Prospecting
Corey HainesCommunityCreated by Corey Haines
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This skill provides a systematic approach to building lead lists by defining an Ideal Customer Profile (ICP) and applying branch-specific discovery methods. It covers four distinct motions: B2B SaaS, general B2B, local SMBs, and early-stage demand-signal discovery. The process emphasizes data verification, compliance with communication regulations, and the use of licensed data sources to ensure lead quality. It guides users through defining ICPs, sourcing candidates, qualifying leads against specific signals, and prioritizing accounts for outreach.
What it does
- Defines ICPs based on firmographic, technographic, and behavioral signals.
- Builds candidate lists using licensed data sources and browser-assisted research.
- Verifies email deliverability to maintain outreach reputation.
- Scores and tiers prospects based on buying signals and ICP fit.
- Generates lead sheets in markdown tables or CSV format.
- Ensures compliance with GDPR, CAN-SPAM, and platform terms of service.
When to use it
- When you need to build a targeted list of prospects for outbound outreach.
- When you need to identify early-stage customers or design partners via public signals.
- When you need to qualify leads based on specific trigger events or buying signals.
- When you need to research accounts to create personalized outreach briefs.
Advantages
- Provides a consistent, repeatable framework for lead generation.
- Reduces bounce rates through mandatory email verification.
- Maintains compliance by tracking source lineage for every contact.
- Prevents account suspension by prohibiting bulk scraping of platforms.
Usage examples
- “Build a list of 25 B2B SaaS companies that recently raised a Series A and are hiring for growth roles.”
- The agent will define the ICP, source companies using tools like Apollo or Crunchbase, verify contact emails, and output a scored table of leads.
- “Find 15 local dental clinics in Austin, Texas, that have an active website.”
- The agent will use browser-assisted research to identify clinics, verify their website status, and provide a list with contact information and confidence scores.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/coreyhaines31/prospecting/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip prospecting.zip -d .agents/skills
Documentation by Corey Haines
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:
| Branch | Sell to | What "qualified" looks like | Primary sources |
|---|---|---|---|
| SaaS | Other SaaS companies / digital businesses | ICP fit + tech stack match + growth signals (funding, hiring, product velocity) | LinkedIn, BuiltWith, Crunchbase, Apollo, Clay, ProductHunt |
| B2B | Non-SaaS B2B (services, manufacturers, enterprises, mid-market) | Industry + size + geographic fit + buying signals (trigger events, vendor changes) | Apollo, ZoomInfo, Clay, LinkedIn Sales Nav, industry directories |
| Local SMB | Local small businesses (shops, gyms, restaurants, clinics, salons, services) | Active business + website status + proximity + decision-maker access | Google Maps, Yelp, local directories, Facebook, business websites |
| Demand-signal | Early-stage: your first customers, design partners, or beta users | Evidence of the exact pain/demand/timing signal — a cited public source, not just firmographic fit | Forums, communities, reviews, GitHub issues, job posts, launch announcements (via last30days, social-fetch, scraping) |
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:
Firmographic fit — industry, company size, revenue band, geography, business model
Technographic fit (SaaS branch) — what tools they already use, what they're missing
Buying signal — why now? (trigger event, funding, hiring, new initiative, dissatisfaction with current vendor, recent move/expansion)
Decision-maker profile — role, seniority, what they care about
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), gap plays (a signal plus a gap you can verify from outside, such as ads running with no visible tracking), or warm paths through your own network, 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.
| Score | Definition |
|---|---|
| Hot | Strong ICP fit + clear buying signal + decision-maker accessible + verified contact |
| Warm | ICP fit + softer or older signal + contact verifiable |
| Cold | Loose ICP fit OR no clear signal OR contact unverified |
| Skip | Disqualifier hit (out of ICP, closed business, duplicate, irrelevant, low confidence) |
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
Compliance Guardrails
These apply to every branch. Read first, every engagement.
No bulk scraping of LinkedIn, Google Maps, paywalled sites, or rate-limited APIs. Browser is an assisted research tool, not a scraper.
No CAPTCHA, login wall, or bot protection bypass. If a site requires it, work with what's publicly visible.
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.).
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.
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.
Rate limit yourself. Even on public sources, space requests. Don't fingerprint as a bot.
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.
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.mdif presentTarget 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.)
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.
Tool Selection Quick Picks
Full breakdown in references/data-sources.md. Quick picks:
| If the user has access to... | Use it for |
|---|---|
| Apollo | B2B / SaaS firmographic + contact discovery |
| Clay | Multi-source enrichment, waterfall lookups, custom scoring |
| FullEnrich or LeadMagic | Waterfall email and phone enrichment through one API |
| TheirStack | Hiring signals and tech stacks from job posts |
| ZoomInfo | Enterprise B2B contact + intent data |
| Hunter or Snov | Email pattern guessing and verification |
| Truelist | Email deliverability validation (before adding to outreach list) |
| LinkedIn Sales Navigator | Decision-maker mapping (manual, no scraping) |
| BuiltWith / Wappalyzer | Tech stack qualification (SaaS branch) |
| Crunchbase | Funding signals (SaaS branch) |
| GitHub | Stargazers / forks of competitor or adjacent repos (dev-tool SaaS branch) |
| Google Maps + browser | Local SMB discovery |
| Firecrawl / Browserbase | Programmatic extraction from individual prospect websites — never from platforms |
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,notesLocal SMB columns:
score,business,category,area,distance_km,website_status,website_url,social_urls,phone,email,source_urls,why_prospect,confidence,verified_date,notesAlways 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
Starting discovery without an ICP. Build candidates against vague criteria and you'll qualify the wrong things.
