According to AlphaMoat, as of 2026-10, TikTok Crawling (yt-dlp) has 11,806 downloads and 18 stars — ranked by downloads #2,326 among 10,914 AI skills in this ranking, and #223 of 915 in Data Analysis.
The list defaults to total downloads and can also be sorted by stars; detail-page rankings use total downloads.
| Platform | Downloads | Stars | Comments |
|---|---|---|---|
| ClawHub ↗ | 6.4K | 13 | - |
| SkillHub ↗ | 11.8K | 18 | — |
Platforms count differently and update at different times.
It uses yt-dlp to fetch TikTok video and audio for later content review and analysis, and can limit downloads to works from a recent date range. Fetching and transcoding depend on yt-dlp and ffmpeg, and DATA_DIR sets where files land. Confirm that you are authorized to download and retain each creator’s media and follow applicable platform rules; this is a command-line guide, not an official TikTok API or a guarantee of stable access.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-10 | 11.8K | — | 18 |
| 2026-09 | 8.1K | — | 18 |
| 2026-08 | 6.2K | +8.1% | 18 |
| 2026-07 | 5.8K | — | 13 |
| 2026-05 | 5.1K | — | 16 |
| 2026-03 | 4.0K | — | 13 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: tiktok-scraping-yt-dlp description: Use for TikTok crawling, content retrieval, and analysis --- # TikTok Scraping with yt-dlp yt-dlp is a CLI for downloading video/audio from TikTok and [many other sites](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md). ## Setup
```bash
# macOS
brew install yt-dlp ffmpeg
# pip (any platform)
pip install yt-dlp
# Also install ffmpeg separately for merging/post-processing
```
---
## Download Patterns
### Single Video
```bash
yt-dlp "https://www.tiktok.com/@handle/video/1234567890"
```
### Entire Profile
```bash
yt-dlp "https://www.tiktok.com/@handle" \
-P "./tiktok/data" \
-o "%(uploader)s/%(upload_date)s-%(id)s/video.%(ext)s" \
--write-info-json
```
Creates:
```
tiktok/data/
handle/
20260220-7331234567890/
video.mp4
video.info.json
```
### Multiple Profiles
```bash
for handle in handle1 handle2 handle3; do
yt-dlp "https://www.tiktok.com/@$handle" \
-P "./tiktok/data" \
-o "%(uploader)s/%(upload_date)s-%(id)s/video.%(ext)s" \
--write-info-json \
--download-archive "./tiktok/downloaded.txt"
done
```
### Search, Hashtags & Sounds
```bash
# Search by keyword
yt-dlp "tiktoksearch:cooking recipes" --playlist-end 20
# Hashtag page
yt-dlp "https://www.tiktok.com/tag/booktok" --playlist-end 50
# Videos using a specific sound
yt-dlp "https://www.tiktok.com/music/original-sound-1234567890" --playlist-end 30
```
### Format Selection
```bash
# List available formats
yt-dlp -F "https://www.tiktok.com/@handle/video/1234567890"
# Download specific format (e.g., best video without watermark if available)
yt-dlp -f "best" "https://www.tiktok.com/@handle/video/1234567890"
```
---
## Filtering
### By Date
```bash
# On or after a date
--dateafter 20260215
# Before a date
--datebefore 20260220
# Exact date
--date 20260215
# Date range
--dateafter 20260210 --datebefore 20260220
# Relative dates (macOS / Linux)
--dateafter "$(date -u -v-7d +%Y%m%d)" # macOS: last 7 days
--dateafter "$(date -u -d '7 days ago' +%Y%m%d)" # Linux: last 7 days
```
### By Metrics & Content
```bash
# 100k+ views
--match-filters "view_count >= 100000"
# Duration between 30-60 seconds
--match-filters "duration >= 30 & duration <= 60"
# Title contains "recipe" (case-insensitive)
--match-filters "title ~= (?i)recipe"
# Combine: 50k+ views from Feb 2026
yt-dlp "https://www.tiktok.com/@handle" \
--match-filters "view_count >= 50000" \
--dateafter 20260201
```
---
## Metadata Only (No Download)
### Preview What Would Download
```bash
yt-dlp "https://www.tiktok.com/@handle" \
--simulate \
--print "%(upload_date)s | %(view_count)s views | %(title)s"
```
### Export to JSON
```bash
# Single JSON array
yt-dlp "https://www.tiktok.com/@handle" --simulate --dump-json > handle_videos.json
# JSONL (one object per line, better for large datasets)
yt-dlp "https://www.tiktok.com/@handle" --simulate -j > handle_videos.jsonl
```
### Export to CSV
```bash
yt-dlp "https://www.tiktok.com/@handle" \
--simulate \
--print-to-file "%(uploader)s,%(id)s,%(upload_date)s,%(view_count)s,%(like_count)s,%(webpage_url)s" \
"./tiktok/analysis/metadata.csv"
```
### Analyze with jq
```bash
# Top 10 videos by views from downloaded .info.json files
jq -s 'sort_by(.view_count) | reverse | .[:10] | .[] | {title, view_count, url: .webpage_url}' \
tiktok/data/*/*.info.json
# Total views across all videos
jq -s 'map(.view_count) | add' tiktok/data/*/*.info.json
# Videos grouped by upload date
jq -s 'group_by(.upload_date) | map({date: .[0].upload_date, count: length})' \
tiktok/data/*/*.info.json
```
> **Tip:** For deeper analysis and visualization, load JSONL/CSV exports into Python with `pandas`. Useful for engagement scatter plots, posting frequency charts, or comparing metrics across creators.
