prospecting skill

Prospecting

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 product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

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

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

Pull from product-marketing.md if available. Otherwise, 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; Clearbit or Clay 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.

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 — see Truelist integration in references/data-sources.md. 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.

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

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, Clearbit, Clay) 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.

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 — pull from 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.)

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.

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

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)

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 cold-email).
9. Forgetting to retain consent / lineage records. Required for GDPR DSARs and CAN-SPAM audits.