Treating data sources as authoritative without cross-checks. Apollo and ZoomInfo are out of date often; verify before scoring as "Hot."
Adding contacts without email verification. Cold email reputation tanks fast with bounces — always validate.
Bulk scraping LinkedIn or Google Maps. Real risk: account suspension + ToS violation. Browser as an assisted tool only.
Mixing branches. Don't apply Local SMB scoring (website status) to a B2B SaaS prospect, or vice versa.
"Hot" labels without buying signals. ICP fit alone is not enough — the signal is what makes the timing right.
No source URLs. Every claim should be traceable to a public source. Future outreach depends on this lineage.
Ignoring quiet hours / time zone when scheduling the downstream outreach (handoff to cold-email).
Forgetting to retain consent / lineage records. Required for GDPR DSARs and CAN-SPAM audits.
Task-Specific Questions
Which branch — SaaS, B2B, Local SMB, or Demand-signal (early-stage, finding your first customers)?
What's your ICP? (Or: should I pull from your product-marketing context?)
How many qualified leads do you want?
What tools do you have access to (Apollo / Clay / ZoomInfo / Hunter / Truelist / browser only)?
What's the triggering buying signal you care most about?
Geography or radius (Local SMB / B2B)?
Chat table or CSV?
Tool Integrations
For implementation, see the tools registry. Key prospecting tools:
| Tool | Best For | MCP | Guide |
|---|---|---|---|
| Apollo | B2B / SaaS firmographic + contact discovery | ✓ | apollo.md |
| Clay | Multi-source enrichment + waterfall | ✓ | clay.md |
| FullEnrich | Waterfall email + phone enrichment | ✓ | fullenrich.md |
| LeadMagic | Email, phone, and job-change enrichment | ✓ | leadmagic.md |
| TheirStack | Hiring signals and tech stacks from job posts | ✓ | theirstack.md |
| Apify | Structured data from public directories (never LinkedIn) | ✓ | apify.md |
| ZoomInfo | Enterprise B2B contact + intent | ✓ | zoominfo.md |
| Hunter | Email pattern + verification | ✓ | hunter.md |
| Snov | Email finder + verifier | - | snov.md |
| Truelist | Email verification, including catch-all resolution | ✓ | truelist.md |
| Outreach | Sales engagement (post-list) | ✓ | outreach.md |
| RB2B | Visitor identification (warm intent) | - | rb2b.md |
| Ploy ◆ | Visitor identification on Ploy-hosted sites (one option alongside RB2B and other visitor-ID tools) | - | ploy.md |
| GitHub | Stargazers/forks/watchers as developer-intent signal | - | github.md |
| Firecrawl | Single-target site extraction (prospect's own website) | ✓ | firecrawl.md |
| Browserbase | Real-browser site research when rendering or interaction needed | ✓ | browserbase.md |
Related Skills
cold-email: For writing outbound sequences against the qualified list (the natural next step after prospecting)
customer-research: For understanding why current customers buy — informs the ICP definition
competitor-profiling: For researching competitors. Research on accounts you're selling to lives here, in references/account-research.md
revops: For lead routing, lifecycle, and CRM handoff after prospecting
sales-enablement: For battle cards and one-pagers used in the outreach
directory-submissions: For inbound discovery surfaces (the prospects might find you back)
product-marketing: For the ICP definition that anchors every prospecting engagement
Files
- ATTRIBUTION.mdAdded by AutoGineer883 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md19.9 KiB
- evals/evals.json17.9 KiB
- references/account-research.md3.7 KiB
- references/b2b-prospecting.md4.9 KiB
- references/compliance.md10.8 KiB
- references/data-sources.md13.1 KiB
- references/demand-signals.md9.9 KiB
- references/enrichment-playbook.md5.2 KiB
- references/local-prospecting.md8.0 KiB
- references/saas-prospecting.md5.9 KiB
- references/signal-plays.md13.6 KiB
- references/sourcing-and-enrichment.md6.6 KiB
references/sourcing-and-enrichment.md
Sourcing, Enrichment, and Verification
How to get contacts legitimately, fill in missing data without paying twice or trusting bad data, and verify every address before it reaches a sequence.
Sourcing without scraping LinkedIn
LinkedIn is where the people are, and scraping it is where the lawsuits are (see compliance.md). The workable pattern:
Define the audience in Sales Navigator (or LinkedIn search): titles, seniority, company size, industry, geography, recent job changes, growth. This is research, which is fine.
Pull the contacts from a licensed database using the same filters: Apollo, ZoomInfo, Clay's providers, Prospeo, and similar. They're licensed to provide the data and carry the provenance.
