How do I install the Exa Lead Generation skill?

Run npx skillmds@latest add codealive-ai/exa-lead-generation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Exa Lead Generation safe to use?

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Which AI agents work with Exa Lead Generation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Exa Lead Generation free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Exa Lead Generation?

CodeAlive-AI (@codealive-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Exa Lead Generation by codealive-ai

Name: Exa Lead Generation
Author: CodeAlive-AI

Exa Lead Generation

Build prospect lists with Exa — find target companies matching an ICP, then their decision-makers, then enrich with signals. Use for outbound sales lists, account mapping, and B2B prospecting. Multi-pass orchestration over the Exa API; no MCP server required.

CodeAlive-AI 2d58e60 2 files · 4.1 KB Updated Sep 17, 2026

File contents

Lead Generation (Exa)

Turn an ICP into a prospect list: companies → decision-makers → enrichment. This is a multi-pass workflow built on exa-company-research and exa-people-research. Calls the Exa REST API through local scripts; no MCP server needed, only an EXA_API_KEY.

Setup (once)

export EXA_API_KEY=your-key            # or EXA_API_KEY=... in a .env at the repo root

Get a key at https://dashboard.exa.ai/api-keys. Shared details: exa-native-base.

The pipeline

Pass 1 — Source companies (this skill's script):

python scripts/lead_generation.py "Series A B2B SaaS companies in fintech" -n 20
python scripts/lead_generation.py "category:company developer tools startups hiring backend 2026" -n 20

Run 2-3 angles (category, stage, recent-launch) and dedupe to a clean company list.

Pass 2 — Find decision-makers at each company (use exa-people-research, batch 3-5 companies per subagent):

python ../exa-people-research/scripts/people_research.py "VP Engineering at <Company>" -n 5
python ../exa-people-research/scripts/people_research.py "Head of Sales at <Company>" -n 5

Pass 3 — Enrich (funding, hiring, news signals → timing):

python ../exa-company-research/scripts/company_research.py "<Company> funding round hiring news" -c news -n 5

Token isolation (essential here)

This is a fan-out task. One subagent per batch of seeds (3-5 companies each), each running Passes 2-3 and returning a compact table. Then compile, dedupe by company + person, and rank. Never run the whole enrichment loop in your main context. See exa-native-base for the orchestration pattern.

Qualifying & enriching rows

Define what makes a valid lead before searching (stage, geo, headcount, tech stack, buying signal).
Capture a consistent schema per row: company · website · why-it-fits · contact name · role · profile URL · signal (hiring/funding/launch) · source.
Apply hard filters first (stage, geo), then soft filters (genuine fit). See references/filtering.md in exa-native-base.

Finding hidden connections

For "who are X's customers / partners", direct queries return articles, not connections. Use indirect signals:

python ../exa-company-research/scripts/company_research.py "<Company> case study customer success story" -n 5
python ../exa-native-base/scripts/exa.py contents https://company.com/customers https://company.com/case-studies --text

After you get results

Deliver a deduped table (company · fit · contact · role · profile · signal · source), lead with the highest-signal accounts, and note coverage gaps. Treat results as similarity, not validation — verify before outreach.

---name: exa-lead-generationdescription: Build prospect lists with Exa — find target companies matching an ICP, then their decision-makers, then enrich with signals. Use for outbound sales lists, account mapping, and B2B prospecting. Multi-pass orchestration over the Exa API; no MCP server required.license: MIT---# Lead Generation (Exa)Turn an ICP into a prospect list: **companies → decision-makers → enrichment**. This is a multi-pass workflow built on `exa-company-research` and `exa-people-research`. Calls the Exa REST API through local scripts; **no MCP server needed**, only an `EXA_API_KEY`.## Setup (once)```bashexport EXA_API_KEY=your-key # or EXA_API_KEY=... in a .env at the repo root```Get a key at https://dashboard.exa.ai/api-keys. Shared details: [`exa-native-base`](../exa-native-base/SKILL.md).## The pipeline**Pass 1 — Source companies (this skill's script):**```bashpython scripts/lead_generation.py "Series A B2B SaaS companies in fintech" -n 20python scripts/lead_generation.py "category:company developer tools startups hiring backend 2026" -n 20```Run 2-3 angles (category, stage, recent-launch) and dedupe to a clean company list.**Pass 2 — Find decision-makers at each company** (use `exa-people-research`, batch 3-5 companies per subagent):```bashpython ../exa-people-research/scripts/people_research.py "VP Engineering at <Company>" -n 5python ../exa-people-research/scripts/people_research.py "Head of Sales at <Company>" -n 5```**Pass 3 — Enrich** (funding, hiring, news signals → timing):```bashpython ../exa-company-research/scripts/company_research.py "<Company> funding round hiring news" -c news -n 5```## Token isolation (essential here)This is a fan-out task. **One subagent per batch of seeds** (3-5 companies each), each running Passes 2-3 and returning a compact table. Then compile, dedupe by company + person, and rank. Never run the whole enrichment loop in your main context. See `exa-native-base` for the orchestration pattern.## Qualifying & enriching rows- Define what makes a valid lead *before* searching (stage, geo, headcount, tech stack, buying signal).- Capture a consistent schema per row: company · website · why-it-fits · contact name · role · profile URL · signal (hiring/funding/launch) · source.- Apply **hard filters first** (stage, geo), then **soft filters** (genuine fit). See `references/filtering.md` in `exa-native-base`.## Finding hidden connectionsFor "who are X's customers / partners", direct queries return articles, not connections. Use indirect signals:```bashpython ../exa-company-research/scripts/company_research.py "<Company> case study customer success story" -n 5python ../exa-native-base/scripts/exa.py contents https://company.com/customers https://company.com/case-studies --text```## After you get resultsDeliver a deduped table (company · fit · contact · role · profile · signal · source), lead with the highest-signal accounts, and note coverage gaps. Treat results as *similarity, not validation* — verify before outreach.

Frequently asked questions