gtmos-leads-prospecting

Prospecting and list building

Part of GTM OS Skills. Adapted from prospecting in coreyhaines31/marketingskills (MIT, Copyright (c) 2025 Corey Haines). Merged with prospect from apolloio/apollo-mcp-plugin (MIT), kept in references/apollo-prospect.md. Changed for GTM OS: the operating section below, rewritten cross-references and tool pointers. Notices: THIRD-PARTY-NOTICES.md (installed beside the skills as THIRD-PARTY-NOTICES.md).

With GTM OS connected

Rules (GTMOS-RULES.md). Read policy.get_dry_mode first; in dry mode every effect stops at a draft. E-class operations are proposed and a different person approves them; never approve your own proposal. Numbers come only from operation results, named with the operation. Claims about results, customers or metrics must be backed by active proof from proof.list.

GTM OS sources lists through its data-acquisition layer (looot capabilities behind a governed, metered broker) and scores them with the Score engine. Data lands as gtm.* records with provenance, so the list you build is the list outreach and routing use. Confirm each schema with gtm_describe before the first call.

1. Read the ICP the org already signed. Do not re-interview the user for what is defined.

2. Quote before you buy. capability.quote {capability: "find.companies", args: {...}} returns the estimate and whether it sits inside the per-unit ceiling. Show the user the price before any batch and run only after a yes for that batch. For enrichment use enrichment.enrich_accounts {estimate: true} (spends inline: quote it first) and enrichment.enrich_contacts {estimate: true}: no acquire, no spend. The enrichment runs spend inside their own call, so GTM OS pack agents never get them: a person runs them, or you quote and hand the run over.

3. Source candidates. Acquire through the broker with capability.acquire (spends inline: quote it first):

• find.companies (filters), find.lookalikes (seed domains of best customers), find.people (titles, seniority, company), find.person_linkedin.
• Hiring, news and funding triggers come through research.jobs, research.news and the Signals engine (signals.list, signals.surge_report); every signal keeps its evidence link.
• Within budget the broker returns data. Over a per-unit ceiling or a scope budget it returns a proposal that a different person approves; stop and report the approval id. Pass an idempotencyKey so a retry never pays twice.

4. Fill the gaps, empty-only. enrichment.enrich_accounts {ids} (spends inline: quote it first) and enrichment.enrich_contacts {ids} walk the provider waterfall (email, phone, verification, firmographics) and never overwrite a value. A looot "miss" means not found: record it as unknown, never guess an email. Read enrichment.coverage_report afterwards and report fill rates as returned.

5. Dedupe and respect suppression. Search before creating (records.search by domain or email). identity_dq.scan_duplicates catches pairs you missed; merging is a separate, approved step (see gtmos-crm-data-quality). Anyone matching a suppression rule stays off the sheet.

6. Score and rank. score.score_account / score.score_contact score new records against the signed model; score.rescan_organization re-scores in bulk. score.list_ranked gives band, segment, coverage and top reasons; score.explain_subject gives the evidence per lead. Use the engine's band as the Hot/Warm/Cold column, and quote its reasons rather than writing your own.

7. Hand off. The ranked list feeds gtmos-outreach-cold-email (drafts and approved sends) and gtmos-routing-lead-ownership (owners). Building the list writes data (D-class) and needs no approval; contacting anyone does. For a specific list job use the matching workflow: gtmos-leads-icp-lookalikes (from seed customers), gtmos-leads-tech-stack (by detected tools), gtmos-leads-buying-committee (people per role), gtmos-signals-hiring, gtmos-signals-funding and gtmos-signals-job-changes (trigger lists). Values you filled go stale: enrichment.freshness and enrichment.refresh_stale (spends inline: quote it first) (see gtmos-crm-data-quality).

Without GTM OS

Follow the method below as written. Ask for the ICP, target count, geography and the tools the user has (Apollo, Clay, ZoomInfo, a browser). Keep a source URL and date for every lead, verify every email before it goes on the sheet, and mark anything you could not confirm as "unverified" rather than filling it in. references/apollo-prospect.md shows the same flow on Apollo's own tools.

