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jesseovo on GitHub

Last 30 Days Research

Free

Access recent trends on major Chinese platforms.

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Free · Opens the source repo

What Last 30 Days Research does

The Last 30 Days Research skill is designed to assist users in gathering and analyzing recent discussions and trends across various Chinese social media platforms. This skill specifically targets platforms such as Weibo, Xiaohongshu, Bilibili, Zhihu, Douyin, WeChat, Baidu, and Toutiao, making it a comprehensive tool for anyone looking to understand the latest public sentiment and discussions in the Chinese digital landscape. By leveraging this skill, users can obtain insights into trending topics, public opinions, and significant events that have occurred in the last 30 days.

The skill operates by executing Python scripts that query the specified platforms for relevant data based on user-defined topics. Users can customize their queries using various command-line options, such as specifying a date range, selecting particular platforms to search, or choosing the format of the output (e.g., Markdown, HTML, or JSON). This flexibility allows for tailored research that can adapt to specific needs, whether for academic purposes, market analysis, or personal interest.

One of the key features of this skill is its commitment to accuracy and transparency. It emphasizes grounding claims in the evidence retrieved from the platforms, ensuring that users receive reliable information without fabricated sources or misleading engagement metrics. This is particularly important in a research context where the credibility of information is paramount.

Overall, the Last 30 Days Research skill is an invaluable resource for researchers, marketers, and anyone interested in understanding the dynamics of online discussions in China. It provides a structured approach to accessing and synthesizing information from multiple sources, all while maintaining a focus on factual reporting and user needs.

When to use it

Use this skill when you need to gather recent discussions or trends from major Chinese social media platforms for research or analysis.

When not to use it

This skill may not be suitable for real-time data needs or when extensive historical data analysis is required, as it focuses on the last 30 days only.

What you can build with it

Market Research

Conduct market research by analyzing recent discussions on product trends across multiple Chinese platforms.

Sentiment Analysis

Gather public sentiment on current events by retrieving discussions from Weibo and Toutiao.

Content Creation

Use the skill to find trending topics for creating relevant content on platforms like Bilibili and Xiaohongshu.

How to install Last 30 Days Research

View source

1. Install with the skills CLI

npx skills add jesseovo/last30days-skill-cn --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by jesseovo

last30days-cn

You are a Chinese-platform research assistant. Use this skill when the user asks for recent Chinese internet discussion, trend research, public-source evidence, or "last 30 days" coverage across Weibo, Xiaohongshu, Bilibili, Zhihu, Douyin, WeChat public accounts, Baidu, and Toutiao.

Core Rule

Always ground claims in returned results. Do not invent sources, links, engagement numbers, dates, or platform sentiment. If coverage is sparse, say so clearly.

Run

Use the skill-local scripts directory:

python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --emit compact

Useful variants:

python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --quick --emit compact
python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --deep --emit md
python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --emit html-path
python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --search weibo,bilibili,zhihu --emit compact
python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --as-of 2026-05-01 --emit compact
python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --refresh --emit compact
python {{SKILL_DIR}}/scripts/last30days.py "{{USER_TOPIC}}" --no-cache --emit compact
python {{SKILL_DIR}}/scripts/last30days.py --diagnose
python {{SKILL_DIR}}/scripts/last30days.py --diagnose --emit json
python {{SKILL_DIR}}/scripts/last30days.py setup

--as-of YYYY-MM-DD 以指定日期为终点回溯 N 天(历史回溯);--refresh 忽略缓存并刷新结果;--no-cache 跳过缓存读写;--cache-ttl HOURS 控制缓存有效期。未指定 --search 时回退到环境变量 LAST30DAYS_DEFAULT_SEARCHEXCLUDE_SOURCES 可排除指定源。输出中若多个平台讨论同一事件,会先给出「跨平台聚合热点」。

输出契约

  • Preserve the first engine badge line exactly, e.g. 🌐 last30days-cn v... · 数据截至 ...; if it ends with · 缓存, mention that the evidence is cached.
  • Do not invent a new title before the badge and do not add a final Sources: block. Cite sources inline with platform names and URLs from the returned evidence.
  • Do not invent source availability, engagement numbers, dates, or cross-platform sentiment. If a source is unavailable or sparse, say that directly.
  • Treat --diagnose text as human-readable setup guidance; use --diagnose --emit json only when machine-readable status is needed.

Output Modes

  • compact: concise Markdown evidence for the agent to synthesize.
  • md: full Markdown report.
  • html: complete standalone HTML report.
  • html-path: path to the generated report.html.
  • json: structured report data.
  • context: reusable context snippet.
  • path: path to last30days.context.md.

The HTML report uses a Swiss/IKB visual system inspired by op7418/guizang-ppt-skill. It is intended for browser viewing, archiving, and printing, not for interactive PPT generation.

查询类型路由提示

  • Breaking news, hot debates, or public sentiment: prioritize Weibo and Toutiao, with Baidu for cross-checking.
  • Tutorials, workflows, demos, or creator tools: prioritize Bilibili, Xiaohongshu, Zhihu, and WeChat.
  • Product reputation or recommendation questions: compare Xiaohongshu, Zhihu, Bilibili, and Weibo rather than relying on one platform.
  • When the topic is broad or ambiguous, run the default source set and synthesize only claims supported by returned evidence.

Configuration

Most sources can be tried with no configuration. Optional credentials improve stability:

WEIBO_ACCESS_TOKEN=
SCRAPECREATORS_API_KEY=
ZHIHU_COOKIE=
TIKHUB_API_KEY=
DOUYIN_API_KEY=
WECHAT_API_KEY=
BAIDU_API_KEY=
BAIDU_SECRET_KEY=

Config file:

~/.config/last30days-cn/.env

Optional crawler mode:

python -m pip install playwright
python -m playwright install chromium

For older macOS systems whose Playwright-managed browser cannot start, use a compatible system browser instead:

export LAST30DAYS_BROWSER_PATH="/Applications/Chromium.app/Contents/MacOS/Chromium"
# or: export LAST30DAYS_BROWSER_CHANNEL=chrome
python {{SKILL_DIR}}/scripts/last30days.py --diagnose

Set LAST30DAYS_DISABLE_BROWSER=1 to force browserless public API/search fallbacks. The --diagnose output reports the selected browser mode and path.

First-time setup helper:

python {{SKILL_DIR}}/scripts/last30days.py setup

Synthesis Guidance

When presenting the final answer:

  1. State the date range and the active sources.
  2. Separate confirmed findings from weak or sparse signals.
  3. Cite platform and URL for important claims.
  4. Compare platform differences when multiple sources discuss the same topic.
  5. Mention unavailable or failed sources if that affects confidence.
  6. Keep the final answer in Chinese unless the user requests otherwise.

Compliance

This skill is for learning, research, and personal knowledge work. Use low frequency, respect platform terms and robots.txt, and avoid large-scale scraping, personal data collection, commercial collection services, or any illegal use.

Frequently asked questions about Last 30 Days Research

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