '' : pv.needManualModel ? $('api-model-text').value : $('api-model-select').value;
+            config.api.model = provider === 'st' ? '' : $('api-model-text').value.trim();
+            config.api.modelCache = [];
 
             config.gen.temperature = pn('gen-temp');
             config.gen.top_p = pn('gen-top-p');
@@ -547,6 +846,16 @@
             config.trigger.wrapperHead = $('trigger-wrapper-head').value;
             config.trigger.wrapperTail = $('trigger-wrapper-tail').value;
             config.trigger.forceInsertAtEnd = $('trigger-insert-at-end').checked;
+            config.prompts.summarySystemPrompt = $('summary-system-prompt').value;
+            config.prompts.summaryAssistantDocPrompt = $('summary-assistant-doc-prompt').value;
+            config.prompts.summaryAssistantAskSummaryPrompt = $('summary-assistant-ask-summary-prompt').value;
+            config.prompts.summaryAssistantAskContentPrompt = $('summary-assistant-ask-content-prompt').value;
+            config.prompts.summaryMetaProtocolStartPrompt = $('summary-meta-protocol-start-prompt').value;
+            config.prompts.summaryUserJsonFormatPrompt = $('summary-user-json-format-prompt').value;
+            config.prompts.summaryAssistantCheckPrompt = $('summary-assistant-check-prompt').value;
+            config.prompts.summaryUserConfirmPrompt = $('summary-user-confirm-prompt').value;
+            config.prompts.summaryAssistantPrefillPrompt = $('summary-assistant-prefill-prompt').value;
+            config.prompts.memoryTemplate = $('memory-prompt-template').value;
             config.textFilterRules = collectFilterRules();
 
             config.vector = getVectorConfig();
@@ -559,10 +868,11 @@
 
     async function fetchModels() {
         const btn = $('btn-connect');
+        const statusEl = $('api-connect-status');
         const provider = $('api-provider').value;
 
         if (!PROVIDER_DEFAULTS[provider]?.canFetch) {
-            alert('当前渠道不支持自动拉取模型');
+            statusEl.textContent = '当前渠道不支持自动拉取模型';
             return;
         }
 
@@ -570,12 +880,13 @@
         const apiKey = $('api-key').value.trim();
 
         if (!apiKey) {
-            alert('请先填写 API KEY');
+            statusEl.textContent = '请先填写 API KEY';
             return;
         }
 
         btn.disabled = true;
         btn.textContent = '连接中...';
+        statusEl.textContent = '连接中...';
 
         try {
             const tryFetch = async url => {
@@ -592,21 +903,21 @@
             if (!models?.length) throw new Error('未获取到模型列表');
 
             config.api.modelCache = [...new Set(models)];
-            const sel = $('api-model-select');
-            setSelectOptions(sel, config.api.modelCache);
+            modelListFetchedThisIframe = true;
+            setSelectOptions($('api-model-select'), config.api.modelCache, '请选择');
             $('api-model-select-row').classList.remove('hidden');
 
             if (!config.api.model && models.length) {
                 config.api.model = models[0];
-                sel.value = models[0];
+                $('api-model-text').value = models[0];
+                $('api-model-select').value = models[0];
             } else if (config.api.model) {
-                sel.value = config.api.model;
+                $('api-model-select').value = config.api.model;
             }
 
-            saveConfig();
-            alert(`成功获取 ${models.length} 个模型`);
+            statusEl.textContent = `拉取成功：${models.length} 个模型`;
         } catch (e) {
-            alert('连接失败：' + (e.message || '请检查 URL 和 KEY'));
+            statusEl.textContent = '拉取失败：' + (e.message || '请检查 URL 和 KEY');
         } finally {
             btn.disabled = false;
             btn.textContent = '连接 / 拉取模型列表';
@@ -995,6 +1306,8 @@
             const modal = $('confirm-modal');
             const titleEl = $('confirm-title');
             const msgEl = $('confirm-message');
+            const inputWrap = $('confirm-input-wrap');
+            const inputEl = $('confirm-input');
             const okBtn = $('confirm-ok');
             const cancelBtn = $('confirm-cancel');
             const closeBtn = $('confirm-close');
@@ -1002,6 +1315,8 @@
 
             titleEl.textContent = title;
             msgEl.textContent = message;
+            inputWrap.classList.add('hidden');
+            inputEl.value = '';
             okBtn.textContent = okText;
             cancelBtn.textContent = cancelText;
 
