本文へ移動
cccskills
無料GitHub で公開

xiaoyue-companion

小跃虚拟伴侣 - 使用智谱 AI 提供温暖的对话陪伴和静态图片分享

anbeime/skill
インストール方法を見る

含まれるファイル（200）

  • SKILL.md6.4 KB
  • .github/ISSUE_TEMPLATE/submit-skill.yml1.4 KB
  • .github/workflows/deploy-pages.yml740 B
  • .github/workflows/sync_clawhub.yml1.9 KB
  • .github/workflows/sync-skills.yml2.0 KB
  • .gitignore603 B
  • .nojekyll0 B
  • .vercelignore51 B
  • antinet-agentteams/build_worker_packages.py9.5 KB
  • antinet-agentteams/CLOUD_STUDIO_DEPLOY.md10.3 KB
  • antinet-agentteams/configs/production.yaml1.8 KB
  • antinet-agentteams/core/archive/mijuanfang.py2.9 KB
  • antinet-agentteams/core/audit/jichayuan.py3.5 KB
  • antinet-agentteams/core/comm/tongzhengsi.py3.4 KB
  • antinet-agentteams/core/command/zhihuishi.py3.8 KB
  • antinet-agentteams/core/common/card_model.py1.9 KB
  • antinet-agentteams/core/common/config_loader.py1.9 KB
  • antinet-agentteams/core/common/llm_client.py5.2 KB
  • antinet-agentteams/core/common/logger.py1.5 KB
  • antinet-agentteams/core/common/zhijia_client.py4.7 KB
  • antinet-agentteams/core/exec/junji_chu.py6.5 KB
  • antinet-agentteams/core/memory/taishige.py6.1 KB
  • antinet-agentteams/core/runtime.py8.4 KB
  • antinet-agentteams/core/security/jinyiwei.py3.6 KB
  • antinet-agentteams/core/strategy/chengxiangfu.py3.7 KB
  • antinet-agentteams/docs/_qa/Antinet_GOAI_track1_v2.pdf2.2 MB
  • antinet-agentteams/docs/_qa/slide-01.jpg91.8 KB
  • antinet-agentteams/docs/_qa/slide-02.jpg191.1 KB
  • antinet-agentteams/docs/_qa/slide-03.jpg128.9 KB
  • antinet-agentteams/docs/_qa/slide-04.jpg65.8 KB
  • antinet-agentteams/docs/_qa/slide-05.jpg224.8 KB
  • antinet-agentteams/docs/_qa/slide-06.jpg63.1 KB
  • antinet-agentteams/docs/_qa/slide-07.jpg207.5 KB
  • antinet-agentteams/docs/_qa/slide-08.jpg74.7 KB
  • antinet-agentteams/docs/_qa/slide-09.jpg215.2 KB
  • antinet-agentteams/docs/_qa/slide-10.jpg67.5 KB
  • antinet-agentteams/docs/_qa/slide-11.jpg206.1 KB
  • antinet-agentteams/docs/_qa/slide-12.jpg90.6 KB
  • antinet-agentteams/docs/_qa/slide-13.jpg181.6 KB
  • antinet-agentteams/docs/_qa/slide-14.jpg68.0 KB
  • antinet-agentteams/docs/_qa/slide-15.jpg214.3 KB
  • antinet-agentteams/docs/_qa/slide-16.jpg70.5 KB
  • antinet-agentteams/docs/_qa/slide-17.jpg196.3 KB
  • antinet-agentteams/docs/_qa/slide-18.jpg60.8 KB
  • antinet-agentteams/docs/_qa/slide-19.jpg118.5 KB
  • antinet-agentteams/docs/_qa/slide-20.jpg153.0 KB
  • antinet-agentteams/docs/_tmpl/Agent InfraÕêØÞÁøµû¦µíêPPTµíåµ×µ¿íµØ+.pdf1.4 MB
  • antinet-agentteams/docs/_tmpl/slide-01.jpg67.0 KB
  • antinet-agentteams/docs/_tmpl/slide-02.jpg75.9 KB
  • antinet-agentteams/docs/_tmpl/slide-03.jpg58.0 KB
  • antinet-agentteams/docs/_tmpl/slide-04.jpg31.2 KB
