---

name: ai-sdr

#description: Find relevant companies and leads for B2B sales with ICP definition and qualification frameworks.

#AI SDR

Find relevant companies and leads for B2B sales. Define ideal customer profiles, identify target accounts, qualify prospects, and organize research for outreach.

#When to Use

• User wants to find companies that match their ICP
• User needs to build a prospect list for sales outreach
• User wants to research target accounts
• User needs lead qualification analysis

#When NOT to Use

• Writing outreach sequences (use sdr-outreach or cold-email-writer skills)
• Recruiting candidates (use ai-recruiter skill)
• General market research (use deep-research skill)

#Methodology

#Step 0: Interview the User About Their Business

Before doing any research, interview the user. Most users won't give you enough context unprompted — they'll say "find me customers" without explaining what they sell or who buys it. Use multi-option quizzes to make this fast and low-friction. Ask one question at a time.

Question flow:

1. What do you sell?

   • A) Software / SaaS
   • B) Physical product
   • C) Professional services / consulting
   • D) Marketplace / platform
   • E) Something else (describe)

2. Who buys it?

   • A) Other businesses (B2B)
   • B) Consumers (B2C / DTC)
   • C) Both

3. (If B2B) What size companies?

   • A) Startups / small teams (1-50 people)
   • B) Mid-market (50-500)
   • C) Enterprise (500+)
   • D) Not sure yet

4. What's your price point?

   • A) Under $100/mo
   • B) $100-$1,000/mo
   • C) $1,000-$10,000/mo
   • D) $10,000+/mo or custom pricing
   • E) One-time purchase

5. Who inside the company makes the buying decision?

   • A) Engineering / technical (CTO, VP Eng, developers)
   • B) Marketing (CMO, growth, content)
   • C) Sales / revenue (CRO, VP Sales, RevOps)
   • D) Operations / finance (COO, CFO)
   • E) Founder / CEO directly
   • F) Not sure

6. Do you have existing customers?

   • A) Yes, paying customers — I can describe who they are
   • B) A few early users / pilots
   • C) No customers yet

7. (If they have customers) What do your best customers have in common? (free text — this is the most valuable answer)

8. Any industries or verticals you're focused on? (free text or skip)

If the user provides a detailed prompt upfront, skip the questions they already answered. Don't re-ask what's obvious. But if key info is missing (who buys, what size, what price), ask before proceeding — the ICP will be wrong without it.

#Step 1: Define ICP (Ideal Customer Profile)

An ICP describes accounts where three things are true: they can buy (budget/size fit), they will buy (pain exists now), and they will stay (retention profile). Pick 6-10 attributes — more than 10 and nothing qualifies.

Attribute	How to define it	Example
Headcount	Hard range, not "SMB"	50-500 employees
Revenue	Estimate from headcount if private	$10M-$100M ARR
Industry	NAICS/SIC codes or named verticals	SaaS, fintech, digital health
Geography	Where you can legally sell + support	US, UK, Canada
Tech stack	Tools that signal fit (technographics)	Uses Salesforce + Segment + AWS
Funding stage	Proxy for budget + growth pressure	Series A-C, raised in last 18mo
Hiring signals	Job posts reveal priorities	Hiring "RevOps" or "Head of Data"
Negative signals	Disqualifiers — the sharpest filter	<20 employees, agency model, on-prem only

ICP vs Buyer Persona: ICP = which company. Persona = which human inside it. A Series B fintech (ICP) has a VP Eng who cares about velocity and a CFO who cares about cloud spend (two personas, different messaging).

#Step 2: Source Accounts

Free sources (use webSearch + webFetch):

• Crunchbase — site:crunchbase.com "series a" fintech 2025 for funding events
• LinkedIn — site:linkedin.com/company [industry] "11-50 employees" (headcount filter leaks into page text)
• BuiltWith / Wappalyzer lookups — webFetch a prospect's homepage, then scan source for tech signatures (Segment snippet, Intercom widget, Shopify checkout)
• Job boards — site:linkedin.com/jobs "[target company]" "data engineer" reveals what they're building; site:greenhouse.io and site:lever.co for startup hiring
• G2 / Capterra category pages — companies reviewing competitors are in-market
• GitHub orgs — public repos reveal tech stack and eng team size for dev-tool ICPs
• SEC EDGAR (public cos) — 10-K "Risk Factors" sections list the exact problems they're worried about

Paid sources the user likely has (shape output for these):

• Apollo (~210M contacts, $49+/mo) — best value for SMB/mid-market, filters on headcount growth + job postings + intent
• LinkedIn Sales Navigator (~1B profiles) — most accurate job-change data, but no email export
• ZoomInfo — strongest US enterprise coverage + intent data (tracks content consumption across the web)
• Clay ($134+/mo) — waterfall enrichment: chains Apollo → Hunter → Cognism to maximize match rate. Best for teams with RevOps capacity.
• Cognism — best EU/UK data + phone-verified mobiles (GDPR-compliant)

#Step 3: Buying Signals (Trigger Events)

Prospects with an active trigger convert 3-5x higher. Rank by signal strength:

