| name | event-leads |
| description | Full lead-analysis pipeline for event/hackathon registration CSVs: verify every attendee against live web sources, score them per sponsor product, rank by social influence (X/GitHub followers), flag competitors/investors/sponsor-staff, and ship a sortable spreadsheet and/or password-gated web dashboard, plus curated Top-N builder and judge lists. Use this whenever the user has an attendee/registration/guest CSV (Luma, Eventbrite, etc.) and wants leads reviewed, screened, enriched, categorized, ranked, or turned into a dashboard; whenever they ask 'who are the top leads/builders/judges at this event'; for follow-up requests on an existing event analysis; and when they want to productize this (pull from the Luma API, scrape event calendars, or run autonomously). Trigger even if they just say 'review the attached attendee list for our sponsors'. |
Event Lead Analysis
Turn a raw registration CSV into verified, per-sponsor-scored, influence-ranked leads with curated builder/judge lists. The user supplies a CSV; everything below is the method.
Step 0 — Collect inputs (ask only for what is missing)
- CSV path (usually given). If macOS blocks ~/Downloads, ask the user to drag the file into the project folder.
- Sponsors/products with one-line ICPs. Scoring is per sponsor, so you need what each sells and to whom.
- Deliverable: spreadsheet, dashboard, or both. If deploying: domain plus password.
- Optional: event URL for track/prize context.
Step 1 — Ingest and triage
Parse with a BOM-tolerant encoding (Luma exports carry a BOM). Dedupe by email; keep all rows. Map columns to: name, email, company/project, build description, LinkedIn, Twitter/X, GitHub, website, demo video, phone. Compute a heuristic signal score per row to decide verification depth only, never final ranking. Flag sponsor-domain emails as do-not-pitch (all scores 0) and mark duplicate humans.
Step 2 — Verify everyone (two tiers)
Deep enrichment for the top ~10% by signal: verify identity, company, role, funding/stage, size; output ranked best-fit products, a 1 to 9 score, lead type, a concrete talking point, and flags. Light screening for everyone else in parallel batches, one line per lead. The LinkedIn slug is the identity anchor. Never assert what you cannot confirm; name collisions are the top hallucination source. Expect inflated titles and false founder claims; catch them, score honestly, and surface corrections in flags rather than fixing silently.
Step 3 — Score per sponsor (never one global score)
One column per sponsor. Best-fit product scores full, second best minus one, third minus two, absent minus three (floor 1). Investors score equally across sponsors. A sponsor-competitor employee or fund scores 1 for that sponsor with an explicit flag.
Step 4 — Social influence
Clean handles; reject placeholders and celebrity collisions. X followers and GitHub followers summed into a Social Reach and an Influence Rank, point-in-time. LinkedIn follower counts are not obtainable at scale; say so rather than faking them.
Step 5 — Deliverables
Spreadsheet with per-sponsor color-scaled score columns, lead type, why, flags, raw fields, handles, followers, rank, contacts. Dashboard as a single self-contained HTML file with live search, per-sponsor sort, filter chips, and a client-side HubSpot/Salesforce CRM export of the current filtered view (email as dedupe key, UTF-8 BOM for Excel). PII rule, non-negotiable: local by default; if deployed, private repo plus a server-side auth gate (HttpOnly cookie, middleware redirect), never a client-side prompt.
Step 6 — Curation layers (when asked)
Top-N builders weight demo video, social reach, best sponsor score, and verification tier, minus a big-co penalty; then a manual pass removes picks that fail scrutiny and backfills. Judges are senior investors/operators not demoing, balanced across capital, reach, and technical authority. Builders and judges must be mutually exclusive.
Step 7 — Update loop
On a new CSV export, diff by guest_id (fallback email), screen only additions, fetch followers for new handles only, drop removals and backfill any vacated curated slot, then rebuild and verify the live site actually changed.
Quality bar
Narrate counts at each stage, surface every correction where a registration claim failed verification, and finish with top leads per sponsor, the investor list, competitor warnings, and links to the deliverables.
Closing line
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