List Expander — seed companies → lookalikes → mined filters → qualified TAM

The problem this solves

Niche lists come out tiny because we search databases with narrow explicit keywords, but real good-fit companies (e.g. Atlantic Medical Group, Hackensack Meridian) often don't carry that keyword in their database record. Instead of guessing keywords top-down, this skill works bottom-up: take companies you KNOW fit, discover how the database actually tags them, expand via lookalike engines, and only then derive the wide-net filters — with measured precision per filter — before pulling and AI-qualifying at scale.

Pipeline (5 phases)

All scripts live in scripts/, run with npx tsx, and are dependency-free (node ≥ 18 native fetch). Every script takes --help. Keys load from the repo-root .env (see .env.example), falling back to ~/.env. Artifacts land in ~/output/list-expander/{run}/.

Phase 1  fingerprint.ts    seeds → how each shows up in Prospeo + its live homepage
Phase 2  lookalikes.ts     seeds → candidates via Prospeo lookalike + Exa + Parallel
                           (qualify candidates → lookalikes-confirmed.csv)
Phase 3  mine-filters.ts   confirmed fits → candidate filters → scorecard (volume × precision)
Phase 4  pull.ts           winning filters → wide pull, auto-shard, dedup, exclusions
         score-batch.ts    scale AI qualification (gpt-5-nano by default)
         verify-website.ts second pass on QUALIFIED rows: live homepage fetch →
                           dead / suspended-parked / live; live sites re-judged on
                           their CURRENT content (catches stale-DB ghosts). Always run —
                           DB descriptions happily qualify dead companies otherwise.
Phase 5  contact-count.ts  verified-email TAM ceiling (free Prospeo count trick)
         report.ts         single-file HTML transparency report for the client

Phase 0 — inputs

• ~10 seed companies known for sure to fit (domains).
• 1–2 sentence ICP description ("multi-site medical/specialty groups in the US, ≥$3M revenue").
• Optional: exclusion CSV (companies already in campaigns).

Phase 1 — fingerprint the seeds

npx tsx scripts/fingerprint.ts --domains="a.com,b.com,..." --run=<slug>

Prints coverage (which seeds Prospeo is missing — that gap IS the under-count story for the client), plus industry and keyword-tag frequency tables. Writes fingerprint.json/csv.

Phase 2 — generate lookalikes

npx tsx scripts/lookalikes.ts --domains="<seeds>" --run=<slug> \
  --objective="<NL description of the COMPANY TYPE (not your product!)>" \
  --country="United States #US" --pages=3

• Prospeo company_lookalike (required lane): {"domain": "<seed>"} — one call per seed, single domain only (arrays 400). Composable with location/headcount/keyword filters. One large health system seed returned 5,727 lookalikes.
• Exa findSimilar (optional lane): content similarity, and it returns homepage text in the same call — free evidence for qualification.
• Parallel.ai entity-search (optional lane): ~$0.005/req, pads to match_limit with junk — always qualify before trusting; returns LinkedIn URLs, not domains.

Optional lanes whose key is unset are skipped with a log line, so the run still completes on Prospeo alone. Then qualify candidates (Claude sub-agents for a small set, or score-batch.ts with a draft prompt) → write lookalikes-confirmed.csv. Target 50–150 confirmed.

⚠️ EXHAUSTIVE-SWEEP DEFAULT

Phase 3's scorecard is for TRANSPARENCY, not selection. The pull in Phase 4 must include: (a) EVERY industry carried by ≥1 confirmed fit — whole industry, headcount band + geo only, no keyword narrowing; (b) EVERY discriminative keyword from confirmed fits across ALL industries. Score everything with the cheap model. Only band + geography are legal pre-filters. Snowball until net-new drops under 2–3%. Never trim the sweep to save AI cost — recall is the product. Pull keywords unless they are stopword-grade.

