Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add exa-labs/agent-skills --skill lead-generationgit clone --depth 1 https://github.com/exa-labs/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/exa-labs/agent-skills/lead-generation)<a href="https://agentmods.dev/skills/exa-labs/agent-skills/lead-generation"><img src="https://agentmods.dev/badge/skills/exa-labs/agent-skills/lead-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/exa-labs/agent-skills/lead-generation"><img src="https://agentmods.dev/badge/skills/exa-labs/agent-skills/lead-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.02066 |
| Opus 5.5 | $0.00033 | $0.00826 |
| Sonnet 5.5 | $0.00017 | $0.00413 |
| Haiku 4.5 | $0.00008 | $0.00207 |
Grade A, and why
lead-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Generation with Exa Agent
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.
For very large or continuously maintained lead lists with per-item verification, consider Exa Websets instead: https://exa.ai/docs/websets/quickstart
Prerequisites
This skill requires the Exa MCP server with the Agent tool enabled. Use the agent_tools URL selection alias to enable agent_run.
If the Agent tools are not available, tell the user:
You need the Exa MCP server installed with the Agent tools and your API key. Instructions: https://exa.ai/docs/get-started/exa-mcp
Then stop.
Tool Restriction
Use agent_run, plus Write and Bash (for CSV output). Do NOT use generic web search for the lead list itself.
Workflow
1. Confirm the ICP with the user (one small Agent run if research is needed)
2. Call `agent_run` with an outputSchema
3. If the result is still running, call `agent_run` again with its `runId`
4. Read `output.structured` from the `agent_run` result
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_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"]
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago Changed afa49b934a73
- 18d ago Changed 61b4614c488a
- 2mo ago First seen · 190 lines · 83 tokens per session scan A 96a57ad18fee
lead-generation is a skill published in the GitHub repository exa-labs/agent-skills (65 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 2,066 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
lead-generation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| lead-generationexa-labs/agent-skills | Skill | 65 | yesterday | MIT | A | 83 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |