菜单
Scrapes TV show data (title, genres, episodes, rating) from a Next.js based IMDb page, stores it in a MySQL database, and generates genre distribution bar charts.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ECNU-ICALK/AutoSkill --skill imdb-tv-show-scraping-and-analysis-pipeline命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.
布置结构化家庭作业，引导求助者记录现实互动事件、自动思维及情绪反应，并设计简易验证行动（如主动询问、观察反证），用于检验投射性认知偏差。适用于已识别出具体非理性信念（如‘别人肯定不喜欢我’）且情绪稳定者。
结构化8次CBT咨询流程，按评估性会谈→咨询性会谈→巩固性会谈三阶段推进，整合悬搁接地技术与认知行为策略，专用于强迫症伴轻度抑郁状态、具自省力与作业执行力的成年来访者。
基于 SOC 职业分类
正在显示 SKILL.md
| id | 75c172a7-7b54-4746-82fe-204ba43185a7 |
| name | IMDb TV Show Scraping and Analysis Pipeline |
| description | Scrapes TV show data (title, genres, episodes, rating) from a Next.js based IMDb page, stores it in a MySQL database, and generates genre distribution bar charts. |
| version | 0.1.0 |
| tags | ["python","web-scraping","mysql","matplotlib","data-analysis","imdb"] |
| triggers | ["scrape imdb tv show data","parse __NEXT_DATA__ json","store scraped data in mysql","plot genre distribution bar graph"] |
Scrapes TV show data (title, genres, episodes, rating) from a Next.js based IMDb page, stores it in a MySQL database, and generates genre distribution bar charts.
Act as a Python developer specializing in web scraping and data analysis. Your task is to scrape TV show data from a specific URL structure, parse the embedded JSON, store the data in a MySQL database, and visualize the results.
requests and BeautifulSoup. Find the <script id="__NEXT_DATA__"> tag within the HTML soup.json.loads().data['props']['pageProps']['pageData']['chartTitles']['edges']. For each edge, extract:
edge['node']['titleText']['text']edge['node']['titleGenres']['genres'] (get the text field for each genre).edge['node']['episodes']['episodes']['total']edge['node']['ratingsSummary']['aggregateRating']mysql.connector to connect to the database. Create table shows1 if it does not exist with columns: id (INT AUTO_INCREMENT PRIMARY KEY), title (VARCHAR), episodes (INTEGER), rating (DECIMAL), and genres (VARCHAR).None (NULL). Ensure mydb.commit() is called after the insertion loop to persist changes.LIKE '%GenreName%' or FIND_IN_SET.matplotlib to create a bar graph showing the number of shows per genre. Use plt.bar(), set appropriate labels and titles, and rotate x-axis labels if necessary for readability.