- Broaden Airtable lead counting to attr_utm_source IN ('Lead ads','landingpage')
— the 'landingpage' leads (100% fbclid, 0% gclid) were being missed entirely,
undercounting real leads for '_web' suffixed campaigns and skewing
capping/pacing decisions since yesterday's first production run.
- Add airtable_client.get_meta_leads_bulk() for day/curso-level aggregation.
- Drop per-familia Slack sectioning in favor of a single Formación block,
chunked by campaign batches instead.
- Add daily AT-vs-Meta table, per-curso PPL/CPL contrast table (leadform vs
landing breakdown), and a Claude-generated portfolio strategic diagnosis
(ported from leads-optimizer's portfolio_daily_analysis).
- Persist daily aggregate totals to a new Baserow table (daily_metrics) so
the dashboard and future reports don't depend on Meta's historical API
access remaining available indefinitely.
- Surface adset/ad-level recommendations in the campaign cards instead of
only numeric tables.
Windows console encoding crashed the script right after creating the first
table; make it re-runnable by skipping tables that already exist instead of
aborting entirely.
Ports meta-optimizer's Meta Ads execution/approval/creative-analysis layer
(agent.py, meta_ads_client.py, baserow_client.py, slack_notifier.py,
approval_server.py) and replaces the per-vertical CPL model with the
PPL + monthly-capping-per-course model already used by leads-optimizer,
via a new airtable_client.py that shares Cursos/Familias/CentroCurso/
CursoMes/Leads Lake with that project and adds Meta Ads Campaigns /
MetaCampaignMes alongside its Google Ads Campaigns / GACampaignMes.
