openclaw-voc-skill/claude-code/claude-code-voc-intelligence/mcp/src/features/voc-business-workflow/business-workflow.js

const path = require('path');
const { runXiaohongshuTrend } = require('../../tools/xiaohongshu-trend-run');
const { runDouyinTrend } = require('../../tools/douyin-trend-run');
const { runVocIssuePool } = require('../../tools/voc-issue-pool-run');
const { runVocProblemDeepDive } = require('../../tools/voc-problem-deep-dive-run');
const { runVocContentPlan } = require('../../tools/voc-content-plan-run');
const { runVocSpeakingScript } = require('../../tools/voc-speaking-script-run');

function asList(value) {
  if (!value) return [];
  if (Array.isArray(value)) return value.map(String).map(item => item.trim()).filter(Boolean);
  return String(value).split(/[,，、\n\r]+/).map(item => item.trim()).filter(Boolean);
}

function firstText(...values) {
  for (const value of values) {
    if (value && String(value).trim()) return String(value).trim();
  }
  return '';
}

function collectEvidence(trendResult) {
  const comments = trendResult.data && Array.isArray(trendResult.data.comments)
    ? trendResult.data.comments
    : [];
  const notes = trendResult.data && Array.isArray(trendResult.data.notes)
    ? trendResult.data.notes
    : [];
  const videos = trendResult.data && Array.isArray(trendResult.data.videos)
    ? trendResult.data.videos
    : [];
  const commentEvidence = comments
    .slice(0, 12)
    .map(comment => comment.text || comment.content)
    .filter(Boolean);
  const itemEvidence = [...notes, ...videos]
    .slice(0, 5)
    .map(item => firstText(item.title, item.desc, item.content, item.contentExcerpt))
    .filter(Boolean);
  return [...commentEvidence, ...itemEvidence].slice(0, 16);
}

function extractIssues(issuePoolResult, fallbackEvidence) {
  const issues = issuePoolResult.data && Array.isArray(issuePoolResult.data.issues)
    ? issuePoolResult.data.issues
    : [];
  const titles = issues.map(issue => issue.title || issue.issue || issue.name).filter(Boolean);
  return titles.length ? titles : fallbackEvidence.slice(0, 3);
}

function compactSection(title, body, maxLines = 18) {
  const lines = String(body || '').split('\n').filter(Boolean);
  return [`## ${title}`, '', ...lines.slice(0, maxLines)].join('\n');
}

async function runBusinessWorkflow(input = {}) {
  const platform = String(input.platform || input.sourcePlatform || 'douyin').toLowerCase().includes('xiao')
    || String(input.platform || '').includes('小红书')
    ? 'xiaohongshu'
    : 'douyin';
  const collectionMode = input.collectionMode || input['collection-mode'] || 'sample';
  const project = firstText(input.project, input.brand, input.store, 'VOC经营闭环');
  const industry = firstText(input.industry, input.category, '本地生活餐饮');
  const brand = firstText(input.brand, input.store, project);
  const scenario = firstText(input.scenario, input.scene, '经营诊断');
  const audience = firstText(input.audience, input.targetAudience, '目标用户');
  const keywords = asList(input.keywords).length ? asList(input.keywords) : asList(input.keyword);
  const outputRoot = path.resolve(input.output || path.join('outputs', 'voc-business-workflow', new Date().toISOString().slice(0, 10)));

  const trendInput = {
    ...input,
    project,
    industry,
    targetAudience: Array.isArray(input.targetAudience) ? input.targetAudience : [audience],
    keywords: keywords.length ? keywords : undefined,
    collectionMode,
    output: path.join(outputRoot, platform === 'xiaohongshu' ? 'xiaohongshu-trend' : 'douyin-trend'),
    keywordLimit: input.keywordLimit || input['keyword-limit'] || 2,
    notesPerKeyword: input.notesPerKeyword || input['notes-per-keyword'] || 2,
    videosPerKeyword: input.videosPerKeyword || input['videos-per-keyword'] || 2,
    maxCommentPages: input.maxCommentPages || input['max-comment-pages'] || 1
  };
  const trendResult = platform === 'xiaohongshu'
    ? await runXiaohongshuTrend(trendInput)
    : await runDouyinTrend(trendInput);