---
## Ongoing Scraping
### Archive (Skip Already Downloaded)
The `--download-archive` flag tracks downloaded videos, enabling incremental updates:
```bash
yt-dlp "https://www.tiktok.com/@handle" \
-P "./tiktok/data" \
-o "%(uploader)s/%(upload_date)s-%(id)s/video.%(ext)s" \
--write-info-json \
--download-archive "./tiktok/downloaded.txt"
```
Run the same command later—it skips videos already in `downloaded.txt`.
### Authentication (Private/Restricted Content)
```bash
# Use cookies from browser (recommended)
yt-dlp --cookies-from-browser chrome "https://www.tiktok.com/@handle"
# Or export cookies to a file first
yt-dlp --cookies tiktok_cookies.txt "https://www.tiktok.com/@handle"
```
### Scheduled Scraping (Cron)
```bash
# crontab -e
# Run daily at 2 AM, log output
0 2 * * * cd /path/to/project && ./scripts/scrape-tiktok.sh >> ./tiktok/logs/cron.log 2>&1
```
Example `scripts/scrape-tiktok.sh`:
```bash
#!/bin/bash
set -e
HANDLES="handle1 handle2 handle3"
DATA_DIR="./tiktok/data"
ARCHIVE="./tiktok/downloaded.txt"
for handle in $HANDLES; do
echo "[$(date)] Scraping @$handle"
yt-dlp "https://www.tiktok.com/@$handle" \
-P "$DATA_DIR" \
-o "%(uploader)s/%(upload_date)s-%(id)s/video.%(ext)s" \
--write-info-json \
--download-archive "$ARCHIVE" \
--cookies-from-browser chrome \
--dateafter "$(date -u -v-7d +%Y%m%d)" \
--sleep-interval 2 \
--max-sleep-interval 5
done
echo "[$(date)] Done"
```
---
## Troubleshooting
| Problem | Solution |
| ---------------------------------------- | --------------------------------------------------------------------------- |
| Empty results / no videos found | Add `--cookies-from-browser chrome` — TikTok rate-limits anonymous requests |
| 403 Forbidden errors | Rate limited. Wait 10-15 min, or use cookies/different IP |
| "Video unavailable" | Region-locked. Try `--geo-bypass` or a VPN |
| Watermarked videos | Check `-F` for alternative formats; some may lack watermark |
| Slow downloads | Add `--concurrent-fragments 4` for faster downloads |
| Profile shows fewer videos than expected | TikTok API limits. Use `--playlist-end N` explicitly, try with cookies |
### Debug Mode
```bash
# Verbose output to diagnose issues
yt-dlp -v "https://www.tiktok.com/@handle" 2>&1 | tee debug.log
```
---
## Reference
### Key Options
| Option | Description |
| ----------------------------- | ------------------------------------------- |
| `-o TEMPLATE` | Output filename template |
| `-P PATH` | Base download directory |
| `--dateafter DATE` | Videos on/after date (YYYYMMDD) |
| `--datebefore DATE` | Videos on/before date |
| `--playlist-end N` | Stop after N videos |
| `--match-filters EXPR` | Filter by metadata (views, duration, title) |
| `--write-info-json` | Save metadata JSON per video |
| `--download-archive FILE` | Track downloads, skip duplicates |
| `--simulate` / `-s` | Dry run, no download |
| `-j` / `--dump-json` | Output metadata as JSON |
| `--cookies-from-browser NAME` | Use cookies from browser |
| `--sleep-interval SEC` | Wait between downloads (avoid rate limits) |
### Output Template Variables
| Variable | Example Output |
