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微信收藏可视化:从加密的微信本地数据库端到端解密、解析,生成交互式 HTML 可视化报告。 触发词:/wx-favorites-report、微信收藏可视化、收藏报告、收藏分析 支持输入:加密/解密后的 Favorite.db(SQLite)或 CSV/JSON 导出文件

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Source SKILL.md: https://github.com/zhuyansen/wx-favorites-report/blob/HEAD/SKILL.md

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微信收藏可视化 Skill

将微信收藏数据(加密 Favorite.db)端到端解密、解析、生成交互式 HTML 可视化报告。

执行流程

Step 1: 确认数据源

询问用户是否已有解密后的 Favorite.db。

  • 已有解密 DB → 跳到 Step 3
  • 未解密 → 进入 Step 2 密钥提取 + 解密

Step 2: 密钥提取与数据库解密(macOS)

2a. 准备签名副本

# 复制微信到桌面并去掉 Hardened Runtime
cp -R /Applications/WeChat.app ~/Desktop/WeChat.app
codesign --force --deep --sign - ~/Desktop/WeChat.app

2b. 安装 frida

pip3 install frida frida-tools

2c. 提取密钥

# 关闭原版微信,从桌面副本启动
killall WeChat 2>/dev/null; sleep 2

# 用 frida spawn 模式启动微信并 hook PBKDF2
PYTHONPATH=$(python3 -c "import site; print(site.getusersitepackages())") python3 << 'PYEOF'
import frida, time

JS_CODE = """
function buf2hex(buffer) {
    var a = new Uint8Array(buffer); var h = '';
    for (var i = 0; i < a.length; i++) h += ('0' + a[i].toString(16)).slice(-2);
    return h;
}
var found = false;
Process.enumerateModules().forEach(function(m) {
    if (found) return;
    m.enumerateExports().forEach(function(exp) {
        if (found) return;
        if (exp.name === "CCKeyDerivationPBKDF") {
            found = true;
            send("[*] Hook installed on " + m.name);
            Interceptor.attach(exp.address, {
                onEnter: function(args) {
                    this.pwLen = args[2].toInt32();
                    this.saltLen = args[4].toInt32();
                    this.rounds = args[6].toInt32();
                    this.pw = args[1]; this.salt = args[3];
                    this.dk = args[7]; this.dkLen = args[8].toInt32();
                },
                onLeave: function(retval) {
                    if (this.pwLen < 4 || this.pwLen > 256) return;
                    if (this.saltLen < 4 || this.saltLen > 64) return;
                    var saltHex = buf2hex(this.salt.readByteArray(this.saltLen));
                    var dkHex = buf2hex(this.dk.readByteArray(this.dkLen));
                    var pwHex = buf2hex(this.pw.readByteArray(this.pwLen));
                    send("[PBKDF2] r=" + this.rounds + " salt=" + saltHex);
                    var f = new File("/tmp/wechat_frida_keys.log", "a");
                    f.write("rounds=" + this.rounds + "\\npw=" + pwHex + "\\nsalt=" + saltHex + "\\ndk=" + dkHex + "\\n\\n");
                    f.flush(); f.close();
                }
            });
        }
    });
});
if (!found) send("[!] CCKeyDerivationPBKDF not found");
"""

device = frida.get_local_device()
pid = device.spawn(["/Users/" + __import__('os').getenv('USER') + "/Desktop/WeChat.app/Contents/MacOS/WeChat"])
session = device.attach(pid)
script = session.create_script(JS_CODE)
script.on("message", lambda msg, data: print(msg.get("payload", msg)))
script.load()
device.resume(pid)
print("WeChat running. Login → open Favorites → wait 60s...")
time.sleep(120)
session.detach()
print("Done. Check /tmp/wechat_frida_keys.log")
PYEOF

重要:微信启动后必须登录并打开「收藏」页面,触发 favorite.db 的密钥加载。

2d. 匹配密钥并解密

解析 frida 日志,找到 favorite.db 对应的 enc_key(通过 salt 匹配),然后用 Python 解密:

# 核心解密逻辑(内置在 parse_favorites.py 的上游)
import hashlib, hmac, struct
from Crypto.Cipher import AES

# enc_key = 从 frida 日志中 256000 轮 PBKDF2 输出匹配 DB salt 的 dk
# 逐页 AES-256-CBC 解密,HMAC-SHA512 校验

安装依赖:pip3 install pycryptodome

Step 3: 解析数据

python3 ~/.claude/skills/wechat-favorites-viz/scripts/parse_favorites.py \
  --input "<解密后的 favorite.db 路径>" \
  --output "<输出目录>/data.json"

兼容 WeChat 3.x (FavItems) 和 4.x (fav_db_item) 表结构。

Step 4: 生成可视化报告

python3 ~/.claude/skills/wechat-favorites-viz/scripts/generate_report.py \
  --input "<输出目录>/data.json" \
  --output "<输出目录>/report.html"

Step 5: 预览

# 推荐用 http server(file:// 下部分交互可能受限)
cd <输出目录> && python3 -m http.server 8765 &
open http://localhost:8765/report.html

报告功能

区域内容
统计卡片总数、时间跨度、日均收藏、最忙日
亮点发现最爱来源、主力类型
月度趋势折线 + 面积图
类型分布甜甜圈图
来源 Top15水平柱状图
活跃热力图星期 × 小时
时段分布小时 / 星期柱状图
词云标题 + 描述提取
标签云微信收藏标签
收藏浏览按类型筛选 + 按标签筛选 + 搜索 + 排序 + 分页
详情弹窗点击卡片展开完整内容、链接、来源、标签

收藏类型

Type含义解析字段
1文本desc
2图片datafmt + fullsize
4视频datatitle + datadesc
5文章/链接pagetitle + pagedesc + link + pagethumb_url
8文件datatitle + datafmt + fullsize
14聊天记录datalist → 逐条 datadesc 拼接
其他位置/笔记/小程序等通用 title + desc 回退

技术依赖

  • Python 3.9+ 标准库(sqlite3, json, re, html, collections, datetime)
  • PyCryptodome(仅解密步骤需要:pip3 install pycryptodome)
  • frida(仅密钥提取步骤需要:pip3 install frida frida-tools)
  • ECharts 5.x + echarts-wordcloud 2.x(CDN 内联,无需安装)

输出文件

<输出目录>/
├── data.json      # 解析后的结构化数据
└── report.html    # 单文件可视化报告(可直接分享)

已知限制

  • 图片/视频/文件的原始内容存储在微信 CDN(加密内部格式),本地缓存也加密,无法在报告中直接预览
  • 文章缩略图来自微信公众号 CDN,需联网加载
  • 密钥提取需要 macOS,frida 需要 ad-hoc 签名的微信副本