Signal	Why it matters	How to find it
New exec in target persona	New VPs buy tools in first 90 days	site:linkedin.com "[company]" "I'm excited to join" or Sales Nav job-change alerts
Funding round	Budget just unlocked	Crunchbase, webSearch: "[company] raises"
Hiring spike in relevant role	Building the team that needs you	LinkedIn Jobs count, site:greenhouse.io/[company]
Tech stack change	Migration = pain = budget	BuiltWith historical, job posts mentioning "migrating from X"
Competitor displacement	Negative G2 review of competitor	site:g2.com "[competitor]" 1-star OR 2-star
M&A / new product launch	Org chaos creates tool gaps	Press releases, TechCrunch
Earnings call mentions	Public co priority signals	webFetch SeekingAlpha transcripts, Ctrl-F for your problem space

#Step 4: Qualify & Tier

Fast disqualification (do this first): Before researching, kill accounts that fail any hard constraint — wrong geo, below headcount floor, competitor customer under contract, recent layoffs (no budget).

Qualification frameworks:

• BANT (Budget / Authority / Need / Timeline) — fine for transactional/SMB
• MEDDPICC (Metrics / Economic buyer / Decision criteria / Decision process / Paper process / Identify pain / Champion / Competition) — use for enterprise deals >$50k. The extra P's (paper process, competition) matter because enterprise deals die in legal/procurement, not in the pitch.

Tiering:

• Tier 1 — ICP match + active trigger in last 30 days → full personalization, multi-channel, SDR owns it
• Tier 2 — ICP match, no trigger → lighter-touch automated sequence, monitor for triggers
• Tier 3 — Partial fit → newsletter/nurture, revisit quarterly

#Step 5: Scale with Parallel Agents

Target minimum 40 prospects. A single sequential search won't get there fast enough. Use startAsyncSubagent to run 5 parallel research agents, each focused on a different search angle:

1. Industry/vertical search — companies in the target vertical via Crunchbase, G2 category pages
2. Funding/growth search — recently funded companies matching the ICP
3. Hiring signal search — companies hiring for roles that indicate they need the user's product
4. Competitor customer search — companies using competitors or reviewing them on G2/Capterra
5. Lookalike search — competitors and alternatives to any strong-fit companies already found

Each agent should return 10-15 prospects with all columns filled in. Use the wait_for_background_tasks tool to wait for them, then deduplicate and merge into the final spreadsheet.

#Step 6: Output as a Spreadsheet

Build a real spreadsheet using the excel-generator skill or write a CSV file — not a markdown table. The output should be something the user can import directly into a CRM, Clay, or Apollo.

Columns:

| Company | Domain | Headcount | Fit Score (1-5) | Trigger | Trigger Date | Target Contact Name | Target Title | LinkedIn URL | Email (if found) | Why Now (1 sentence) |

• LinkedIn URL — search for the target persona at the company: site:linkedin.com/in "[company]" "[title]". Include direct profile links.
• Email — look for email patterns via webSearch("[company] email format" OR "[name] [company] email"). Common patterns: first@company.com, first.last@company.com. If not found, leave blank and note the likely format.
• Why Now — the most valuable column. It's the first line of the cold email.

The spreadsheet should be downloadable and ready to import — not just displayed as text.

#Deep Research for Complex ICPs

For industries or markets you don't know well, pull in the deep-research skill to build context before prospecting. This is especially useful when:

• The user sells into a niche vertical you don't have strong priors on (e.g., "construction tech", "veterinary SaaS")
• You need to understand market landscape, key players, and buyer behavior before defining the ICP
• The user wants competitor analysis as part of the prospecting process

Use deep-research to gather industry context, then return here to build the prospect list with that knowledge.

#Agent Tactics

Tech stack detection: Use webSearch("[company] tech stack" OR "[company] built with") to find BuiltWith/Wappalyzer/StackShare profiles. Common signatures to look for in search results:

• Segment, Intercom, Stripe, Shopify, Google Analytics
• Job postings often reveal stack: webSearch("[company] careers engineering")

Note: webFetch returns markdown content, not raw HTML — script tags and asset URLs are stripped. Use search-based detection rather than HTML source scanning.

Waterfall search pattern: If one query returns nothing, don't stop — try synonym variants. "VP Engineering" OR "Head of Engineering" OR "Engineering Lead" OR "CTO" all map to the same persona at different company sizes.

Lookalike expansion: Once you find 5 good-fit accounts, search for their direct competitors — webSearch: "[good-fit company] vs" or "[good-fit company] alternatives" surfaces the category.

#Best Practices

1. 50 researched > 500 sprayed — reply rates on researched lists run 3-5x higher
2. Disqualify before you qualify — negative filters are cheaper to check
3. Trigger freshness decays fast — a funding round is a 60-day window, a job change is a 90-day window
4. Enrich once, cache the result — don't re-research the same account every sequence
5. B2B data decays ~30%/year — any list older than 6 months needs re-verification

#Limitations

• Cannot log into LinkedIn Sales Navigator, Apollo, ZoomInfo, or Clay — builds search strategies the user executes
• Cannot verify email deliverability (user should run through NeverBounce/ZeroBounce before sending)
• Cannot detect intent data (Bombora/6sense-style content consumption signals require paid platforms)
• Company headcount/revenue estimates from public web are approximate — private company data is inherently fuzzy