Phase 3 — mine + score filters

npx tsx scripts/mine-filters.ts --csv=<confirmed.csv> --run=<slug> --propose --country="United States #US"
# Claude reviews/edits {run}/candidates.json: prune generic n-grams, add synonym keywords
# (the "every medical group contains 'group'" trap — kill terms that are frequent but not discriminative)
npx tsx scripts/mine-filters.ts --run=<slug> --scorecard
# score the 25-company samples:
for f in ~/output/list-expander/<slug>/samples/*.csv; do
  npx tsx scripts/score-batch.ts --csv=$f --prompt-file=<icp-prompt.txt> --out=${f%.csv}-scored.csv; done
mkdir -p ~/output/list-expander/<slug>/samples-scored && mv ~/output/list-expander/<slug>/samples/*-scored.csv $_
npx tsx scripts/mine-filters.ts --run=<slug> --scorecard --precision-from=~/output/list-expander/<slug>/samples-scored

--propose fingerprints each confirmed company against Prospeo and fetches its live homepage, then mines 2–3-grams across that combined text. The homepage is the evidence source that matters: it says what a company calls itself today, which is exactly what a keyword filter has to match. Pass --no-scrape to mine from Prospeo descriptions only (faster, thinner).

Output: filter-scorecard.csv — per filter: Prospeo count, sampled precision, estimated qualified yield. Review with the user before Phase 4.

Phase 4 — wide pull + scale qualification

Tune the qualification prompt FIRST via /icp-prompt-builder (interactive, 10-company batches, 2 clean rounds to converge).

# Write {run}/winners.json (filter_sets + base_filters — format documented in pull.ts header)
npx tsx scripts/pull.ts --run=<slug> --test          # 2 pages/set sanity check FIRST
npx tsx scripts/pull.ts --run=<slug> --exclude=<existing.csv>
npx tsx scripts/score-batch.ts --csv=<run>/pull-all.csv --prompt-file=<icp-prompt.txt> --scrape --concurrency=8
npx tsx scripts/verify-website.ts --run=<slug> --prompt-file=<icp-prompt.txt> --concurrency=40

Verify the first test output shows real successes before the full run. Any filter set whose total_count exceeds 24k is auto-sharded (country → 51 states → headcount-band bisection) so the tail is never truncated.

Phase 5 — TAM ceiling + report

npx tsx scripts/contact-count.ts --csv=<qualified.csv> --run=<slug> \
  --titles="COO,VP Operations,..." --seniorities="C-Suite,Vice President,Head,Director"
npx tsx scripts/report.ts --run=<slug> --title="<Vertical> — TAM Expansion"
open ~/output/list-expander/<slug>/report.html

Requirements / env

Put these in the repo-root .env (copy .env.example), or ~/.env.

REQUIRED

OPTIONAL (each is one lane; unset = that lane logs skipped: <VAR> not set and the run continues)

No database is required. Every artifact is a file under ~/output/list-expander/{run}/.

Verified API facts

Prospeo pacing (measured): PROSPEO_MIN_INTERVAL_MS=200 (5 req/s) trips "Rate limit exceeded" after ~1,000 requests; 450 ms (~2.2 req/s) ran 1,550+ requests clean. The account limit is GLOBAL, so lib.ts paces every process through a shared slot file (~/.cache/prospeo-lock/) with a 450 ms floor, backs off 45 s on a rate-limit, and makes that penalty visible to every other running process. Identical request+page re-runs within 30 days are FREE (free:true) — re-pulling after a partial failure costs nothing.

Prospeo count trick: a page-1 call's pagination.total_count sizes any filter cheaply; add person_contact_details:{email:["VERIFIED"]} on /search-person for the verified-email ceiling. Seniority enum: Founder/Owner, C-Suite, Partner, Vice President, Head, Director, Manager, Senior, Entry, Intern — never "VP", never "President".

Related skills

• /icp-prompt-builder — tune the qualification prompt before Phase 4 (the score-batch script it refers to lives HERE: scripts/score-batch.ts)
• /prospeo-search-api — full Prospeo filter reference
• /prospeo-full-export — title-first paginated lead export once you have the company list
• /blitz-list-builder — domain-first contact discovery on the qualified companies
• /disco-like — a 4th lookalike source (seed domains or NL ICP text)
• /list-quality-scorecard — grade the final CSV before it goes anywhere near a campaign