Use LinkedIn by hand for Tier 1 accounts: confirm the person still holds the role, read their recent posts, decide whether to connect.
Other sources that don't involve LinkedIn:
| Source | Good for | Notes |
|---|---|---|
| Job boards via TheirStack or PredictLeads | Companies hiring in your function | Also reveals their tools |
| Directories and marketplaces | Niche lists: app store developers, agency directories, partner listings, software review categories | Check each site's terms; prefer official APIs or exports |
| Competitor customer pages | Logos and case studies | Public, but use them as accounts, not as a pitch ("I see you use X") |
| Google Places API | Local and SMB lists | The compliant programmatic route; see compliance.md |
| GitHub | Developer tools | See the SaaS branch reference |
| Podcast guests, conference speakers | Leaders who talk publicly | Ready-made research material too |
| AI list builders (Exa Websets, Parallel FindAll) | Niche lists from a plain-English description ("Series A fintechs that added a pricing page this year") | Precise but incomplete. In one 2026 evaluation the best tool found about a fifth of possible matches, at high precision. Use them for depth, not coverage |
| Apify actors | Structured data from sites without an API | Pick actors for public sites whose terms allow it; never for LinkedIn |
| Lookalikes | "More companies like our best customers" | Exa find-similar, Ocean.io, or seed-based search in your database |
Enrichment: run providers in order
No single provider finds everyone, and the ones that find the most also return more wrong data. The standard approach is a waterfall: query providers one at a time and stop at the first confident result.
Order providers by accuracy for your ICP, then by coverage. Independent comparisons disagree wildly. Different 2025–26 benchmarks put the same tools anywhere from 25% to 87% find rates, and the highest finders sometimes had four times the false-positive rate. Run your own bake-off (below).
Stop at the first result that passes verification. Paying three providers for the same contact is the most common waste.
Set a cost cap per contact and log which provider filled each field.
Mobile numbers are for Tier 1 only. They're the most expensive field, and many are personal phones (see the phone rules in compliance.md). For EU and UK numbers, providers with human-verified European data tend to do better.
Keep the enrichment result in your data, not just the sending tool, so you can re-verify later and answer data subject requests.
For acceptance rules per field, cache keys, and how to treat errors versus genuine no-matches, see enrichment-playbook.md. Clay is the common way to orchestrate a waterfall across many providers. FullEnrich and similar aggregators do it as a single API call. Building it yourself means calling each provider's API in sequence.
Provider bake-off
Before committing to a provider or an order, test on your own ICP:
Take 200 contacts you already know are correct (customers, recent conversations).
Strip the emails and phones, then run each candidate provider.
Score each: found, correct, wrong (a confident answer that was false), cost per correct contact.
Order the waterfall by fewest wrong answers first, then by cost per correct contact.
Repeat each quarter, because providers' data quality shifts.
Verification
Verify every address before it reaches a sequence. This is the single cheapest protection for sender reputation.
When to verify
When you enrich the contact.
Again within 7 days of sending, for any list built earlier. B2B data decays steadily (vendor estimates run around 2% a month and 20–30% a year).
Re-verify CRM contacts every quarter, and anything older than 30–60 days before using it.
What to do with each result
| Result | Action |
|---|---|
| Valid | Send |
| Invalid, disposable, or spam trap | Suppress permanently |
| Role address (info@, sales@) | Exclude from personal sequences. Fine only for local businesses that publish it as their main contact |
| Catch-all (accept-all) | See below |
| Unknown | Retry once later; don't send if it's still unknown |
Catch-all domains accept mail for any address, so basic verification can't tell whether the person exists. They make up a large share of B2B domains (vendor estimates run 15–28%). Handle them with a policy, not a guess:
Resolve them with a verifier that goes beyond the SMTP check (BounceBan, Scrubby, Truelist, and others combine extra signals). Some verifiers only mark catch-alls "risky."
Send resolved-valid catch-alls from separate, lower-volume inboxes, so a bad batch can't damage your main sending.
Send unresolved catch-alls only for Tier 1 accounts, where the account is worth the risk, and watch bounces on that inbox closely.
Hard limits: keep bounces under 2% per campaign (under 1% is best in class) and pause automatically above that. See the cold-email skill's deliverability reference.
Suppression list
Keep one suppression list, synced to every tool that sends (email tool, LinkedIn tool, dialer, CRM), and check it before every send:
Unsubscribes and opt-outs from any channel
Hard bounces and invalid addresses
Current customers and open deals (outbound to them is a sales-team problem)
Competitors and partners
People who replied "not interested," for the cooldown period you choose
Do-not-contact requests and data deletion requests
Key it on email, LinkedIn URL, and phone number so a person removed in one channel is removed in all of them.
Provenance
Every contact carries: the source (provider or URL), the collection date, the lawful basis for EU/UK contacts, and the last verification date and result. This answers "where did you get my email?" and makes data deletion requests possible.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
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Attribution and licence
- Created by
- Corey Haines
- Publisher
- Corey Haines (Community)
- Source
- coreyhaines31/marketingskills · skills/prospecting · commit 96798cf
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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