  if (trendResult.status !== 'ok') {
    return {
      ...trendResult,
      assistantMessage: [
        '# VOC经营闭环未完成',
        '',
        '第一步真实声音采集没有拿到可继续分析的样本，先按下面提示处理：',
        '',
        trendResult.assistantMessage || '',
        '',
        '处理完成后再说：继续做经营闭环。'
      ].filter(Boolean).join('\n')
    };
  }

  const evidence = collectEvidence(trendResult);
  const issuePool = await runVocIssuePool({
    project,
    brand,
    industry,
    scenario,
    audience,
    evidence,
    reportText: trendResult.assistantMessage
  });
  const issues = extractIssues(issuePool, evidence);
  const topIssue = firstText(input.issue, input.problem, issues[0], '用户顾虑不清晰');
  const deepDive = await runVocProblemDeepDive({
    project,
    brand,
    industry,
    scenario,
    audience,
    issue: topIssue,
    evidence
  });
  const contentPlan = await runVocContentPlan({
    project,
    brand,
    industry,
    platform: platform === 'xiaohongshu' ? '小红书' : '抖音',
    issues: issues.slice(0, 5),
    reportText: deepDive.assistantMessage
  });
  const firstTopic = contentPlan.data && Array.isArray(contentPlan.data.plan)
    ? contentPlan.data.plan[0]
    : null;
  const speakingScript = await runVocSpeakingScript({
    project,
    brand,
    industry,
    topic: firstText(input.topic, firstTopic && firstTopic.title, topIssue),
    userIssue: firstText(firstTopic && firstTopic.userIssue, topIssue),
    evidence: evidence.slice(0, 5),
    feedback: input.feedback,
    finalize: Boolean(input.finalize)
  });

  const files = [
    ...(trendResult.files || []),
    ...(issuePool.files || []),
    ...(deepDive.files || []),
    ...(contentPlan.files || []),
    ...(speakingScript.files || [])
  ];
  const assistantMessage = [
    '# VOC经营闭环（第一轮）',
    '',
    '你这轮能拿到：市场真实声音、问题优先级、一个单点解决动作、7天内容方向，以及一条可继续改稿的口播稿。',
    '',
    `- 平台：${platform === 'xiaohongshu' ? '小红书' : '抖音'}`,
    `- 模式：${collectionMode}`,
    `- 行业/品类：${industry}`,
    `- 品牌/门店：${brand}`,
    '',
    compactSection('1. 市场和用户声音', trendResult.assistantMessage, 16),
    '',
    compactSection('2. 先改哪个问题', issuePool.assistantMessage, 18),
    '',
    compactSection('3. Top问题怎么解决', deepDive.assistantMessage, 18),
    '',
    compactSection('4. 下周账号发什么', contentPlan.assistantMessage, 18),
    '',
    compactSection('5. 第一条口播稿', speakingScript.assistantMessage, 18),
    '',
    '## 下一步建议',
    '',
    `1. 先确认 Top 问题“${topIssue}”是否真的最影响生意。`,
    '2. 从 7 天内容计划里选 1 条进入改稿。',
    '3. 7 天后反馈哪些动作有效/无效，让记忆继续优化。'
  ].join('\n');

  return {
    status: 'ok',
    assistantMessage,
    summary: {
      workflowStage: 'business_workflow_first_round',
      platform,
      collectionMode,
      trendStatus: trendResult.status,
      issueCount: issues.length,
      topIssue,
      contentTopicCount: contentPlan.summary && contentPlan.summary.topicCount,
      speakingScriptStatus: speakingScript.status
    },
    data: {
      trend: trendResult,
      issuePool,
      deepDive,
      contentPlan,
      speakingScript
    },
    files,
    nextActions: [
      `确认 Top 问题“${topIssue}”是否优先`,
      '选择 7 天内容计划中的一条继续改成最终口播稿',
      '执行 7 天后反馈有效/无效动作，让记忆继续收敛'
    ],
    warnings: [
      collectionMode === 'sample' ? '本轮使用 sample 演示数据，不是真实采集。' : ''
    ].filter(Boolean),
    errors: []
  };
}

module.exports = {
  runBusinessWorkflow
};