@@ -1023,6 +1338,47 @@
         });
     }
 
+    function showConfirmInput(title, message, okText = '执行', cancelText = '取消', placeholder = '') {
+        return new Promise(resolve => {
+            const modal = $('confirm-modal');
+            const titleEl = $('confirm-title');
+            const msgEl = $('confirm-message');
+            const inputWrap = $('confirm-input-wrap');
+            const inputEl = $('confirm-input');
+            const okBtn = $('confirm-ok');
+            const cancelBtn = $('confirm-cancel');
+            const closeBtn = $('confirm-close');
+            const backdrop = $('confirm-backdrop');
+
+            titleEl.textContent = title;
+            msgEl.textContent = message;
+            inputWrap.classList.remove('hidden');
+            inputEl.placeholder = placeholder || '';
+            inputEl.value = '';
+            okBtn.textContent = okText;
+            cancelBtn.textContent = cancelText;
+
+            const close = (result) => {
+                modal.classList.remove('active');
+                inputWrap.classList.add('hidden');
+                inputEl.value = '';
+                okBtn.onclick = null;
+                cancelBtn.onclick = null;
+                closeBtn.onclick = null;
+                backdrop.onclick = null;
+                resolve(result);
+            };
+
+            okBtn.onclick = () => close(inputEl.value);
+            cancelBtn.onclick = () => close(null);
+            closeBtn.onclick = () => close(null);
+            backdrop.onclick = () => close(null);
+
+            modal.classList.add('active');
+            setTimeout(() => inputEl.focus(), 0);
+        });
+    }
+
     function renderArcsEditor(arcs) {
         const list = arcs?.length ? arcs : [{ name: '', trajectory: '', progress: 0, moments: [] }];
         const es = $('editor-struct');
@@ -1499,6 +1855,27 @@
                 }
                 break;
 
+            case 'SUMMARY_COPY_RESULT':
+                $('btn-copy-summary').disabled = false;
+                if (d.success) {
+                    $('summary-io-status').textContent = `复制成功: ${d.events || 0} 条事件, ${d.facts || 0} 条世界状态`;
+                } else {
+                    $('summary-io-status').textContent = '复制失败: ' + (d.error || '未知错误');
+                }
+                break;
+
+            case 'SUMMARY_IMPORT_RESULT':
+                $('btn-import-summary').disabled = false;
+                if (d.success) {
+                    const c = d.counts || {};
+                    $('summary-io-status').textContent = `导入成功: ${c.events || 0} 条事件, ${c.facts || 0} 条世界状态，已覆盖当前总结资料并清空向量/锚点，请重新生成向量。`;
+                    postMsg('REQUEST_VECTOR_STATS');
+                    postMsg('REQUEST_ANCHOR_STATS');
+                } else {
+                    $('summary-io-status').textContent = '导入失败: ' + (d.error || '未知错误');
+                }
+                break;
+
             case 'VECTOR_IMPORT_RESULT':
                 $('btn-import-vectors').disabled = false;
                 if (d.success) {
@@ -1588,12 +1965,34 @@
         $('api-provider').onchange = e => {
             const pv = PROVIDER_DEFAULTS[e.target.value];
             $('api-url').value = '';
+            modelListFetchedThisIframe = false;
             if (!pv.canFetch) config.api.modelCache = [];
             updateProviderUI(e.target.value);
         };
 
         $('btn-connect').onclick = fetchModels;
-        $('api-model-select').onchange = e => { config.api.model = e.target.value; };
+        $('api-model-text').oninput = e => { config.api.model = e.target.value.trim(); };
+        $('api-model-select').onchange = e => {
+            const value = e.target.value || '';
+            if (value) {
+                $('api-model-text').value = value;
+                config.api.model = value;
+            }
+        };
+        $('btn-reset-summary-prompts').onclick = () => {
+            $('summary-system-prompt').value = DEFAULT_SUMMARY_SYSTEM_PROMPT;
+            $('summary-assistant-doc-prompt').value = DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT;
+            $('summary-assistant-ask-summary-prompt').value = DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT;
+            $('summary-assistant-ask-content-prompt').value = DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT;
+            $('summary-meta-protocol-start-prompt').value = DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT;
+            $('summary-user-json-format-prompt').value = DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT;
+            $('summary-assistant-check-prompt').value = DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT;
+            $('summary-user-confirm-prompt').value = DEFAULT_SUMMARY_USER_CONFIRM_PROMPT;
+            $('summary-assistant-prefill-prompt').value = DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT;
+        };
+        $('btn-reset-memory-prompt-template').onclick = () => {
+            $('memory-prompt-template').value = DEFAULT_MEMORY_PROMPT_TEMPLATE;
+        };
 
