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/**
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* 生成 lab-eval-prompt.txt：便于在 Cursor 聊天中 @student-lab-ai-evaluation 后整段粘贴，
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* 将助手返回的纯 JSON 手动保存为 lab-eval-ai.json（无需 CURSOR_API_KEY）。
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*/
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import fs from 'fs';
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import path from 'path';
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import { fileURLToPath } from 'url';
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import { createRequire } from 'module';
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const __dirname = path.dirname(fileURLToPath(import.meta.url));
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const ROOT = path.join(__dirname, '..');
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const require = createRequire(import.meta.url);
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const { analyzeChatLogsFile } = require(path.join(ROOT, 'lib/analyzeChatLogs.js'));
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const { resolveChatLogPath } = require(path.join(ROOT, 'lib/resolveChatLogPath.cjs'));
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function readText(p) {
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return fs.readFileSync(p, 'utf8');
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}
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function main() {
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const chatPath = resolveChatLogPath(ROOT);
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if (!fs.existsSync(chatPath)) {
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console.error(`缺少聊天日志文件: ${chatPath}（可设置环境变量 CHAT_LOG_PATH）`);
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process.exit(1);
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}
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const taskMd = readText(path.join(ROOT, 'config/lab-task-description.md'));
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const dims = readText(path.join(ROOT, 'config/evaluation-dimensions.yaml'));
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const skillPath = path.join(ROOT, '.cursor/skills/student-lab-ai-evaluation/SKILL.md');
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const skill = fs.existsSync(skillPath) ? readText(skillPath) : '';
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const heuristic = analyzeChatLogsFile(chatPath);
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const heuristicStr = JSON.stringify(heuristic, null, 2);
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const body = `【说明】在 Cursor 中引用技能 @student-lab-ai-evaluation，将下文从「--- 开始」到结尾整段复制给助手；要求助手只输出一个 JSON 对象（无 Markdown 围栏），保存为项目根目录 lab-eval-ai.json。
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--- 开始 ---
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你是课程助教。请严格遵守下列 SKILL 中的输出约束。
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--- SKILL 开始 ---
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${skill}
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--- SKILL 结束 ---
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--- 实训任务描述 ---
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${taskMd}
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--- 评价维度配置（YAML）---
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${dims}
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--- 启发式解析 JSON（来自 chat_logs）---
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${heuristicStr}
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--- 结束 ---
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`;
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const outPath = path.join(ROOT, 'lab-eval-prompt.txt');
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fs.writeFileSync(outPath, body, 'utf8');
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console.log('已生成', outPath);
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console.log('下一步：在 Cursor 中 @student-lab-ai-evaluation，粘贴该文件全文；将模型返回的 JSON 存为 lab-eval-ai.json 后执行 npm start 刷新报告。');
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}
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main();
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