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Build prospect lists with Exa — find target companies matching an ICP, then their decision-makers, then enrich with signals.
Passed 14 of 18 test cases with the skill, and 11 of 18 without it.
and 2 more in the full report
5 fixed12 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to scrape raw text from a target company's case study page at https://example.com/customers. Which helper script from the exa-native-base module should be called with the --text flag?
| Checked | Without skill | With skill |
|---|---|---|
| Executes python ../exa-native-base/scripts/exa.py with contents and --text. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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.
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
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.
references/filtering.md in exa-native-base.test cases passed, vs 11 / 18 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.