Cross-reference money_intent_surfacer.py (earning GSC queries) with li-network.json + leads.csv (network operators) to surface "these N operators in your network match the X earning query." Builds on existing data, no canonical mutations, no fake pages. Worth building when GSC recovery brings real money signals.
1. data/money/report-YYYY-MM-DD.json — output of money_intent_surfacer.py. Top earning + opportunity GSC queries with action recommendations. 2. data/li-network.json — 6,262-connection LinkedIn network with cluster tags + decision-maker flags + recency. 3. data/leads.csv — 547 prospects with status (queued/sent/replied/cold-archive/dead-archive).
Read the doctrine. Apply it on the next ship cycle.
Cross-reference money_intent_surfacer.py (earning GSC queries) with li-network.json + leads.csv (network operators) to surface "these N operators in your network match the X earning query." Builds on existing data, no canonical mutations, no fake pages. Worth building when GSC recovery brings real money signals.
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