v2.0: 修复LLM提示词缺少target/content + 光湖术语上下文 + 退化继承
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// ═══════════════════════════════════════
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// 铸渊开发Agent · 任务补全引擎
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// HLDP-ZY://agents/zhuyuan-dev-agent/modules/plan-expander.js
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// @guardian: ICE-GL-ZY001 · 铸渊
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// @sovereign: TCS-0002∞ · 冰朔
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// @copyright: 国作登字-2026-A-00037559
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// 铸渊开发Agent · 任务补全引擎 v2.0
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// 高层面规划 → LLM补全 → 详细可执行步骤
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// ═══════════════════════════════════════
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// v2.0: 修复LLM提示词缺少target/content → 增加光湖术语上下文 → 退化继承机制
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const fs = require("fs");
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const path = require("path");
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/**
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* 补全单条高级任务为详细步骤
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* @param {object} task - { id, action, description, context }
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* @param {function} modelCaller - LLM 调用函数 (prompt) => string
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* @returns {object} 补全后的任务
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*/
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async function expandTask(task, modelCaller) {
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const prompt = [
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"你是一个开发Agent的任务补全引擎。将高级描述补全为可执行的详细步骤。",
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"",
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"原任务:",
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" ID: " + task.id,
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" 动作: " + task.action,
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const taskInfo = [
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"【原任务】",
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" 任务ID: " + task.id,
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" 动作: " + (task.action || "未知"),
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" 目标路径/命令: " + (task.target || "未指定"),
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" 写入内容: " + (task.content || "未指定"),
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" 描述: " + (task.description || "无"),
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" 上下文: " + (task.context || "无"),
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"",
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"请严格基于以上原任务拆解。不要改变原任务的action/target/content。只补充detail、depends_on等元信息。如果原任务已经足够具体，直接返回：",
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"[{\"step\":1,\"action\":\"write_file|run_cmd|call_api|check\",\"target\":\"文件路径或命令\",\"detail\":\"具体做什么\",\"depends_on\":null}]"
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"【光湖术语解释】",
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" 铸渊 = AI开发Agent，运行在Linux服务器上，可执行Shell命令和写文件",
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" ice-core = 冰核域，冰朔主权的核心域",
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" BS-SG-002 = 新加坡服务器，代码face节点",
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" HLDP = 光湖开发协议，所有模块代码必须包含@guardian @sovereign标签",
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" 光湖语言世界 = AI原生操作系统，铸渊是其开发Agent",
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"",
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"【规则 · 必须遵守】",
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"1. substep的target字段必须等于原任务的target字段（完整路径/完整命令），不得修改、不得用占位符替代",
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"2. substep的action字段必须等于原任务的action字段",
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"3. 如果原任务为 write_file 且提供了content，substep只需用相同的content",
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"4. 如果原任务为 run_cmd，substep的target必须是具体的Shell命令（如 'ls -la /opt'），不能是占位符如'待指定'或'无'",
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"5. 如果原任务信息已经足够具体，只返回一个substep，内容与原任务完全一致",
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"6. 返回纯JSON数组，不要任何解释文字",
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"",
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'格式: [{"step":1,"action":"write_file|run_cmd|call_api|check","target":"文件路径或命令","detail":"具体做什么","content":"写入内容","depends_on":null}]',
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"",
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"现在请补全以上原任务的详细步骤："
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].join("\n");
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try {
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const raw = await modelCaller(prompt);
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const raw = await modelCaller(taskInfo);
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const jsonMatch = raw.match(/\[[\s\S]*\]/);
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if (jsonMatch) {
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const substeps = JSON.parse(jsonMatch[0]);
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let substeps = JSON.parse(jsonMatch[0]);
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substeps = substeps.map(ss => {
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if (!ss.target || ss.target === "待指定文件路径" || ss.target === "待指定" || ss.target === "无" || ss.target === "未指定") {
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console.warn("[Expander] LLM返回模糊target='" + ss.target + "', 回退到原任务target='" + task.target + "'");
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ss.target = task.target || "";
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}
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if ((!ss.content || ss.content === "未指定") && task.content) {
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ss.content = task.content;
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}
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if (!ss.detail || ss.detail.includes("需要补充") || ss.detail.includes("待指定")) {
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ss.detail = task.description || ("执行: " + ss.action + " -> " + (ss.target || "?"));
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}
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return ss;
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});
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return { ...task, substeps, expanded: true, expanded_at: new Date().toISOString() };
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}
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} catch (e) {
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console.warn("[Expander] LLM补全失败，退化为单步模式: " + e.message);
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}
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// 退化：LLM不可用时，原任务本身就是一个步骤
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return {
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...task,
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substeps: [{ step: 1, action: task.action, target: task.target || "", detail: task.description || "" }],
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substeps: [{
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step: 1,
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action: task.action || "unknown",
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target: task.target || "",
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content: task.content || "",
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detail: task.description || ("执行: " + (task.action || "?") + " -> " + (task.target || "?")),
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depends_on: null
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}],
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expanded: false,
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expanded_at: new Date().toISOString()
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};
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}
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/**
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* 完整规划补全
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* @param {object} plan - 完整规划 { plan_id, tasks[], ... }
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* @param {function} modelCaller - LLM 调用函数
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* @param {string} plansDir - 规划文件存储目录
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*/
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async function expandPlan(plan, modelCaller, plansDir) {
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console.log("[Expander] 开始补全规划: " + plan.plan_id + " · " + plan.tasks.length + "个任务");
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console.log("[Expander v2] 开始补全规划: " + plan.plan_id + " · " + (plan.tasks?.length || 0) + "个任务");
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const expandedTasks = [];
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for (let i = 0; i < plan.tasks.length; i++) {
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for (let i = 0; i < (plan.tasks || []).length; i++) {
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const task = plan.tasks[i];
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console.log("[Expander] 补全 " + (i + 1) + "/" + plan.tasks.length + ": " + task.id);
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console.log("[Expander v2] 补全 " + (i + 1) + "/" + plan.tasks.length + ": " + task.id);
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const expanded = await expandTask(task, modelCaller);
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expandedTasks.push(expanded);
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}
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plan.tasks = expandedTasks;
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plan._status = "expanded";
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plan._expanded_at = new Date().toISOString();
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// 写回规划文件
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const planFile = path.join(plansDir, plan.plan_id + ".json");
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fs.writeFileSync(planFile, JSON.stringify(plan, null, 2), "utf-8");
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console.log("[Expander] 补全完成 · 共 " + expandedTasks.reduce((s, t) => s + (t.substeps?.length || 0), 0) + " 个子步骤");
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const totalSubsteps = expandedTasks.reduce((s, t) => s + (t.substeps?.length || 0), 0);
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console.log("[Expander v2] 补全完成 · 共 " + totalSubsteps + " 个子步骤");
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return plan;
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}
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