# Icp Prospect List Builder > End-to-end methodology for building a large, source-cited B2B prospect dataset from public data — define an ICP in searchable terms, discover matching companies across ranked sources (source-code/technographic search, competitor customer bases, software + AI-tool directories, the Product Hunt API, accelerator portfolios), then categorize, ICP-score, dedupe, and pull size-routed decision-maker contacts. Use when asked to find "companies like our customers", build a GTM/sales target list, enumerate a market segment, score prospects for fit, or turn a raw company list into a prioritized, contactable pipeline. Public sources only. - Skill: `ever-just/icp-prospect-list-builder` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ever-just/icp-prospect-list-builder` - Raw SKILL.md: https://api.skillmd.com/api/skills/ever-just/icp-prospect-list-builder/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: ever-just (https://skillmd.com/u/ever-just) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ever-just/icp-prospect-list-builder --- # ICP Prospect List Builder ## Overview How to build a **prioritized, source-cited prospect dataset** — hundreds of companies with decision-maker contacts — entirely from public data. The output is two cross-linked tables (companies + contacts) where every company is categorized, scored for ideal-customer-profile (ICP) fit, and every claim is attributed and confidence-tagged. Use this skill when you need to: - Find "companies like our existing customers" and turn them into a ranked target list - Enumerate a market segment (e.g. "all website/app builders") across many sources - Score a raw company list for fit against a specific product's ICP - Attach the right contacts to each company (execs for small cos, functional leaders for large) - Produce a CRM-importable dataset, not a one-off report Developed and validated July 2026 building a **621-company / 900+-contact** GTM dataset for a custom-domain-onboarding product (Connect Domain, an Entri alternative), spanning disclosed-customer research, 10 accelerator portfolios + Y Combinator, a Minnesota regional ecosystem, and a Product Hunt API sweep — run with the **`parallel-agent-fanout`** skill. > **Ethics (non-negotiable):** public sources only; professional/business contacts > in their professional capacity only (no personal/home data); never fabricate — a > blank field beats a guess; every row carries a `source_url` + `confidence`. --- ## Step 1 — Define the ICP in *searchable* terms A fit definition you can't query is useless. Convert the ICP into (a) a one-sentence buyer description, (b) priority **sub-segments**, and (c) a **keyword lexicon** that appears on a matching company's site. Worked example (custom-domain onboarding product): - **Buyer:** multi-tenant SaaS platforms whose end-users connect their own custom domain during onboarding ("bring-your-own-domain"). - **Sub-segments:** website/site builders · AI website/app builders · e-commerce builders · newsletter/email + deliverability · link-in-bio/landing/funnel · publishing/CMS · course/community/membership · forms/booking/portfolio · white-label/agency/PaaS. - **Lexicon (recognize fit on-page):** `custom domain`, `connect your domain`, `bring your own domain`, `add a custom domain`, `custom sending domain`, `white-label domain`, `vanity domain`, `map your domain`, `SSL for SaaS`. - **Anti-signals (exclude):** hardware, robotics, biotech, deep tech, pure data/AI infra, consumer mobile apps, logistics, single-tenant internal tools. The lexicon is what every downstream search and every ICP-fit judgment keys on. --- ## Step 2 — Discover companies (sources ranked by signal) Sweep sources in roughly this order; higher tiers give cleaner ICP density. ### Tier 1 — highest signal - **Source-code / technographic search** — the single best move. Search page HTML for a competitor's embed string to get their *live customer list*: `PublicWWW` (`publicwww.com`), `NerdyData`, `BuiltWith` relationships. E.g. searching for a rival's widget script returns every platform that embeds it — literally your ICP. *(These bulk-index tools are usually paywalled; a paid seat is the highest-ROI data buy. Log the cap when gated.)* - **Competitor customer bases** — scrape case studies / logo walls / testimonials from adjacent vendors. Their customers *are* your ICP. Also mine any relevant open adopter list (protocol adopter pages, "customers of X" pages). ### Tier 2 — high volume, well-categorized - **Software directories** — G2, Capterra, GetApp, TrustRadius. Category pages map 1:1 to sub-segments; pull the *vendor* list, not reviewers. (G2 often 403s bots; fall back to category listicles / search snippets.) - **Product Hunt (API)** — best for newly launched tools. Use the GraphQL API, not scraping. See `scripts/producthunt_pull.py`. Query the **root `posts(topic:)`** field (the `Topic` type has no `posts` connection), order by `VOTES`, drop nested `topics` from the node (it inflates query complexity). Rate limit ~6250 complexity / 15 min — self-throttle on the `x-rate-limit-remaining` header. Note: the `website` field is a tracked `producthunt.com/r/...