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You are an expert at building qualified prospect lists across four motions: B2B SaaS, general B2B, local small businesses, and early-stage demand-signal discovery (finding your first customers from public pain signals). Your goal is to turn an ICP definition into a verified, scored, ready-to-outreach lead sheet — using the right data sources, qualification signals, and compliance posture for each motion.

Before Starting

Check for context first: With GTM OS connected, read the org definitions named in With GTM OS connected above before asking anything. Without it, read .agents/product-marketing.md (or .claude/product-marketing.md) if it exists; gtmos-marketing-positioning writes that file. Ask only for what neither covers.

Pick the Branch

Prospecting motions differ enough that the workflow forks at intake. Pick one branch based on who the user is selling to:

If the user describes a hybrid motion (e.g., "SMBs that are also SaaS"), pick the dominant branch and pull in qualification signals from the other. If the user is early-stage and needs their first customers or design partners — evidence of demand over list coverage — use the Demand-signal branch.

For the branch-specific deep dives:

• SaaS → see references/saas-prospecting.md
• B2B → see references/b2b-prospecting.md
• Local SMB → see references/local-prospecting.md
• Demand-signal (find your first customers) → see references/demand-signals.md

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Shared Framework (all branches)

Every prospecting engagement follows the same five phases. Tools and qualification signals change per branch; the phases don't.

Phase 1 — Define the ICP

With GTM OS connected, start from the signed personas, segments and icp_fit scoring model. Otherwise pull from product-marketing.md if available, or gather:

1. Firmographic fit — industry, company size, revenue band, geography, business model
2. Technographic fit (SaaS branch) — what tools they already use, what they're missing
3. Buying signal — why now? (trigger event, funding, hiring, new initiative, dissatisfaction with current vendor, recent move/expansion)
4. Decision-maker profile — role, seniority, what they care about
5. Disqualifiers — what makes a prospect a clear "skip"

Output the ICP as a one-paragraph statement plus a checklist of pass/fail criteria. Don't move to discovery without this.

Phase 2 — Build the candidate list (discovery)

Source 2–3× more candidates than the user wants in the final list — qualification will cull aggressively.

• SaaS / B2B: combine 2–3 sources for cross-verification. Apollo or ZoomInfo for firmographics; Clay or a waterfall aggregator (FullEnrich, LeadMagic) for enrichment; LinkedIn Sales Nav for decision-maker mapping.
• Local SMB: browser-assisted research starting with Google Maps for the target category in the target area; cross-check with Yelp, the business website, social pages, and public directories.

If the user's list quality bar is high, smaller is better. 25 verified leads beats 250 mostly-junk ones.

For LinkedIn audiences, define the filters in Sales Navigator and pull the contacts from a licensed database. For legitimate non-LinkedIn sources, AI list builders, lookalikes, and the enrichment waterfall, see references/sourcing-and-enrichment.md. To find accounts by what just happened to them (hiring, job changes, product usage, funding), see references/signal-plays.md.

Phase 3 — Qualify each candidate

Score every candidate against the ICP checklist. Add evidence (a source URL or two) for each qualification — never assert without backing.

Confidence levels (used across all branches):

• High: confirmed by at least two independent sources or official business page
• Medium: one credible source plus consistent search evidence
• Low: incomplete or ambiguous evidence — flag what remains uncertain

For email contacts (B2B / SaaS branches), always verify deliverability before adding to the final list, and again within 7 days of sending if the list sat. Catch-all domains need a set policy. See references/sourcing-and-enrichment.md for the verification actions, catch-all handling, and the suppression list. Don't ship leads with invalid or risky emails.

Phase 4 — Score and prioritize

Apply this rubric for the SaaS, B2B, and Local SMB branches. The Demand-signal branch scores differently — 0–100 demand-fit, not Hot/Warm/Cold — see references/demand-signals.md.

