prospect-discovery-pipeline

Prospect Discovery Pipeline

End-to-end pipeline: PredictLeads lookalikes → ICP filter → Crustdata CMO finder → multi-signal enrichment → 2 LinkedIn variants drafted with per-lead personalization. Pauses for user review before any expensive operation. Always quotes credit cost up front.

When to use

• Building a target account list anchored on 1–2 known clients
• Generating a campaign-ready batch (10–25 leads with full signal context)
• Producing 2 A/B-testable LinkedIn message variants tied to actual signal data per lead

Don't use when: ad-hoc lookup of one company (use predictleads-signals); just lookalike domains without contacts (use predictleads-lookalikes); enriching a list you already have qualified leads for (use signals:enrich --result-set directly).

The 5-phase flow

Always follow this order. Quote credit cost before each phase.

Phase 1 — Discovery (2 PL credits for 2 anchors)

npx tsx src/cli/index.ts signals:similar --domain anchor1.com --limit 50
npx tsx src/cli/index.ts signals:similar --domain anchor2.com --limit 50

Merge into a candidate pool, dedupe by domain. Expect 30–80 unique candidates per pair.

Phase 2 — ICP filter (FREE, pause for user review)

Hand-filter the pool against the user's ICP criteria:

• Employee count (use Crustdata company_identify — FREE — only when judgement uncertain)
• Industry vertical (back-office SaaS, commerce infra, HR-tech, etc.)
• HQ region
• Marketing maturity proxies (visible content investment)

STOP and present the 10 finalists to the user before spending more credits. Surface any obvious gaps or weak fits. Wait for explicit approval.

Phase 3 — CMO finder (3 Crustdata credits, batch)

Single batch search across all 10 companies:

filters = {
  op: 'and',
  conditions: [
    { column: 'current_employers.company_website_domain', type: 'in', value: ['10 domains'] },
    { column: 'current_employers.title', type: '[.]', value: 'Marketing' },
    { column: 'current_employers.seniority_level', type: 'in', value: ['CXO', 'Vice President', 'Director'] },
  ],
}
limit: 50

Pick 1 marketing leader per company (prefer CMO > VP > Head > Director).

Common gotcha: some companies' websites are stored in Crustdata as ATS or marketing domains (e.g., hubs.li for Shopware), not their actual .com. If a company returns 0 hits, do a fallback search by current_employers.name substring.

Skip people_enrich unless the campaign needs emails (LinkedIn-only campaigns don't). Saves ~30 credits.

Phase 4 — Multi-signal enrichment (40 PL credits for 10 finalists)

for d in domain1.com domain2.com ...; do
  npx tsx src/cli/index.ts signals:fetch --domain "$d"
done

Or use the bulk shortcut if leads already in a result set:

npx tsx src/cli/index.ts signals:enrich --result-set <id>

Phase 5 — Hydrate templates + draft 2 variants (FREE)

Pick the single most outreach-relevant signal per company (most recent news > recent financing > recent job_opening). Build a personalization_natural line per lead that:

• Never says "I saw your [signal]" (per outbound rules)
• Embeds the signal as context for a category insight
• Stays ≤18 words per sentence
• Has no dashes, no I openers, says Hello, ends with a specific CTA

Draft both variants with different angles (e.g., results-led case study vs. category-shift narrative). Save the full draft to 00_Inbox/predictleads-discovery-{date}.md.

Do not push to Notion or activate the campaign without explicit user approval.

Total cost (typical)

| Phase | Credits | |---|---| | 1. Lookalikes (2 anchors) | 2 PL | | 2. ICP filter | 0 | | 3. CMO batch search | 3 Crustdata | | 4. Multi-signal enrichment (10 companies × 4 types) | 40 PL | | 5. Template hydration | 0 | | Total | ~45 credits (42 PL + 3 Crustdata) |

If people_enrich is needed: +30 Crustdata credits.

Verification checkpoints

The pipeline pauses at:

1. End of Phase 2 — present 10 finalists, wait for "approved"
2. End of Phase 5 — present hydrated drafts, wait for "approved"

Never push to Notion / activate Unipile campaign without explicit user approval at the second checkpoint.

Output artifacts

• SQLite: company_signals rows for the 10 finalists
• File: 00_Inbox/predictleads-discovery-{date}.md with the 2 hydrated variants
• Optional: HTML dashboard via predictleads-dashboard skill

Common pitfalls

• Megacaps in the lookalike pool: PredictLeads returns SAP/Microsoft/Oracle for B2B SaaS seeds. Filter manually before Phase 3.
• Crustdata domain mismatch: search by company name as fallback when domain returns 0.
• Personalization that flag-waves: "I saw your funding round" violates outbound rules. Reframe as category context.

Required env

PREDICTLEADS_API_KEY, PREDICTLEADS_API_TOKEN, CRUSTDATA_API_KEY in ~/.gtm-os/.env. See TEAM_SETUP.md.

Related skills

• predictleads-signals — single-company ad-hoc
• predictleads-lookalikes — discovery only, no outreach
• predictleads-dashboard — HTML viz of enriched signals
• unipile-campaign — what runs the actual outreach after this skill drafts the variants