How to Extract Service Planning Insights from Overseas Community Data
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When you try to conduct market research with social data after watching tutorials on YouTube or blogs, you quickly hit a wall. Example code throws errors in the terminal, and after running open-source tools a few times, you get hit with tens of dollars in API charges. Looking at the collected text, half of it consists of advertisements and Twitter spam, so no matter how hard you look, there is virtually nothing useful for service planning.
This is the very first barrier encountered by solopreneurs or novice PMs who don't come from a development background. When using open-source tools like last30days-skill, what matters isn't grand AI trends. It's about setting up an environment that strictly caps costs at $5 a month, filters out unnecessary noise, and uncovers business opportunities from user complaints.
To avoid getting stuck during environment setup, you should install using verified commands. Open a Linux or macOS terminal and run the commands in order.
sudo apt update && sudo apt install -y git nodejs npm gh pipx.pipx install yt-dlp && pipx ensurepath to place the asynchronous collection tool in an isolated space so it doesn't conflict with your existing Python environment./plugin marketplace add mvanhorn/last30days-skill, followed by /plugin install last30days to complete the collection skill installation. If you are using Cursor or Codex, install it globally with npx skills add mvanhorn/last30days-skill -g.Once installation is complete, create a .env file at the root of your project folder and insert your API keys. Write down your Anthropic key (ANTHROPIC_API_KEY) and Scrape Creators key (SCRAPECREATORS_API_KEY) for X collection. Make sure to add a .env line to the .gitignore file in the same folder so keys don't leak into public repositories like GitHub.
Do not run the script immediately after adding the keys. The Anthropic console initially sets a monthly limit of $100. The moment a code loop goes wrong, money will vanish from your account in an instant.
To control spending, go to the Billing menu in the Anthropic Developer Console (platform.claude.com). Change the Monthly Spend Limit to $5. Turn off the Auto-reload switch directly below it as well. Now, the moment monthly spend reaches $5, all API requests will stop.
Because last30days-skill scrapes Reddit, Hacker News, and GitHub data using open APIs and public JSON, the raw data collection cost is $0. When refining collected posts, pairing prompt caching with the Claude 3.5 Haiku model allows you to thoroughly analyze hundreds of community posts daily, even on a $5 monthly budget.
X and Reddit are overflowing with product promotional posts and affiliate links. If you collect these as-is, it's easy to mistake fake trends for genuine market needs.
While last30days v3 uses a relevance algorithm by default, you need to manually tweak search parameters to leave clean data. When collecting Reddit data, narrow the scope to specific subreddits like r/SaaS, r/micro-saas, and r/SideProject, and specify fetching mainly comments with high upvote counts.
When scraping X data, you must exclude promotional tweets posted directly by creators.
about:"SaaS tool" -from:promoted after the search command to strip out ad tweets.INCLUDE_SOURCES=reddit,hackernews,github option in the terminal.--as-of=2026-05-01 to lock the collection period.When filters are applied this way, viral marketing posts are almost entirely filtered out. Only actual questions and answers exchanged between real users remain in the collection results.
Collected text data is raw language poured out by users, strictly speaking. Reading through it as-is only consumes time. Running the prompt below to organize the text into 4 complaint types can save more than 6 hours of market research time every week.
`text
[Role Definition]
You are a Senior Product Manager (PM) specializing in B2B SaaS and IT products.
[Input Data]
Raw text of social data from the last 30 days collected by running /last30days
[Categorization Instructions]
Excluding simple emotional venting from the collected text, categorize complaints that occurred while using existing products/services into the 4 categories below and organize them in a Markdown table format.
[Output Format]
Category | Major Cited Raw Quotes (Actual User Language) | Frequency/Upvote Count | Extractable New Service Planning Opportunities
`
Once categorization is complete, convert the actual sentences written by users into landing page copy or planning document phrasing. For example, if there were many posts on Reddit saying "I'm scared because my API bill suddenly came out to $200," you can extract main landing page copy like "A real-time automatic hard-cap system that eliminates API billing anxiety."
Collected results can be directly exported as a clean briefing web page using the --emit=html option. Locally saved analysis data can be pulled in as-is to fill out PRD document templates.
`markdown
Users are currently losing [X hours] weekly due to [specific technical friction] of existing solutions.