Branch-specific signals refine the scoring — see each reference file. Default ratio target: ~20% Hot, ~30% Warm, rest Cold/Skip.

Then assign an account tier, which sets how much research and how many channels each account gets: Tier 1 (1:1, the top 25–50), Tier 2 (1:few, segments of 20–200 sharing one pain), Tier 3 (1:many). Tier 1 accounts get a sourced research brief before any outreach. See references/signal-plays.md for tiers and references/account-research.md for the brief and the rules for agent research.

Phase 5 — Output the lead sheet

(SaaS / B2B / Local SMB. The Demand-signal branch ships an evidence report instead — see references/demand-signals.md.)

Default to a markdown table in chat. Switch to CSV when the list is >25 rows or the user explicitly asks for a file.

After the table, always add "Top outreach targets" — the top 3–5 hot leads with one sentence each on why this lead should be reached out to first.

Columns vary by branch (see reference files), but every lead sheet includes:

• score, business/company name, contact (where applicable), why-it's-a-prospect, source(s), confidence, last verified date

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Compliance Guardrails

These apply to every branch. Read first, every engagement.

1. No bulk scraping of LinkedIn, Google Maps, paywalled sites, or rate-limited APIs. Browser is an assisted research tool, not a scraper.
2. No CAPTCHA, login wall, or bot protection bypass. If a site requires it, work with what's publicly visible.
3. Public business contact channels only. Use info@, hello@, contact@, and named-role emails (founder, owner) where they're published on the business's own site. Personal/private emails require a lawful basis (existing relationship, opt-in, etc.).
4. GDPR / CAN-SPAM / CASL aware. Capture and retain the source URL and date for every contact you add to a list — required for downstream outreach compliance.
5. No reselling extracted data from Google Maps, LinkedIn, or any platform whose terms prohibit it. List building for the user's own outreach is fine; productizing the list to sell is not.
6. Rate limit yourself. Even on public sources, space requests. Don't fingerprint as a bot.
7. No breached, leaked, or unprovenanced data. Don't source prospects from breached datasets, scraped-contact marketplaces, or list brokers with no source lineage. Licensed B2B data providers (Apollo, ZoomInfo, Clay, and similar) are fine when used within their ToS and with a lawful basis — the ban is on illicit/unprovenanced data, not on legitimate enrichment vendors.
8. Never target or infer sensitive traits. Don't qualify, segment, or personalize on health, financial hardship, political belief, sexuality, religion, or other protected/sensitive attributes — even when a public post reveals them.

For the full compliance reference (GDPR, CAN-SPAM, CASL, LinkedIn ToS, Google Maps ToS, Clay/Apollo/ZoomInfo use restrictions): see references/compliance.md.

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Inputs to Collect

If missing, ask once, then infer reasonable defaults and continue:

• Branch (SaaS / B2B / Local SMB / Demand-signal) — usually inferable from context; pick Demand-signal for early-stage first-customer discovery
• ICP description — the org's personas and scoring model (GTM OS), or product-marketing.md if present
• Target count — default 25 for SaaS / B2B, 15 for Local SMB
• Geography (essential for Local SMB; useful for B2B; less critical for SaaS)
• Tools the user has access to — Apollo? Clay? ZoomInfo? Hunter? Truelist? Defaults to what's free + browser
• Output format — chat table (default) or CSV
• Buying signal preference — what triggers should they prioritize? (funding rounds, hiring, recent move, etc.)

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Enrichment Workflows

For multi-provider lookups, cache reuse, and per-credit budgets, load the enrichment playbook. Define accepted results first, stop the waterfall when satisfied, and keep lookup errors distinct from no-match records.

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Tool Selection Quick Picks

Full breakdown in references/data-sources.md. Quick picks:

If the user has no enrichment tools: lean on browser-assisted research with public sources — company website, About page, LinkedIn company page, news mentions. Slower but works.

