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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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
General-purpose web search powered by Exa — the search_web / web_search analog. Use for any open-ended lookup, fact-finding, or 'search the web for X' when no specialized skill fits. Semantic (meaning-based) search over the whole index; runs a local script against the Exa API, no MCP server required.
Foundation for the Exa skill collection — how to call Exa (search, contents, answer, similar) from a local script with just an EXA_API_KEY, how to write good semantic queries, and how to orchestrate subagents for deep research. Read this first; the use-case skills (company, people, papers, code, news, finance, personal sites, X) build on it.
Find code with Exa — API usage examples, error fixes, library docs, and GitHub repositories. Use when you need a working code snippet, to resolve an error, find an open-source implementation, or pull up official docs for a language/framework. Runs a local script against the Exa API; no MCP server required.
Research companies with Exa semantic search — find competitors, funding, headcount, news, and build company lists. Use for competitor analysis, market mapping, sourcing companies by category/stage/geo, or 'find companies like X'. Runs a local script against the Exa API; no MCP server required.
Find financial filings with Exa — SEC filings (10-K, 10-Q, S-1), earnings reports, and annual reports. Use to pull a company's financials, compare results across periods, or locate a specific filing. Runs a local script against the Exa API; no MCP server required.
Find people with Exa semantic search — by role, company, and location, LinkedIn-weighted. Use to map a company's team, find experts/decision-makers, locate a specific person, or build people lists. Runs a local script against the Exa API; no MCP server required.
| name | exa-lead-generation |
| description | 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 |
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.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
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.