Generate enriched ICP-based lead lists with Exa Agent, including structured scoring and CSV output. Use when generating leads, building prospect lists, finding companies to sell to, outbound research, or ICP-based company discovery. Triggers on leads, lead gen, prospect list, find companies, ICP, outbound list. Distinct from lead-magnet (content asset that captures emails).
Generate enriched ICP-based lead lists with Exa Agent, including structured scoring and CSV output. Use when generating leads, building prospect lists, finding companies to sell to, outbound research, or ICP-based company discovery. Triggers on leads, lead gen, prospect list, find companies, ICP, outbound list. Distinct from lead-magnet (content asset that captures emails).
allowed-tools
["Bash","Read","Write","Skill"]
author
Exa Labs (ported by mktg)
license
MIT
user-invocable
true
metadata
{"openclaw":{"emoji":"🎯"}}
On Activation
Read brand/audience.md, brand/positioning.md, and brand/competitors.md if present to seed ICP + exclusions. All optional.
Confirm Exa MCP with Agent tools (agent_tools / agent_run) or EXA_API_KEY. Without Agent access, stop and surface the MCP config from this skill.
Confirm ICP with the user before large runs (default 200 leads is expensive).
Write CSV under the project (e.g. marketing/leads/ or cwd). Never write credentials into brand/.
Lead Generation with Exa Agent
mktg runtime note
Prefer Exa MCP when available (tools: web_search_exa, web_search_advanced_exa, web_fetch_exa, agent_run).
If MCP Agent tools use the older create/wait/get names (agent_create_run, agent_wait_for_run, agent_get_run_output), use those equivalently.
Without MCP, call the HTTP API with x-api-key: $EXA_API_KEY (POST https://api.exa.ai/search, /contents, /agent).
Firecrawl remains the path for deep scrape of a known URL after Exa discovery.
Generate enriched lead lists using the Exa Agent API. An Agent run is an asynchronous, multi-step web research task: you describe the list you want plus an output schema, and Exa handles query decomposition, searching, verification, enrichment, and structured output internally. You do NOT need to orchestrate parallel searches, subagents, or manual deduplication.
This skill requires the Exa MCP server with the Agent tools enabled (agent_tools): agent_create_run, agent_wait_for_run, agent_get_run_output, agent_cancel_run.
If the Agent tools are not available, tell the user:
Use the Exa Agent tools (, , , ), plus Write and Bash (for CSV output). Do NOT use generic web search for the lead list itself.
agent_create_run
agent_wait_for_run
agent_get_run_output
agent_cancel_run
Workflow
1. Confirm the ICP with the user (one small Agent run if research is needed)
2. Create the lead-gen Agent run(s) with an outputSchema
3. Wait for completion (agent_wait_for_run)
4. Read output.structured (agent_get_run_output)
5. Write the CSV
6. Optional: expand with follow-up runs (previousRunId + input.exclusion)
Step 1: Understand the ICP
When the user says something like "Make a list of 200 leads for [company]", first establish the Ideal Customer Profile. If the user already described the ICP, confirm it. If not, run one small Agent run to research it:
agent_create_run {
"query": "Research {company_name}: what they sell, who their existing customers are, and what their ideal customer profile is.",
"effort": "low",
"outputSchema": {
"type": "object",
"properties": {
"company_description": { "type": "string", "description": "What the company does in 2 sentences or less" },
"icp_description": { "type": "string", "description": "Concise ICP description that clearly defines target companies" },
"sub_verticals": { "type": "array", "maxItems": 10, "items": { "type": "string" }, "description": "Sub-verticals breaking down the ICP" },
"useful_enrichments": { "type": "array", "maxItems": 8, "items": { "type": "string" }, "description": "Enrichment columns useful for filtering high-signal companies" }
},
"required": ["company_description", "icp_description", "sub_verticals", "useful_enrichments"]
}
}
Present the ICP to the user and confirm:
Is the ICP description accurate?
Any companies to exclude (competitors, existing customers)?
How many leads do they want? (default 200)
Any specific enrichment columns they care about?
Step 2: Create the Lead-Gen Run
Design an outputSchema with a bounded companies array. Keep schemas small, flat, and explicit; always bound arrays with maxItems.
Core fields to always include:
company_name (string)
website (string)
product_description (string, "in 12 words or less")
icp_fit_score (integer, 1-10)
icp_fit_reasoning (string, "compelling one-liner in 20 words or less")
Add enrichment fields tailored to the campaign (funding stage, headcount range, headquarters, hiring signals, etc.). Give string fields a length hint in their description to keep CSV output clean.
Use the run inputs for the pieces the old manual pipeline handled by hand:
query - describe the list: the ICP, geography, stage, and how many companies you want
outputSchema - the exact structure back, with maxItems bounding the companies array
input.exclusion - companies to avoid (competitors, existing customers, results from earlier runs)
effort - "auto" by default; "high" or "xhigh" for large or hard lists
Example:
agent_create_run {
"query": "Find 100 companies matching this ICP: {icp_description}. Prioritize {sub_verticals}. For each company, score ICP fit 1-10 for {user_company}.",
"effort": "auto",
"systemPrompt": "Prefer official company sites and recent funding announcements. Do not include duplicates or subsidiaries of the same parent company.",
"input": {
"exclusion": ["{competitor_1}", "{existing_customer_1}"]
},
"outputSchema": {
"type": "object",
"properties": {
"companies": {
"type": "array",
"maxItems": 100,
"items": {
"type": "object",
"properties": {
"company_name": { "type": "string" },
"website": { "type": "string", "format": "uri" },
"product_description": { "type": "string", "description": "in 12 words or less" },
"icp_fit_score": { "type": "integer", "description": "1-10" },
"icp_fit_reasoning": { "type": "string", "description": "one-liner in 20 words or less" }
},
"required": ["company_name", "website", "product_description", "icp_fit_score", "icp_fit_reasoning"]
}
}
},
"required": ["companies"]
}
}
agent_create_run returns an agent_run_... ID immediately. Save it.
Step 3: Wait and Read Output
Call agent_wait_for_run with the run ID. It polls until the run reaches a terminal status (completed, failed, or cancelled) or times out - call it again if the run is still going.
When completed, call agent_get_run_output. Read the companies from output.structured, citations from output.grounding, and the run cost from costDollars.
Do not paste the full raw output into the conversation - go straight to CSV.
Step 4: Write the CSV
Write output.structured.companies to {target_company}_leads_{YYYY-MM-DD}.csv, sorted by icp_fit_score descending. Join any array fields with " | ". Use Python's csv.writer (handles quoting/escaping) via Bash, or Write directly for small lists.
Print a summary:
## Lead Generation Complete
- Total leads: {count}
- ICP score distribution: 8-10: {N} | 5-7: {N} | 1-4: {N}
- Run ID: {agent_run_id}
- Cost: ${costDollars}
- Output: {filename}
Step 5: Expanding the List
If the user wants more leads than one run returned:
Create a follow-up run with previousRunId set to the completed run's ID, asking for additional companies
Put the company names already collected into input.exclusion so the new run avoids them
Append the new results to the CSV and re-deduplicate by normalized company name (strip "Inc"/"Ltd"/etc., case-insensitive)
For lists in the many hundreds, run a few runs sequentially this way rather than one giant run, and confirm scope with the user first: "This will require ~{N} Agent runs. Proceed?"
Handling Failures
If a run ends failed, read the error from agent_get_run_output, adjust the query or schema, and retry once with different wording
Use agent_cancel_run if a run is clearly researching the wrong thing
If results are consistently below the requested count, narrow the ICP into 2-3 sub-vertical runs instead of one broad run
