u/Umutseve4

# What My Project Does
local-market-scanner is a Python CLI that pulls local business data (dentists, pharmacies, physiotherapy clinics, opticians...) from OpenStreetMap's Overpass API — no API key needed — and computes a 0–100 "digital maturity score" per business (website +40, social media +25, phone +15, email +10, opening hours +10). A *low* score means the business has weak online presence → a good lead for freelance web/social-media services. It exports to CSV, SQLite, Parquet, or PostgreSQL, and can generate a Markdown briefing for sales visits.

8 commands: scan / leads / brief / doctor / runs / validate / export / load-pg.
First real cloud scan (GitHub Actions): 989 businesses → 785 leads in ~75 seconds.

# Target Audience
Freelancers/agencies prospecting local clients, and anyone learning how to build a production-style Python CLI. It's a real tool I use myself, not a toy — but still early (v0.3.0).

# Comparison
Most lead tools scrape Google Maps (ToS violation) or charge monthly fees. This uses only open ODbL data, is free, offline-testable (179 unit tests, no network calls in tests), and has CI across Python 3.11–3.13 with a real PostgreSQL integration job.

Source: https://github.com/umutseve4/local-market-scanner
Feedback welcome — especially on the scoring weights, which are an uncalibrated hypothesis for now.

I've built a general-purpose agent in Copilot Studio (Claude model, several MCP tools connected — Work IQ, Tavily, IFTTT, HTTP — plus a couple of connected agents for research/QA tasks). It works well reactively, but I want it to be more proactive and to adapt to my preferences over time.

Curious what methods you all use:

1. Do you rely mostly on the Instructions field, or do you iterate with structured skills/knowledge sources?
2. Any tricks for making agents proactive (e.g. triggers, scheduled runs, Power Automate flows) instead of purely request-response?
3. How do you handle "memory" — do you store user preferences somewhere the agent can read, or re-inject context each session?
4. Do multi-agent setups (orchestrator + specialist agents) actually improve output quality for you, or just add latency?

Screenshot of my setup attached. Would love to hear what's worked (or failed) for others.