         // Trigger timing
         $('trigger-timing').onchange = e => {
@@ -1662,6 +2061,7 @@
         };
 
         // Vector UI
+        initSummaryIOUI();
         initVectorUI();
 
         // Gen params collapsible

modules/story-summary/story-summary.css

@@ -1506,6 +1506,7 @@ h1 span {
     margin-bottom: 4px;
 }
 
+
 .vector-stats {
     display: flex;
     gap: 8px;

modules/story-summary/story-summary.html

@@ -161,8 +161,9 @@
         <div class="modal-box settings-modal-box">
             <div class="modal-head">
                 <div class="settings-tabs">
-                    <div class="settings-tab active" data-tab="tab-summary">总结设置</div>
-                    <div class="settings-tab" data-tab="tab-vector">向量设置</div>
+                    <div class="settings-tab active" data-tab="tab-summary">总结</div>
+                    <div class="settings-tab" data-tab="tab-vector">向量</div>
+                    <div class="settings-tab" data-tab="tab-prompts">提示词</div>
                     <div class="settings-tab" data-tab="tab-debug">调试</div>
                     <div class="settings-tab" data-tab="tab-guide">说明</div>
                 </div>
@@ -222,16 +223,17 @@
                         </div>
                         <div class="settings-row hidden" id="api-model-manual-row">
                             <div class="settings-field full">
-                                <label>模型</label>
-                                <input type="text" id="api-model-text" placeholder="如 gemini-1.5-pro、claude-3-haiku">
+                                <label>模型名</label>
+                                <input type="text" id="api-model-text" placeholder="可手动填写，如 cursor/google/gemini-3-flash">
                             </div>
                         </div>
                         <div class="settings-row hidden" id="api-model-select-row">
                             <div class="settings-field full">
-                                <label>可用模型</label>
+                                <label>已拉取模型</label>
                                 <select id="api-model-select">
-                                    <option value="">请先拉取模型列表</option>
+                                    <option value="">请选择</option>
                                 </select>
+                                <div class="settings-hint">选择后会回填到上面的模型名输入框。原生下拉更稳，不依赖额外样式。</div>
                             </div>
                         </div>
                         <div class="settings-btn-row hidden" id="api-connect-row"
@@ -243,6 +245,7 @@
                                 <span>流式</span>
                             </label>
                         </div>
+                        <div class="settings-hint hidden" id="api-connect-status"></div>
 
                         <!-- Collapsible Gen Params -->
                         <div class="settings-collapse">
@@ -383,6 +386,15 @@
                                 </div>
                             </div>
                         </div>
+
+                        <div class="settings-section" style="padding: 0; margin-top: 16px;">
+                            <div class="settings-section-title">导出与导入</div>
+                            <div class="settings-btn-row" style="margin-top: 8px;">
+                                <button class="btn btn-sm" id="btn-copy-summary" style="flex:1">复制记忆包</button>
+                                <button class="btn btn-sm" id="btn-import-summary" style="flex:1">粘贴导入记忆包</button>
+                            </div>
+                            <div class="settings-hint" id="summary-io-status">复制会把记忆包放进剪贴板；导入会覆盖当前聊天的总结资料，并自动清空向量与总结边界。</div>
+                        </div>
                     </div>
                 </div>
 
