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douyin-report-search抖音举报搜索

Agent Skill

douyin-report-search 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

9,433

周安装

397

GitHub Stars

公开资料未说明

下载量

3,303
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:douyin-report-search(抖音举报搜索)
来源仓库:https://github.com/hhofchina/douyin-report-search
安装命令:
openclaw skills install douyin-report-search
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install douyin-report-search

简介

执行端到端抖音主题研究并生成分析报告。

  • 支持二维码登录与批量视频数据整理。douyin-report-search 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 自动整理内容特征、用户互动与趋势洞察。
  • 安装命令:openclaw skills install douyin-report-search,需准备登录环境与目标关键词。
  • 涉及大量数据抓取,请评估网络负载与合规边界。

SKILL.md

name
douyin-report-search
description
This skill automates end-to-end Douyin topic research and report generation. Given a search keyword and a target video count, it handles QR-code login, batch video collection via API interception, automatic CAPTCHA solving (slide-puzzle), detail page enrichment (likes, shares, collects, comments, followers), multi-factor engagement analysis, and final interactive HTML report generation. This skill should be used when the user wants to research Douyin content trends, analyze what makes videos go viral, or generate a data-driven report for any topic keyword.

Douyin Topic Research & Report Skill

Purpose

Automate the full pipeline: keyword → data collection → CAPTCHA bypass → enrichment → analysis → HTML report, replicating a proven workflow that successfully collected and analyzed 100 videos on the topic "女性成长".

Applicable Scenarios

  • "帮我分析抖音上 [关键词] 的视频,哪些因素让视频更多点赞转发"
  • "整理抖音 [话题] 最近 N 条视频数据,生成分析报告"
  • "我想研究抖音某类内容的爆款规律"
  • "给我一份抖音 [关键词] 的可视化数据报告"

Default Parameters

ParameterDefaultNotes
KEYWORD女性成长Search keyword (URL-encoded automatically)
TOTAL100Total videos to collect
DETAIL_LIMIT50Max videos to visit detail pages
COMMENTS_TOP5Top comments per video

Full Pipeline (5 Steps)

Step 1 — Environment Setup

cd <work_dir>
python3 -m venv venv && source venv/bin/activate
pip install playwright pillow numpy scipy scikit-image openpyxl
playwright install chromium

Step 2 — Login & Save Session

Run scripts/douyin_login.py (or adapt inline). The script:

  1. Launches Chromium (headless=False)
  2. Navigates to https://www.douyin.com
  3. Waits for user to scan QR code (polls document.cookie until login detected)
  4. Saves cookies to douyin_session.json

Key anti-detection settings (always apply):

args=["--disable-blink-features=AutomationControlled", "--no-sandbox",
      "--window-size=1440,900"]
# Init script:
"Object.defineProperty(navigator,'webdriver',{get:()=>undefined});"

Step 3 — Batch Video Collection

See scripts/collect_videos.py. Core logic:

  • Intercept search/item API response (aweme_list field contains video data)
  • Navigate to https://www.douyin.com/search/{keyword}?type=video
  • For batch 2+: scroll down 8× with window.scrollBy(0, 600) then wait 4s
  • Extract fields: aweme_id, desc (title), statistics (likes/shares/collects/comments), author.uid, author.nickname, author.follower_count, video.duration, text_extra (tags)

Step 4 — Detail Enrichment + CAPTCHA Solving

See scripts/parse_videos.py and scripts/captcha_solver.py.

CAPTCHA Solving Algorithm (proven, use exactly)

The algorithm is embedded in scripts/captcha_solver.py. Key findings from empirical testing:

  1. Template matching is the primary method (most accurate, directly gives left edge of gap)
  2. Sobel edge detection is secondary (detects right edge of gap → left peak of dual-peak = left edge)
  3. Decision: if diff ≤ 25px → weighted average (70% template + 30% Sobel); else → use template only
# Element selectors (抖音 captcha iframe)
captcha_frame selector: frame.url contains "verifycenter" or "captcha"
bg_el  = frame.locator(".captcha-verify-image").first
sl_el  = frame.locator(".captcha-verify-image-slide").first
btn_el = frame.locator(".captcha-slider-btn").first

# Slide distance formula
gap_center_abs = bg_bb["x"] + gap_x + sl_bb["width"] / 2
btn_center_abs = btn_bb["x"] + btn_bb["width"] / 2
slide_distance = gap_center_abs - btn_center_abs

Human-like Slide Path (ease-out + overshoot)

def ease_out_cubic(t): return 1 - (1 - t) ** 3

# overshoot 3-7px, then pull back in final 15% of path
# Y-axis jitter ±2px, X-axis jitter ±1px during 5%-80%
# Timing: fast phase (frac<0.5) 5-8ms, mid 10-18ms, slow 25-45ms

Refresh captcha between retries

rb = frame.locator(".vc-captcha-refresh,.captcha-refresh,[class*='refresh']").first

Step 5 — Analysis & Report Generation

See scripts/analyze_factors.py and scripts/generate_report.py.

Analysis dimensions (all proven to have measurable effect):

DimensionKey Finding
Duration2-3 min is sweet spot (15× better than >5 min)
Tag count1-2 tags >> 5+ tags (up to 6× difference)
Best tags#自我成长 #个人成长 #认知 #女生必看
Follower (log-corr)r=0.617, moderate positive
Title with +2× likes vs no exclamation
Title length11-20 chars optimal
Emotion keywordsLove/marriage/mood words → higher shares

Report output: douyin_analysis_report.html with 10 interactive Chart.js charts.

File Structure

work_dir/
├── douyin_session.json      # saved login cookies
├── douyin_raw_data.json     # raw collected videos
├── douyin_parsed.json       # enriched with detail data
├── analysis_result.json     # computed analysis metrics
├── douyin_report.xlsx       # Excel version
└── douyin_analysis_report.html  # final interactive HTML report

Critical Notes

  • headless=False is required for CAPTCHA solving (screenshot-based)
  • Always mask the slider overlay in the background image before edge detection:

bg_arr[:mask_h, :mask_w] = column_mean_fill

  • search_start = sl_w + 12 to skip the initial slider position area
  • Max retries for captcha: 5 attempts with captcha refresh between each
  • After captcha success, wait 3s before continuing
  • The douyin_session.json expires; re-login if 401/redirect to login page

Dependencies

playwright, pillow, numpy, scipy, scikit-image, openpyxl

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

93.99%
按下载量换算3,104

安全审计

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可疑

ClawScan

可疑

Static analysis

通过

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安装前确认

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