| ----------------- | ----------------------- |
| `%(id)s` | `7331234567890` |
| `%(uploader)s` | `handle` |
| `%(upload_date)s` | `20260215` |
| `%(title).50s` | First 50 chars of title |
| `%(view_count)s` | `1500000` |
| `%(like_count)s` | `250000` |
| `%(ext)s` | `mp4` |
[Full template reference →](https://github.com/yt-dlp/yt-dlp#output-template)
中国金融机构数据爬取与分析综合 Skill v12.0.0。2334家/43大类机构名单、60+财经媒体源舆情、 A股报告/公告/研报、60+种查询多源金融数据(自动降级+缓存)、爬取调度与网页操作增强、 实时数据中枢、24 技术指标引擎、多源证据融合、110 MCP 工具 + 10 Resource + 14 Prompt。 🆕 v12.0.0 四条线(信息获取 / 打包下载 / 数据分析 / 登录): 编排:gather_info() 一次跑完"路由→抓取→去占位→质量分→能力边界→回执→打包", 8 类意图各带问法、输入输出、典型耗时与 not_for(说清哪件事做不了)。 诚实:反爬占位条目一律剔除并记账,空结果报 E_SOURCE_EMPTY,不把假数据判成 B 级成功。 交付:包内自带 manifest.json(逐文件 sha256),verify 查得出改动/缺失/多塞, 批量下载单条失败不中断整批,失败清单可直接只重下那几条。 登录:Cookie 由用户导出或扫码接管,档案化 + 健康度四档,对外输出全脱敏(值永不回显)。 何时触发:爬取机构、舆情爬取、东方财富、公告下载、实时行情、技术指标、统一查询、 交付包、批量重下、我已经登录了、登录态还有效吗(完整触发词见下方 auto_trigger)。
大宗商品价格数据查询技能。用于查询大宗商品期现货价格、宏观经济指标、产业链供需数据、进出口库存数据、金融市场数据等。当用户询问:(1) 期货/现货价格查询,如"螺纹钢价格"、"铜期货收盘价"; (2) 历史行情或趋势分析;(3) 区域或企业价格对比;(4) 宏观经济指标,如GDP、CPI;(5) 产业链上下游数据;(6) 多市场金融数据(期权、股票、债券、外汇);(7) 结算确认单生成时触发此技能。
将数据文件(CSV/TSV/TXT/XLSX/XLS/JSON)转化为独立交互式 ECharts HTML 图表——32 类图表、3 套主题,全离线无需 CDN。出图前可先取数据画像(--profile:每列 dtype/基数/缺失/样本/统计、实体粒度、候选洞察信号、疑似非数据行),让 agent 在用户没说想看什么时自己读懂数据并决定出什么图——哪列是 ID、哪列是维度、出什么图,全部由 agent 从画像的客观事实自行判断,技能不代笔;每张图内置事实卡(plot_stats + data_preview),其数值是解读时唯一可引用的数字。本技能负责出图与算事实,不产出书面报告或叙事文档。触发:用户提供或指向表格数据文件并要求出图;提到「画图/出图/可视化/生成图表/generate a chart/make a graph」;想把表格渲染成交互式 HTML 图表。不触发:要的是书面报告、叙事分析或幻灯片;没有数据文件;需要后端的仪表盘;图片/音频/视频等非表格内容。
The data layer for AdMapix: turn ad and app queries into API calls that return raw JSON only.
论文配图一键生成:一条命令从 JSON/CSV/Excel 生成顶刊级学术图表,科研绘图 与期刊配图全场景覆盖。40 种图表(森林图/KM 生存曲线(自动风险表)/竞争风险累计发生率/ROC/校准 曲线/决策曲线 DCA/列线图/RCS 非线性剂量反应/亚组分析森林图/RMST 限制平均生存时间/Galbraith 径向图/火山图/雨云图/山脊图/哑铃图/游泳图(逐例时长+里程碑)/ 肿瘤缓解瀑布图/UpSet 交集/PRISMA 流程图/漏斗/BA 一致性/PCA/热图/箱线/小提琴/ 组合图/Cox 多因素回归森林图等),9 套配色(含色盲安全 Okabe-Ito 与期刊色板),9 种期刊投稿预设, YAML 流水线一条命令出全套论文图,一次渲染多格式导出(TIFF/PNG/PDF), PDF 重叠门禁,600dpi。纯本地运行,数据不出机,中文零配置。适用:写论文/ 投稿要配图、把数据变成可投稿图表、批量重出全套图。不适用:文本润色/翻译/ 改写(请用写作类工具);商业报表/仪表盘/海报装饰;无数据支撑的纯示意图。
Intelligent chart generation and data analysis skill. Reads user-supplied data files (CSV/Excel/JSON), analyzes data characteristics with LLM assistance, auto-recommends and generates interactive ECharts visualizations. Use when the user asks to analyze data, generate charts, create visualizations, or work with tabular data files.
全球智能投研工具(stock-researcher)v13.0:多源数据 + 概率预测 + 可导出报告。 核心能力: - 多源数据路由:按市场/类别选源,逐级降级;每个结果标注来源与时效等级;源不可用时明确标 unavailable - 全资产量化:股票/ETF/场外基金/指数/期货(期限结构、基差、持仓、折溢价)多因子分析 - 概率预测:1d/5d/1M/3M/6M 经验分布 + 分位数 + 方向概率 + 区间,kNN 邻域收缩融合全局分布; 样本不足自动弃权,可按历史命中样本(同桶)做概率校准 - 证据链简报:每条结论绑定来源、时间与数据质量,冲突信号保留而不抹平 - 报告导出:Word/PPT/PDF/Markdown/JSON/纯文本,纯标准库渲染 + 可选第三方引擎 - MCP Server 24 工具,Claude Code 可直接调用;知识库 12 适配器双向同步 三条铁律: - 数据不装:拿不到就报不可用,不拿 0 价、不拿空表冒充 - 预测不装:概率来自历史相似条件的经验分布,样本不足就说不足,不硬给方向 - 评分≠盈利保证:任何结论都带免责声明
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Data as of 2026-10. Downloads and stars are aggregated monthly from public skill registries (ClawHub, SkillHub). See data methodology.