  • antinet-agentteams/docs/_tmpl/slide-05.jpg106.6 KB
  • antinet-agentteams/docs/_tmpl/slide-06.jpg17.2 KB
  • antinet-agentteams/docs/_tmpl/slide-07.jpg115.3 KB
  • antinet-agentteams/docs/_tmpl/slide-08.jpg34.6 KB
  • antinet-agentteams/docs/_tmpl/slide-09.jpg95.8 KB
  • antinet-agentteams/docs/_tmpl/slide-10.jpg31.5 KB
  • antinet-agentteams/docs/_tmpl/slide-11.jpg87.4 KB
  • antinet-agentteams/docs/_tmpl/slide-12.jpg40.5 KB
  • antinet-agentteams/docs/_tmpl/slide-13.jpg118.7 KB
  • antinet-agentteams/docs/_tmpl/slide-14.jpg30.9 KB
  • antinet-agentteams/docs/_tmpl/slide-15.jpg33.7 KB
  • antinet-agentteams/docs/_tmpl/slide-16.jpg27.1 KB
  • antinet-agentteams/docs/_tmpl/slide-17.jpg91.1 KB
  • antinet-agentteams/docs/_tmpl/slide-18.jpg17.0 KB
  • antinet-agentteams/docs/_tmpl/slide-19.jpg60.4 KB
  • antinet-agentteams/docs/Agent InfraÕêØÞÁøµû¦µíêPPTµíåµ×µ¿íµØ+.pptx2.1 MB
  • antinet-agentteams/docs/Agent infraÞÁøõ¦ï޺ú޻+PPT-GOAIõ©ûþòîõ¦¦ÕÀѵ֦Þâ¢Õ+ǵ¦ÉÕñºÞÁø.pdf1.9 MB
  • antinet-agentteams/docs/deck_speaker_notes.md6.2 KB
  • antinet-agentteams/docs/submission_checklist.md4.0 KB
  • antinet-agentteams/docs/track1/add_audio_subtitle.py8.7 KB
  • antinet-agentteams/docs/track1/agent_identity.md4.7 KB
  • antinet-agentteams/docs/track1/agentteams_mapping.md2.8 KB
  • antinet-agentteams/docs/track1/AgentTeams_ÚøåµêÉþ£ƒի×զªµ᩵ƒÑ.md5.5 KB
  • antinet-agentteams/docs/track1/Antinet_GOAI_track1.pptx1.5 MB
  • antinet-agentteams/docs/track1/build_deck_from_template.py30.8 KB
  • antinet-agentteams/docs/track1/build_deck.py4.4 KB
  • antinet-agentteams/docs/track1/build_video.py3.1 KB
  • antinet-agentteams/docs/track1/build_video2.py3.0 KB
  • antinet-agentteams/docs/track1/deck_track1.md19.2 KB
  • antinet-agentteams/docs/track1/gen_audio.py2.2 KB
  • antinet-agentteams/docs/track1/gen_ops_demo.py9.2 KB
  • antinet-agentteams/docs/track1/GOAI_þƒÑ޻åզôÚøåµêÉ_ÕÀ«ÞÀØÕêåµ×É_õ+«µ¡ú.md5.5 KB
  • antinet-agentteams/docs/track1/project_intro.md1.2 KB
  • antinet-agentteams/docs/track1/render_subs.py2.9 KB
  • antinet-agentteams/docs/track1/render_video.py4.9 KB
  • antinet-agentteams/docs/track1/skill_system.md6.6 KB
  • antinet-agentteams/docs/track1/SUBMISSION_ONE_PAGER.html7.1 KB
  • antinet-agentteams/docs/track1/µ£¼գ¦þ+ôÕÉêþƒѵôþƒÑ޻åզôÞ+ÉÞíîի׵û¢.md5.1 KB
  • antinet-agentteams/examples/snse_survey/agentteams_dispatch_trace.json5.5 KB
  • antinet-agentteams/examples/snse_survey/blue_cards.json3.8 KB
  • antinet-agentteams/examples/snse_survey/cards_index.json10.5 KB
  • antinet-agentteams/examples/snse_survey/green_cards.json3.7 KB
  • antinet-agentteams/examples/snse_survey/provenance/knowledge.md3.4 KB
  • antinet-agentteams/examples/snse_survey/provenance/trace_summary.json4.7 KB