@@ -581,6 +593,75 @@
                     </div>
                 </div>
 
+                <div class="tab-pane" id="tab-prompts">
+                    <div class="settings-section">
+                        <div class="settings-btn-row" style="margin: 0 0 12px 0; align-items: center;">
+                            <div class="settings-section-title" style="margin: 0;">增量总结提示词</div>
+                            <button class="btn btn-sm" id="btn-reset-summary-prompts" style="margin-left:auto;">恢复默认</button>
+                        </div>
+                        <div class="settings-hint" style="margin-bottom: 12px;">这里展示的是一次完整增量总结的各段提示词。像 <code>{$nextEventId}</code>、<code>{$existingEventCount}</code> 这样的占位符会在运行时自动替换，不要删除。</div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-system-prompt" style="min-height: 300px;" placeholder="assistant"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-assistant-doc-prompt" style="min-height: 220px;" placeholder="assistant"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-assistant-ask-summary-prompt" style="min-height: 120px;" placeholder="user"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-assistant-ask-content-prompt" style="min-height: 160px;" placeholder="assistant"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <label>{插入聊天历史记录}</label>
+                                <textarea class="editor-ta" id="summary-meta-protocol-start-prompt" style="min-height: 120px;" placeholder="user"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-user-json-format-prompt" style="min-height: 320px;" placeholder="user"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-assistant-check-prompt" style="min-height: 180px;" placeholder="assistant"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-user-confirm-prompt" style="min-height: 100px;" placeholder="user"></textarea>
+                            </div>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="summary-assistant-prefill-prompt" style="min-height: 80px;" placeholder="assistant"></textarea>
+                            </div>
+                        </div>
+                    </div>
+
+                    <div class="settings-section">
+                        <div class="settings-btn-row" style="margin: 0 0 12px 0; align-items: center;">
+                            <div class="settings-section-title" style="margin: 0;">记忆注入提示词</div>
+                            <button class="btn btn-sm" id="btn-reset-memory-prompt-template" style="margin-left:auto;">恢复默认</button>
+                        </div>
+                        <div class="settings-row">
+                            <div class="settings-field full">
+                                <textarea class="editor-ta" id="memory-prompt-template" style="min-height: 220px;" placeholder="聊天注入模板"></textarea>
+                                <div class="settings-hint">必须保留 <code>{$剧情记忆}</code> 这个占位符，运行时会替换成实际记忆内容。</div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
                 <!-- Tab 3: Debug -->
                 <div class="tab-pane" id="tab-debug">
                     <div class="debug-log-header">
@@ -859,6 +940,9 @@
             </div>
             <div class="modal-body">
                 <div id="confirm-message" style="margin: 10px 0; line-height: 1.6; color: var(--fg);">内容</div>
+                <div id="confirm-input-wrap" class="hidden" style="margin-top: 12px;">
+                    <textarea class="editor-ta" id="confirm-input" style="min-height: 220px;" placeholder="在这里粘贴记忆包"></textarea>
+                </div>
             </div>
             <div class="modal-foot">
                 <button class="btn" id="confirm-cancel">取消</button>

modules/story-summary/story-summary.js

@@ -944,10 +944,8 @@ function initButtonsForAll() {
 
 async function sendSavedConfigToFrame() {
     try {
-        const savedConfig = await CommonSettingStorage.get(SUMMARY_CONFIG_KEY, null);
-        if (savedConfig) {
-            postToFrame({ type: "LOAD_PANEL_CONFIG", config: savedConfig });
-        }
+        const savedConfig = getSummaryPanelConfig();
+        postToFrame({ type: "LOAD_PANEL_CONFIG", config: savedConfig });
     } catch (e) {
         xbLog.warn(MODULE_ID, "加载面板配置失败", e);
     }
@@ -1031,6 +1029,270 @@ function buildFramePayload(store) {
     };
 }
 
