Insurance News Aggregation & Keyword Analysis
Every six hours, it scrapes insurance news, filters for keyword relevance.
What it does
You receive curated insurance news every six hours that matches your specific keywords, saving you from sifting through irrelevant updates. This process automatically extracts clean text, filters out low-relevance items, and stores the high-quality articles in your marketing library and AI knowledge base for immediate use.
Step by step
- Set up a scheduled HTTP request trigger that fires every six hours to fetch RSS feeds and specific news URLs.
- Use code blocks with libraries like BeautifulSoup or Cheerio to parse HTML content and extract clean text from article bodies.
- Configure Supabase table schemas for storing metadata, full text, and keyword tags with appropriate indexing for fast retrieval.
- Implement a deterministic relevance filter using predefined keyword lists and simple string matching logic before database insertion.
Where the LLM does the work
- Generate dynamic prompt templates for the LLM to summarize article snippets and extract nuanced context beyond simple keyword matches.
- Refine the relevance scoring algorithm by analyzing sample articles to determine optimal thresholds that balance coverage with noise reduction.
- Create varied keyword expansion lists based on industry trends to ensure the system captures emerging topics not in the static list.
Watch out for
RSS feeds often return inconsistent HTML structures so you must add robust error handling and fallback parsers for malformed pages.
Supabase row-level security policies can block writes if not explicitly configured for your service role key, so verify permissions early.
Keyword matching may flag outdated press releases as relevant, so add a freshness check based on publication dates to filter stale content.
Tools that fit
HTTP Request Service Scraping RSS feeds and web pages
Code API Parsing HTML with Cheerio and filtering content
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Insurance News Aggregation & Keyword Analysis: Every six hours, it scrapes insurance news, filters for keyword relevance. ## Specification - What it does: You receive curated insurance news every six hours that matches your specific keywords, saving you from sifting through irrelevant updates. This process automatically extracts clean text, filters out low-relevance items, and stores the high-quality articles in your marketing library and AI knowledge base for immediate use. - Trigger: On a schedule (Scheduled · every 6 hours) - Autonomy: Fully hands-off - Expected setup effort: under an hour - Tools/services involved: - HTTP Request: Scraping RSS feeds and web pages - Code: Parsing HTML with Cheerio and filtering content - Supabase: Storing articles in dual database storage ## Known pitfalls, handle each one explicitly in your implementation 1. RSS feeds often return inconsistent HTML structures so you must add robust error handling and fallback parsers for malformed pages. 2. Supabase row-level security policies can block writes if not explicitly configured for your service role key, so verify permissions early. 3. Keyword matching may flag outdated press releases as relevant, so add a freshness check based on publication dates to filter stale content. ## Reference implementation https://n8n.io/workflows/9507 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/insurance-news-aggregation-keyword-analysis/ via usecasesforagents.com
Frequently asked questions
Can I use a different Database than Supabase?
Yes. Supabase is only the example database in this recipe. The same flow works with MongoDB, MySQL, PostgreSQL and Redis. Swap the database connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for data analysts who want the pull-and-summarize grind automated.
Marketers can point this at their own content pipeline with no changes to the pattern.