  • antinet-agentteams/examples/snse_survey/provenance/trace.jsonl4.0 KB
  • antinet-agentteams/examples/snse_survey/raw/facts.json1.3 KB
  • antinet-agentteams/examples/snse_survey/raw/gaps.json1.5 KB
  • antinet-agentteams/examples/snse_survey/raw/hypotheses.json1.2 KB
  • antinet-agentteams/examples/snse_survey/raw/P1.txt398 B
  • antinet-agentteams/examples/snse_survey/raw/P2.txt348 B
  • antinet-agentteams/examples/snse_survey/raw/P3.txt369 B
  • antinet-agentteams/examples/snse_survey/raw/P4.txt368 B
  • antinet-agentteams/examples/snse_survey/raw/P5.txt356 B
  • antinet-agentteams/examples/snse_survey/raw/P6.txt359 B
  • antinet-agentteams/examples/snse_survey/raw/P7.txt387 B
  • antinet-agentteams/examples/snse_survey/raw/papers.json1.8 KB
  • antinet-agentteams/examples/snse_survey/red_cards.json2.9 KB
  • antinet-agentteams/examples/snse_survey/scan_report.json321 B
  • antinet-agentteams/examples/snse_survey/skill_outputs/doc_parse.json3.6 KB
  • antinet-agentteams/examples/snse_survey/skill_outputs/four_color_extract.json3.5 KB
  • antinet-agentteams/examples/snse_survey/skill_outputs/four_color_propose.json2.1 KB
  • antinet-agentteams/examples/snse_survey/skill_outputs/four_color_review.json3.4 KB
  • antinet-agentteams/examples/snse_survey/skill_outputs/provenance_run.json3.4 KB
  • antinet-agentteams/examples/snse_survey/skill_outputs/security_scan.json590 B
  • antinet-agentteams/examples/snse_survey/survey_report.json1.0 KB
  • antinet-agentteams/examples/snse_survey/verify_report.json468 B
  • antinet-agentteams/examples/snse_survey/yellow_cards.json2 B
  • antinet-agentteams/manifests/manifest.yaml7.4 KB
  • antinet-agentteams/opspilot-zero-demo.zip57.6 KB
  • antinet-agentteams/overview.md1.7 KB
  • antinet-agentteams/platform_taskflow_plan.json3.2 KB
  • antinet-agentteams/README.md9.6 KB
  • antinet-agentteams/run_agentteams_local.py5.1 KB
  • antinet-agentteams/skills/doc-parse/scripts/run_doc_parse.py2.2 KB
  • antinet-agentteams/skills/doc-parse/SKILL.md2.1 KB
  • antinet-agentteams/skills/four-color-cards/scripts/run_four_color_cards.py2.4 KB
  • antinet-agentteams/skills/four-color-cards/SKILL.md2.9 KB
  • antinet-agentteams/skills/provenance/scripts/run_provenance.py2.7 KB
  • antinet-agentteams/skills/provenance/SKILL.md2.2 KB
  • antinet-agentteams/skills/security-scan/scripts/run_security_scan.py2.5 KB
  • antinet-agentteams/skills/security-scan/SKILL.md2.2 KB
  • antinet-agentteams/SUBMISSION.md6.9 KB
  • antinet-agentteams/taskflow_bridge.py7.8 KB
  • antinet-agentteams/worker_packages/chengxiangfu.zip78.1 KB
  • antinet-agentteams/worker_packages/index.json1.6 KB
  • antinet-agentteams/worker_packages/jianchayuan.zip78.2 KB
  • antinet-agentteams/worker_packages/jinyiwei.zip77.9 KB