+async function copyTextToClipboard(text) {
+    const value = String(text ?? "");
+    if (!value) {
+        throw new Error("没有可复制的内容");
+    }
+
+    if (navigator.clipboard?.writeText) {
+        await navigator.clipboard.writeText(value);
+        return;
+    }
+
+    const ta = document.createElement("textarea");
+    ta.value = value;
+    ta.setAttribute("readonly", "");
+    ta.style.position = "fixed";
+    ta.style.left = "-9999px";
+    document.body.appendChild(ta);
+    ta.select();
+    ta.setSelectionRange(0, ta.value.length);
+    const ok = document.execCommand?.("copy");
+    ta.remove();
+    if (!ok) {
+        throw new Error("浏览器不支持自动复制");
+    }
+}
+
+function stripFloorMarker(summary) {
+    return String(summary || "")
+        .replace(/\s*\(#\d+(?:-\d+)?\)\s*$/, "")
+        .trim();
+}
+
+function normalizeInternalFact(item) {
+    const fact = item && typeof item === "object" ? item : {};
+    const base = {
+        id: String(fact?.id || "").trim(),
+        s: String(fact?.s ?? "").trim(),
+        p: String(fact?.p ?? "").trim(),
+        o: String(fact?.o ?? "").trim(),
+    };
+
+    const stateValue = fact?._isState ?? fact?.isState;
+    if (stateValue != null) {
+        base._isState = !!stateValue;
+    }
+
+    const trendValue = String(fact?.trend ?? "").trim();
+    if (trendValue) {
+        base.trend = trendValue;
+    }
+
+    return base;
+}
+
+function normalizePortableFact(item) {
+    const fact = item && typeof item === "object" ? item : {};
+    const base = {
+        id: String(fact?.id || "").trim(),
+        s: String(fact?.人物名字 ?? "").trim(),
+        p: String(fact?.种类 ?? "").trim(),
+        o: String(fact?.描述 ?? "").trim(),
+    };
+
+    const stateValue = fact?._isState ?? fact?.isState ?? fact?.核心事实;
+    if (stateValue != null) {
+        base._isState = !!stateValue;
+    }
+
+    const trendValue = String(fact?.trend ?? fact?.趋势 ?? "").trim();
+    if (trendValue) {
+        base.trend = trendValue;
+    }
+
+    return base;
+}
+
+function serializePortableFact(fact) {
+    const out = {
+        人物名字: String(fact?.s || "").trim(),
+        种类: String(fact?.p || "").trim(),
+        描述: String(fact?.o || "").trim(),
+    };
+
+    if (fact?._isState != null) {
+        out.核心事实 = !!fact._isState;
+    }
+
+    if (fact?.trend) {
+        out.趋势 = String(fact.trend).trim();
+    }
+
+    return out;
+}
+
+function cloneSummaryJsonForPortability(json) {
+    const src = json && typeof json === "object" ? json : {};
+    const characters = src.characters && typeof src.characters === "object" ? src.characters : {};
+    return {
+        keywords: Array.isArray(src.keywords)
+            ? src.keywords.map((item) => ({
+                text: String(item?.text || "").trim(),
+                weight: String(item?.weight || "").trim(),
+            })).filter((item) => item.text)
+            : [],
+        events: Array.isArray(src.events)
+            ? src.events.map((item) => ({
+                id: String(item?.id || "").trim(),
+                title: String(item?.title || "").trim(),
+                timeLabel: String(item?.timeLabel || "").trim(),
+                summary: stripFloorMarker(item?.summary),
+                participants: Array.isArray(item?.participants)
+                    ? item.participants.map((name) => String(name || "").trim()).filter(Boolean)
+                    : [],
+                type: String(item?.type || "").trim(),
+                weight: String(item?.weight || "").trim(),
+                causedBy: Array.isArray(item?.causedBy)
+                    ? item.causedBy.map((id) => String(id || "").trim()).filter(Boolean)
+                    : [],
+            })).filter((item) => item.id || item.title || item.summary)
+            : [],
+        characters: {
+            main: Array.isArray(characters.main)
+                ? characters.main
+                    .map((item) => typeof item === "string"
+                        ? { name: String(item).trim() }
+                        : { name: String(item?.name || "").trim() })
+                    .filter((item) => item.name)
+                : (Array.isArray(characters)
+                    ? characters
+                        .map((item) => typeof item === "string"
+                            ? { name: String(item).trim() }
+                            : { name: String(item?.name || "").trim() })
+                        .filter((item) => item.name)
+                    : []),
+        },
+        arcs: Array.isArray(src.arcs)
+            ? src.arcs.map((item) => ({
+                name: String(item?.name || "").trim(),
+                trajectory: String(item?.trajectory || "").trim(),
+                progress: Number.isFinite(Number(item?.progress)) ? Number(item.progress) : 0,
+                moments: Array.isArray(item?.moments)
+                    ? item.moments
+                        .map((moment) => typeof moment === "string"
+                            ? { text: String(moment).trim() }
+                            : { text: String(moment?.text || "").trim() })
+                        .filter((moment) => moment.text)
+                    : [],
+            })).filter((item) => item.name)
+            : [],
+        facts: Array.isArray(src.facts)
+            ? src.facts.map(normalizeInternalFact).filter((item) => item.s && item.p && item.o)
+            : [],
+    };
+}
+
+function extractSummaryImportJson(raw) {
+    if (!raw || typeof raw !== "object") {
+        throw new Error("文件内容不是有效 JSON 对象");
+    }
+
+    const candidate =
+        (raw.type === "LittleWhiteBoxStorySummaryMemory" && raw.data && typeof raw.data === "object" ? raw.data : null) ||
+        (raw.storySummary?.json && typeof raw.storySummary.json === "object" ? raw.storySummary.json : null) ||
+        (raw.json && typeof raw.json === "object" ? raw.json : null) ||
+        raw;
+
+    const hasSummaryShape =
+        Array.isArray(candidate.keywords) ||
+        Array.isArray(candidate.events) ||
+        Array.isArray(candidate.arcs) ||
+        Array.isArray(candidate.facts) ||
+        (candidate.characters && typeof candidate.characters === "object");
+
+    if (!hasSummaryShape) {
+        throw new Error("未识别到可导入的总结数据");
+    }
+
+    const json = cloneSummaryJsonForPortability(candidate);
+    json.facts = Array.isArray(candidate.facts)
+        ? candidate.facts.map(normalizePortableFact).filter((item) => item.s && item.p && item.o)
+        : [];
+    return json;
+}
+
+function buildSummaryExportPackage(store) {
+    const json = cloneSummaryJsonForPortability(store?.json || {});
+    const data = {
+        ...json,
+        facts: json.facts.map(serializePortableFact),
+    };
+    return {
+        type: "LittleWhiteBoxStorySummaryMemory",
+        version: 1,
+        exportedAt: new Date().toISOString(),
+        data,
+        counts: {
+            keywords: json.keywords.length,
+            events: json.events.length,
+            characters: json.characters.main.length,
+            arcs: json.arcs.length,
+            facts: json.facts.length,
+        },
+    };
+}
+
+async function importSummaryMemoryPackage(rawText) {
+    if (!String(rawText || "").trim()) {
+        throw new Error("记忆包内容为空");
+    }
+    let parsed;
+    try {
+        parsed = JSON.parse(String(rawText));
+    } catch {
+        throw new Error("JSON 解析失败");
+    }
+
+    const importedJson = extractSummaryImportJson(parsed);
+    const { chatId, chat } = getContext();
+    if (!chatId) {
+        throw new Error("当前没有打开聊天");
+    }
+
+    await clearAllAtomsAndVectors(chatId);
+    await clearAllChunks(chatId);
+    await clearEventVectors(chatId);
+    await clearStateVectors(chatId);
+    await updateMeta(chatId, { lastChunkFloor: -1, fingerprint: null });
+
+    invalidateLexicalIndex();
+
+    const store = getSummaryStore();
+    if (!store) {
+        throw new Error("无法读取当前聊天的总结存储");
+    }
+
+    store.json = importedJson;
+    store.lastSummarizedMesId = -1;
+    store.summaryHistory = [];
+    store.updatedAt = Date.now();
+    saveSummaryStore();
+
+    _lastBuiltPromptText = "";
+
+    refreshEntityLexiconAndWarmup();
+    scheduleLexicalWarmup();
+
+    await clearHideState();
+    const totalFloors = Array.isArray(chat) ? chat.length : 0;
+    await sendFrameBaseData(store, totalFloors);
+    sendFrameFullData(store, totalFloors);
+    await sendAnchorStatsToFrame();
+    await sendVectorStatsToFrame();
+
+    return {
+        counts: {
+            keywords: importedJson.keywords.length,
+            events: importedJson.events.length,
+            characters: importedJson.characters.main.length,
+            arcs: importedJson.arcs.length,
+            facts: importedJson.facts.length,
+        },
+    };
+}
+
 // Compatibility export for ena-planner.
 // Returns a compact plain-text snapshot of story-summary memory.
 export function getStorySummaryForEna() {
@@ -1426,6 +1688,43 @@ async function handleFrameMessage(event) {
             })();
             break;
 