` redirect that 403s to bots, so resolve real domains later (during the contact pull) rather than from the API. - **AI-tool directories** — There's An AI For That, Futurepedia, Toolify. Best for the AI-builder wave the other sources lag on. ### Tier 3 — supplementary - Crunchbase industry filters · AlternativeTo (enter a known ICP tool → get its cluster) · app marketplaces / partner directories · Indie Hackers / BetaList / "Show HN" · GitHub awesome-lists. - **Accelerator portfolios** — filter each portfolio to ICP only. Reality check: ICP density is *low* in most accelerators (they fund biotech/hardware/fintech); a broad-thesis program (e.g. Y Combinator) is the exception. Filtering the full portfolio beats sampling it — but directories are often JS/Crunchbase-gated, so log coverage honestly. --- ## Step 3 — The data model (two grains, cross-linked) Maximum utility comes from two tables joined by a stable `company_id`: **`companies.csv`** — one row per organization: ``` company_id, dataset, segment, organization, domain, description, category, tags, icp_fit, icp_rationale, size_or_stage, location, num_contacts, source_url, confidence ``` **`contacts.csv`** — one row per person (carries `company_id` + joined `category`/`icp_fit` so people are filterable by company attributes): ``` company_id, dataset, segment, organization, contact_name, title, linkedin_url, email, email_status, source_url, confidence ``` `dataset` = the source pass (e.g. `entri-customers`, `accelerator-icp`, `producthunt-icp`) so pipeline slices stay separable. `email_status` ∈ `public | inferred-pattern | none`. --- ## Step 4 — Categorize + ICP-score Assign every company **exactly one** `category` from a *controlled vocabulary* (normalize messy free-text sectors into it) plus free multi-value `tags`. Rule: pick the real vertical, not a surface trait — an AI drug-discovery startup is `Healthcare & Life Sciences`, not `AI/ML`. Then the prioritization layer — **`icp_fit`** (High / Medium / Low / n-a) + a one-line `icp_rationale`: - **High** — the product clearly exhibits the ICP behavior (e.g. end-users attach their own domain). Existing customers of a direct competitor = High. - **Medium** — plausibly could, but doesn't obviously center on it. - **Low** — no ICP surface. - **n-a** — not a buyer (investors/accelerators; relevant as channel only). A deterministic keyword pass gives a fast backbone; a review pass (human or agent) corrects it and writes the rationale. This is what converts a directory into a *ranked* target list. --- ## Step 5 — Dedupe - Prefer **domain** as the dedup key (normalize: strip scheme, `www.`, path, query). Fall back to a normalized company **name** when no domain (e.g. Product Hunt launches) — strip version/suffix tokens (`2.0`, `v3`, `by `, trailing `AI`/`App`) so relaunches don't read as net-new. - Always dedupe **against the existing master** before adding a new pass; tag each candidate `net-new` vs `already-in-dataset`. Report the split — it is the honest signal of how much a source actually added. --- ## Step 6 — Pull contacts (routed by company size) - **Small (<50):** the executive team — founders / CEO / CTO / C-suite. - **Mid / large:** the **operations, financial, product, and technical** leaders (CPO/VP Product, CTO/VP Eng, COO/VP Ops, CFO) — and for very large orgs, only the leaders of the *relevant* org (e.g. the websites/domains product line), not the global CEO. - Per contact: name, title, `linkedin_url` (only if confidently the right person), `email` (public if published; a clear corporate pattern may be filled and marked `inferred-pattern`; else blank), `source_url`, `confidence`. - **Do not pad.** A tiny tool with one public founder gets one row. > **Email reality:** verified public emails are rare (often <2% for small/indie > tools). True deliverability verification needs SMTP tooling + clean IPs. So mark > inferred patterns honestly and tell the user to validate before outreach — > LinkedIn + a named founder is usually the reliable channel. --- ## Step 7 — Synthesize - A normalized **master CSV** per grain (single import). - A short **README/index** with headline counts, per-dataset breakdown, method, and the caveats above. - Optional: a self-contained, filterable **HTML dashboard** (search + category + `icp_fit` filters + distribution bars) — see the `dataviz`/`artifact-design` guidance if available. - A **Tier-1 shortlist**: `icp_fit = High` + has a named decision-maker + LinkedIn, sorted by fit strength → the ready-to-work outreach list. `scripts/rollup.py` normalizes many per-source CSVs (different schemas) into the two master grains and prints integrity stats. --- ## Pitfalls | Pitfall | Fix | |---|---| | "AI" keyword over-matches every company | Categorize by the real vertical, not surface tech | | Product Hunt `website` field is a redirect (403s to bots) | Resolve real domains during the contact pull, dedupe by name meanwhile | | Name-based dedup misses relaunches ("Framer 3.0") | Strip version/`by`/suffix tokens before comparing | | Accelerator/directory portfolios are JS/paywall-gated | Log coverage honestly; note a paid Crunchbase/PublicWWW seat as the unlock | | Treating a big candidate count as "prospects" | Item-level ICP density is often low — score fit, don't equate volume with value | | Inflating `icp_fit` from a topic label | Confirm the BYO/ICP surface on the company's own site before scoring High | | Fabricated emails to look complete | Never mark inferred as public; blank beats a guess | --- ## Combining with other skills - **`parallel-agent-fanout`** — run every discovery + contact-pull wave concurrently; this skill is the *what*, that skill is the *how at scale*. - **`deep-research`** — deeper multi-source verification of a specific high-value target. - **`company-legal-reputation-research`** — vet a shortlisted prospect before outreach. - **`domain-email-enumeration`** — upgrade `inferred-pattern` emails toward verified for the Tier-1 shortlist. - **`competitor-identification`** — seed Tier-1 source-code search with the right competitor embed strings. - **`intelligence-dossier`** — promote a top prospect into a full standing dossier. - **`linkedin-activity-intelligence`** / **`era-validated-linkedin-analysis`** — enrich the decision-maker contacts.