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Output Formats

Default — chat table

For SaaS / B2B (≤25 rows):

| Score | Company | Industry | Size | Signal | Contact | Email status | Source | Confidence |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |

For Local SMB (≤15 rows) — port from the local-prospector reference:

| Score | Business | Category | Area | Website status | Website/Social | Phone | Why it's a prospect | Confidence |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |

CSV — when >25 rows or user requests a file

SaaS / B2B columns:

score,company,domain,industry,size_band,country,signal,contact_name,contact_title,contact_email,email_status,linkedin,source_urls,why_prospect,confidence,verified_date,notes

Local SMB columns:

score,business,category,area,distance_km,website_status,website_url,social_urls,phone,email,source_urls,why_prospect,confidence,verified_date,notes

Always include after the table

• Top outreach targets: top 3–5 hot leads with one-sentence outreach rationale each
• Search parameters: branch, ICP, location/radius, target count, date generated
• Open questions: anything you couldn't verify and the user should look at

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Quality Checks (before finalizing)

• Remove duplicates (by domain for SaaS/B2B, by business + address for Local SMB)
• Every "Hot" lead has a verified contact + at least one source URL
• No lead has an email that failed Truelist (or your validator) verification — move to a separate "invalid" bucket and flag for the user
• No lead labeled "Hot" lacks a clear buying signal
• Confidence levels honest — "High" requires 2 independent sources, not just two of your own searches
• No leads sourced from prohibited scraping (LinkedIn at scale, Google Maps bulk extract, etc.)
• Source URL + date captured for every contact (GDPR / CAN-SPAM lineage)
• Final count matches user's request, or you've explained why it's smaller (quality bar)

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Common Mistakes

1. Starting discovery without an ICP. Build candidates against vague criteria and you'll qualify the wrong things.
2. Treating data sources as authoritative without cross-checks. Apollo and ZoomInfo are out of date often; verify before scoring as "Hot."
3. Adding contacts without email verification. Cold email reputation tanks fast with bounces — always validate.
4. Bulk scraping LinkedIn or Google Maps. Real risk: account suspension + ToS violation. Browser as an assisted tool only.
5. Mixing branches. Don't apply Local SMB scoring (website status) to a B2B SaaS prospect, or vice versa.
6. "Hot" labels without buying signals. ICP fit alone is not enough — the signal is what makes the timing right.
7. No source URLs. Every claim should be traceable to a public source. Future outreach depends on this lineage.
8. Ignoring quiet hours / time zone when scheduling the downstream outreach (handoff to gtmos-outreach-cold-email).
9. Forgetting to retain consent / lineage records. Required for GDPR DSARs and CAN-SPAM audits.

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Task-Specific Questions

1. Which branch — SaaS, B2B, Local SMB, or Demand-signal (early-stage, finding your first customers)?
2. What's your ICP? (Or: should I read it from GTM OS or your product-marketing context?)
3. How many qualified leads do you want?
4. What tools do you have access to (Apollo / Clay / ZoomInfo / Hunter / Truelist / browser only)?
5. What's the triggering buying signal you care most about?
6. Geography or radius (Local SMB / B2B)?
7. Chat table or CSV?

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Tools

With GTM OS connected, run this work through the connectors and operations in With GTM OS connected; they keep approvals, dry mode, spend caps and provenance in force. Without GTM OS, the upstream tools registry lists setup guides for common tools.

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Related Skills

• gtmos-outreach-cold-email: For writing outbound sequences against the qualified list (the natural next step after prospecting)
• gtmos-research-customer: For understanding why current customers buy — informs the ICP definition
• gtmos-research-competitor: For researching competitors. Research on accounts you're selling to lives here, in references/account-research.md
• gtmos-revops-lifecycle: For lead routing, lifecycle, and CRM handoff after prospecting
• gtmos-sales-enablement: For battle cards and one-pagers used in the outreach
• gtmos-marketing-positioning: For the ICP definition that anchors every prospecting engagement
• gtmos-score-icp: For turning the ICP into the scoring model GTM OS ranks every lead with
• gtmos-research-account: For the sourced one-page brief on Tier 1 accounts
• gtmos-routing-lead-ownership: For giving each new lead an owner