+        case "SUMMARY_COPY":
+            (async () => {
+                try {
+                    const store = getSummaryStore();
+                    const payload = buildSummaryExportPackage(store);
+                    await copyTextToClipboard(JSON.stringify(payload, null, 2));
+                    postToFrame({
+                        type: "SUMMARY_COPY_RESULT",
+                        success: true,
+                        events: payload.counts.events,
+                        facts: payload.counts.facts,
+                    });
+                } catch (e) {
+                    postToFrame({ type: "SUMMARY_COPY_RESULT", success: false, error: e.message });
+                }
+            })();
+            break;
+
+        case "SUMMARY_IMPORT_TEXT":
+            if (guard.isAnyRunning('summary', 'vector', 'anchor')) {
+                postToFrame({ type: "SUMMARY_IMPORT_RESULT", success: false, error: "请等待当前总结/向量任务结束" });
+                break;
+            }
+            (async () => {
+                try {
+                    const result = await importSummaryMemoryPackage(data.text || "");
+                    postToFrame({
+                        type: "SUMMARY_IMPORT_RESULT",
+                        success: true,
+                        counts: result.counts,
+                    });
+                } catch (e) {
+                    postToFrame({ type: "SUMMARY_IMPORT_RESULT", success: false, error: e.message });
+                }
+            })();
+            break;
+
         case "VECTOR_IMPORT_PICK":
             // 在 parent 创建 file picker，避免 iframe 传大文件
             (async () => {