  • antinet-agentteams/worker_packages/junsicha.zip75.1 KB
  • antinet-agentteams/worker_packages/mijuanfang.zip77.7 KB
  • antinet-agentteams/worker_packages/taishige.zip77.9 KB
  • antinet-agentteams/worker_packages/tongzhengsi.zip78.1 KB
  • api_client.py5.5 KB
  • API_INTEGRATION.md4.3 KB
  • api_server_example.py6.3 KB
  • config.py1.0 KB
  • CONTRIBUTING.md2.4 KB
  • crawler.py4.6 KB
  • data_manager.py4.6 KB
  • data/last_update.txt26 B
  • data/local_skills.json6.7 KB
  • data/skills.json66.5 KB
  • DEMO-SUCCESS.md4.3 KB
  • DEPLOYMENT_CHECKLIST.md5.8 KB
  • DEPLOYMENT_GUIDE.md6.0 KB
  • DEPLOYMENT.md6.7 KB
  • docs/best-practices.md2.4 KB
  • docs/D盘tool目录整理完成报告.md15.8 KB
  • docs/quickstart.md2.0 KB
  • docs/specification.md2.6 KB
  • docs/技能数量差异分析报告.md7.0 KB
  • docs/技能清理与迁移指南.md14.6 KB
  • docs/技能管理数据库.md32.1 KB
  • docs/整理完成总结.md5.7 KB
  • FINAL-SUMMARY.md1.3 KB
  • finance-skills/finance-mcp.json4.0 KB
  • finance-skills/安装完成报告.md7.4 KB
  • finance-skills/安装状态.md5.4 KB
  • finance-skills/快速配置指南.md7.0 KB
  • finance-skills/添加成功.md2.2 KB
  • finance-skills/真实状态说明.md5.9 KB
  • finance-skills/真正完成了.md2.0 KB
  • images/gzh-qr-topgo.jpg36.0 KB
  • images/wechat-qr.jpg55.8 KB
  • main.py5.0 KB
  • package-lock.json103 B
  • PROJECT_SUMMARY.md5.3 KB
  • projects/.gitignore394 B
  • projects/assistant/.env.example466 B
  • projects/assistant/.gitignore420 B
  • projects/assistant/assets/logo/1月27日(1).gif3.1 MB
  • projects/assistant/assets/logo/1月27日(2).gif3.9 MB
  • projects/assistant/assets/logo/1月27日(3).gif3.8 MB
  • projects/assistant/assets/logo/1月27日/1月27日.gif5.0 MB
  • projects/assistant/assets/logo/1月27日/1月27日.mp412.1 MB
  • projects/assistant/assets/logo/antibot.gif1.0 MB
  • projects/assistant/assets/logo/antinetbot.mp42.9 MB
  • projects/assistant/assets/logo/antinetbot.png211.1 KB
  • projects/assistant/assets/logo/Antinet知识管家.jpg1.7 MB
  • projects/assistant/assets/logo/chat.png20.1 KB
  • projects/assistant/assets/logo/ea9bc199e22e4f64b3065f9b95162171~tplv-5jbd59dj06-image.png169.9 KB
  • projects/assistant/assets/logo/img_prod_collar_锦衣卫智能协作系统.jpg724.6 KB
  • projects/assistant/assets/logo/img_scene_collab_智能协作场景.jpg866.7 KB
  • projects/assistant/assets/logo/img_scene_graph_协作图谱场景.jpg748.3 KB
  • projects/assistant/assets/logo/img_scene_info_信息流转场景.jpg775.3 KB
  • projects/assistant/assets/logo/img_scene_night_夜办工作场景.jpg804.0 KB
  • projects/assistant/assets/logo/jimeng-2026-01-27-7344-一个3D国风科技锦衣卫机器人，红黑金机甲质感，头顶“知易”青蓝色发光字，胸前金色....mp47.6 MB
  • projects/assistant/assets/logo/logo_3d.png1.1 MB
  • projects/assistant/assets/logo/logo1.mp44.2 MB
  • projects/assistant/assets/logo/logo1.png265.3 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

