12d • General discussion
Automate local newsletters with AI
This can be applied to any type of newsletter.
How I Automated a Local Newsletter in Claude Code (5 to 10 Minutes End to End)
I built an automation in Claude Code that handles the full workflow of a local newsletter, from research to a polished HTML preview, in about 5 to 10 minutes.
This post breaks down the approach so you can copy the system and adapt it to your own city or niche.
What a Local Newsletter Is
A local newsletter is a curated digest of:
- Events happening soon
- Local news and openings
- Things to do and places to go
- Community updates, deals, and highlights
Most are monetized through sponsorships and ads, and some add revenue through events or products.
The hard part is not writing. It is research.
The Problem: Manual Research Is the Bottleneck
If you have ever run one of these, you know the weekly routine:
- Scroll Instagram posts and stories
- Check event sites and venues
- Skim local news sources
- Pull details, dates, ticket links, and addresses
- Then rewrite it all into a consistent format
It is repetitive, time-consuming, and perfect for automation.
The Core Idea: Break the Workflow into Stages
A local newsletter can be treated like a pipeline with four stages:
- Research
- Writing
- Polishing
- Deployment
In Claude Code, I implemented each stage as a separate reusable skill.
The goal is simple: build each skill once, then reuse it forever.
Stage 1: Research
Research has two parts:
- Find sources
- Extract information from those sources
Finding Sources
For local newsletters, some of the best sources are Instagram accounts:
- local venues
- community pages
- restaurants and cafes
- galleries and event organizers
I had Claude Code find relevant Instagram accounts using:
- web search for discovery
- browser automation through an MCP server to navigate Instagram directly when needed
Extracting Information
Once the sources are identified:
- Claude Code scrapes posts from the target accounts
- It parses both the captions and the images
- Since Claude is multimodal, it can read details from event posters, flyers, and screenshots
Output from the research stage is saved into a structured Markdown file, which becomes the input for writing.
Stage 2: Writing
Writing is downstream of research, but it needs:
- a consistent format
- a consistent voice
- repeatable content buckets
Copying a Proven Format
Instead of inventing a style, I modeled an existing newsletter that already works.
From that analysis, Claude Code generated a dedicated style skill that captures the patterns of the newsletter, such as:
- how items are introduced
- how much detail is included
- how links are presented
- how sections flow
- how the tone is maintained
Defining Content Buckets
I also had Claude Code identify the recurring content buckets, then map them to skills.
Example buckets might include:
- weekend events
- new openings
- food and drink picks
- local news
- featured spotlights
Each bucket gets:
- a research pattern
- a writing template
- formatting rules
That structure is what makes the whole thing fast and consistent.
Stage 3: Polishing and Preview
Local newsletters live or die by presentation.
The writing might be good, but the final product needs to look clean, scannable, and branded.
For the newsletter I modeled, design details mattered a lot, so I automated these too.
Recreating the Look
Claude Code researched and collected the formatting elements used in the reference newsletter, including:
- section separators and line breaks between content buckets
- fonts
- color scheme
- logos and other assets
- any reusable CSS patterns
Then it generated an HTML preview of the finished newsletter so the output is immediately reviewable and ready to ship.
Stage 4: Deployment (Optional, but Obvious Next Step)
For a full production version, the final step is deployment.
That means sending the final HTML or Markdown to your newsletter provider via API (Beehiiv, Substack, Mailchimp, etc., depending on what you use).
I did not include deployment in the core build, but it is a straightforward extension once the content generation is stable.
Why This Works
This automation works because:
- It separates tasks into modular skills
- It treats research as a repeatable input problem, not a manual scavenger hunt
- It standardizes writing through a style model and content buckets
- It produces output in a usable format (Markdown plus HTML preview)
Once the pipeline exists, the weekly workload becomes review and minor edits instead of starting from scratch.
If You Want to Build Your Own
Here is the simplest way to replicate this:
- Pick a local area and define the newsletter categories you want
- Build the research skill first and output to structured Markdown
- Build a writing skill that turns that Markdown into your format
- Add a polishing skill that produces a clean HTML preview
- Add deployment last, once everything else is stable
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Automate local newsletters with AI
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