Automations

LinkedIn Job Alert Tracker

I was getting 10 to 15 LinkedIn job alert emails a day and tracking applications manually. Each email contained 5 to 6 listings with inconsistent formatting. I built a pipeline that monitors my inbox, parses each listing out of the email, classifies it by role type using Claude, and logs everything to a Google Sheet with a status column for tracking progress.

The parsing was the interesting problem. LinkedIn alert emails have variable line structures — some listings include “Easy Apply” or “Actively Hiring” badges between the job details and the URL. I solved it by walking backwards from each “View job:” URL and skipping known noise lines rather than parsing forward from a predictable starting point.

Runs every 60 minutes, fully unattended. Every listing categorized across 13 role types without touching it.

Reddit Pain Point Monitor

Built to surface small business owners expressing workflow pain points across four subreddits: r/smallbusiness, r/bookkeeping, r/accounting, and r/realtors. The goal was to validate automation pain points for freelance work and generate qualified outreach candidates.

The pipeline fetches 100 posts per subreddit daily, pre-filters by engagement signals, then runs two Claude calls on qualifying posts: one to score the post 0 to 10 for automation pain point relevance, and a second to generate a helpful, non-salesy reply. Qualifying posts land in a Google Sheet with the AI score, a pain point summary, and a ready-to-use suggested reply.

Subreddit selection was deliberate. r/automation and r/nocode were excluded because those are builders, not buyers. Higher-volume communities like r/entrepreneur were excluded to keep daily volume manageable. The four selected communities have real operational problems and the right audience.

Processes roughly 400 raw posts daily and yields 15 to 30 qualifying leads.

WorkRamp + Marketo Learning Integration

All contacts, course names, company names, and event data in this case study are mock, generated for testing and demonstration purposes only. No real user data was used.

ActivTrak was onboarding WorkRamp as its learning management platform with no native Marketo integration. Without visibility into learning activity, there was no way to trigger lifecycle emails from course completions, score leads on product education engagement, or identify users who enrolled but never finished.

I designed a middleware architecture that passes WorkRamp learning events into Marketo as custom object records, one row per event, linked by Salesforce Contact ID. The workflow routes by event type, checks email eligibility before writing any record, generates pipe-delimited course recommendations designed for Velocity token consumption, and runs a separate hourly workflow to flag stalled enrollments after 72 hours.

The SFDC Contact ID was the right join key, not email. Email addresses change and contacts merge. The Contact ID is immutable and already present in both Marketo and WorkRamp via provisioning.

Airtable stands in for the Marketo custom object in the case study, mirroring the same field structure and append-only record behavior. Swapping destinations requires a credential and endpoint change only.

Otto Routes ICP Prospecting Pipeline

Built to identify and qualify companies most likely to need Otto Routes: companies with 100 to 250 employees using Salesforce or HubSpot and running Front or Intercom as a shared inbox tool.

Companies are sourced from G2 reviews of Front and Intercom, enriched and scored in Clay across four signals (CRM confirmed, shared inbox confirmed, headcount in range, ops contact found), and pushed to Airtable when they score 4 or higher. An n8n workflow triggers on each new qualified row, generates a personalized cold email using Claude with the contact's specific CRM and inbox tool referenced, and writes it back to Airtable for review.

The outreach prompt leads with the founder story and references the contact's actual tool stack. The goal is a reply, not a pitch.