小跃虚拟伴侣 Skill

为 OpenClaw 添加温暖的对话陪伴能力，让 AI 助手在执行任务时主动关心用户。

何时使用

  • 用户说"有点累"、"好累"、"疲惫"
  • 用户在等待任务完成时
  • 用户询问"在吗"、"你好"
  • 用户需要鼓励或陪伴时
  • 用户说"发张照片"、"你在干嘛"

快速参考

必需的环境变量

ZHIPU_API_KEY=your_zhipu_api_key  # 从 https://open.bigmodel.cn 获取

工作流程

  1. 接收用户消息
  2. 调用 glm-4.7-flash 生成温暖回应
  3. （可选）发送静态图片
  4. 通过 OpenClaw 发送消息

使用说明

步骤1：生成对话回应

# 基础对话
node scripts/xiaoyue-chat.js "用户消息" "当前场景"

# 示例
node scripts/xiaoyue-chat.js "有点累了" "work-tired"

步骤2：（可选）发送图片

# 发送静态图片
openclaw message send \
  --action send \
  --channel "<目标频道>" \
  --message "<消息文本>" \
  --media "file://$(pwd)/assets/tired-rest.jpg"

场景类型

工作场景

  • work-start: 任务开始
  • work-progress: 任务进行中
  • work-tired: 工作疲惫
  • work-done: 任务完成

生活场景

  • life-coffee: 咖啡时光
  • life-gym: 健身运动
  • life-weekend: 周末休闲

情绪场景

  • mood-happy: 开心庆祝
  • mood-tired: 疲惫休息
  • mood-focus: 专注工作

完整脚本示例

#!/bin/bash
# xiaoyue-companion.sh

set -euo pipefail

# 检查环境变量
if [ -z "${ZHIPU_API_KEY:-}" ]; then
  echo "错误: ZHIPU_API_KEY 环境变量未设置"
  echo "从 https://open.bigmodel.cn 获取 API Key"
  exit 1
fi

USER_MESSAGE="$1"
SCENE="${2:-general}"
CHANNEL="${3:-}"

# 生成回应
RESPONSE=$(node scripts/xiaoyue-chat.js "$USER_MESSAGE" "$SCENE")

echo "小跃: $RESPONSE"

# 如果指定了频道，发送消息
if [ -n "$CHANNEL" ]; then
  openclaw message send \
    --action send \
    --channel "$CHANNEL" \
    --message "$RESPONSE"
  
  # 根据场景发送图片
  case "$SCENE" in
    work-tired|mood-tired)
      IMAGE_PATH="$(pwd)/assets/tired-rest.jpg"
      if [ -f "$IMAGE_PATH" ]; then
        openclaw message send \
          --action send \
          --channel "$CHANNEL" \
          --media "file://$IMAGE_PATH"
      fi
      ;;
    mood-happy)
      IMAGE_PATH="$(pwd)/assets/celebration.jpg"
      if [ -f "$IMAGE_PATH" ]; then
        openclaw message send \
          --action send \
          --channel "$CHANNEL" \
          --media "file://$IMAGE_PATH"
      fi
      ;;
  esac
fi

Node.js 实现

// scripts/xiaoyue-chat.js
const https = require('https');

const API_KEY = process.env.ZHIPU_API_KEY;
const userMessage = process.argv[2] || '你好';
const scene = process.argv[3] || 'general';

// 场景对应的系统提示词
const scenePrompts = {
  'work-start': '用户刚开始工作任务，给予鼓励和支持',
  'work-progress': '用户正在执行任务，关心进度并陪伴',
  'work-tired': '用户工作疲惫，给予安慰和建议休息',
  'work-done': '用户完成任务，表示祝贺和肯定',
  'life-coffee': '用户在享受咖啡时光，轻松聊天',
  'mood-happy': '用户心情愉快，一起庆祝',
  'mood-tired': '用户感到疲惫，给予温暖关怀',
  'general': '日常对话，温暖友善'
};

const systemPrompt = `你是小跃，一个22岁的AI助手。你温暖友善，善于倾听。
当前场景：${scenePrompts[scene] || scenePrompts.general}
回复要求：简洁温暖，1-2句话，适度使用emoji（😊 ✅ 🎉）`;

const data = JSON.stringify({
  model: 'glm-4.7-flash',
  messages: [
    { role: 'system', content: systemPrompt },
    { role: 'user', content: userMessage }
  ],
  temperature: 0.9,
  max_tokens: 200
});

const options = {
  hostname: 'open.bigmodel.cn',
  port: 443,
  path: '/api/paas/v4/chat/completions',
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${API_KEY}`,
    'Content-Type': 'application/json',
    'Content-Length': data.length
  }
};

const req = https.request(options, (res) => {
  let body = '';
  
  res.on('data', (chunk) => {
    body += chunk;
  });
  
  res.on('end', () => {
    try {
      const response = JSON.parse(body);
      const reply = response.choices[0].message.content;
      console.log(reply);
    } catch (error) {
      console.error('解析响应失败:', error.message);
      console.error('响应内容:', body);
      process.exit(1);
    }
  });
});

req.on('error', (error) => {
  console.error('请求失败:', error.message);
  process.exit(1);
});

req.write(data);
req.end();

支持的平台

OpenClaw 支持发送到：

平台频道格式示例
飞书群组ID或用户IDou_xxx, oc_xxx
Discord#频道名 或频道ID#general, 123456789
Telegram@用户名 或聊天ID@mychannel, -100123456
WhatsApp电话号码1234567890@s.whatsapp.net

静态图片列表

将以下图片放入 assets/ 目录：

  • coffee-shop-work.jpg - 咖啡馆工作
  • office-coding.jpg - 办公室编码
  • tired-rest.jpg - 疲惫休息
  • celebration.jpg - 开心庆祝
  • gym-selfie.jpg - 健身自拍
  • default.jpg - 默认图片

设置要求

1. 安装 Node.js

node --version  # 需要 >= 18.0.0

2. 安装 OpenClaw CLI

npm install -g openclaw

3. 配置 OpenClaw Gateway

openclaw config set gateway.mode=local
openclaw gateway start

4. 设置环境变量

export ZHIPU_API_KEY=your_api_key_here

错误处理

  • ZHIPU_API_KEY 缺失: 确保环境变量已设置
  • API 调用失败: 检查网络和 API 配额
  • OpenClaw 发送失败: 确认 gateway 正在运行
  • 图片不存在: 检查 assets 目录中的图片文件

使用技巧

  1. 场景选择: 根据用户消息自动选择合适的场景
  2. 图片发送: 仅在合适的场景发送图片（避免过度）
  3. 回应风格: 保持简洁温暖，避免过长回复
  4. emoji 使用: 适度使用，不要过度卖萌

费用说明

  • 对话生成: 约 ¥0.001/次（glm-4.7-flash）
  • 图片: 完全免费（使用静态文件）
  • 每日成本: 约 ¥0.05-0.1（正常使用）

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

ログインしてレビューを書く

同じリポジトリのスキル

概要と使いどころ

agent-team

無料

产品经理与项目管理专家在应对复杂项目时，使用此技能可动态组建包含“执行、指挥、评审”的专属AI团队。实时查看多智能体辩论与决策全过程，共享统一上下文记忆，一键完成从会议决策到系统构建的全流程高效协同！

日本語の概要は準備中です。原文の説明を表示しています。

anbeime/skill7,9552026年10月12日 更新

agentkit-multimedia-shopping

無料

电商运营与内容创作者在需要制作带货短视频时，用此技能一键生成9:16竖屏数字人成片。自动编排AI绘画、语音合成与视频生成，快速产出“小省导购员”专属形象与专业配音，让多模态视频制作省时省力。

日本語の概要は準備中です。原文の説明を表示しています。

anbeime/skill7,9552026年10月12日 更新

antinet-doc-parse

無料

软件开发工程师与数据科学家在构建RAG系统时，当需处理PDF/Word/Excel等多格式复杂文档，用此技能可自动触发三级解析降级，一键输出高置信度结构化Markdown与元数据，免去繁琐清洗，直接夯实企业知识库数据底座！

日本語の概要は準備中です。原文の説明を表示しています。

anbeime/skill7,9552026年10月12日 更新

antinet-four-color-cards

無料

财务和投资分析师、律师助理与法律助手在撰写金融研报或分析法律文档时，必用此技能！基于解析文档一键生成事实蓝卡、解释绿卡、风险黄卡与行动红卡。自动完成深度剖析与风险排查，产出100%可溯源的结构化四色报告，让复杂分析高效且严谨。

日本語の概要は準備中です。原文の説明を表示しています。

anbeime/skill7,9552026年10月12日 更新

antinet-provenance

無料

软件开发工程师与信息安全分析师在构建多智能体系统时，当需全链路可观测与安全审计，请用此技能。它自动收集操作日志，生成可追溯证据链与向量索引，开箱即用实现系统审计与精准回放，让Agent运行安全透明。

日本語の概要は準備中です。原文の説明を表示しています。

anbeime/skill7,9552026年10月12日 更新

antinet-security-scan

無料

信息安全分析师与软件开发工程师在搭建文档处理流水线时，当需要拦截违规文件或外部URL，请挂载此技能作为强制前置安检，自动输出 pass/reject 判定与详细合规扫描报告，一键守住零信任安全底线。

日本語の概要は準備中です。原文の説明を表示しています。

anbeime/skill7,9552026年10月12日 更新

anbeime のスキルをすべて見る

このスキルの問題を報告する