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 apolloio/apollo-mcp-plugin --skill enrich-leadgit clone --depth 1 https://github.com/apolloio/apollo-mcp-pluginWrote 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/apolloio/apollo-mcp-plugin/enrich-lead)<a href="https://agentmods.dev/skills/apolloio/apollo-mcp-plugin/enrich-lead"><img src="https://agentmods.dev/badge/skills/apolloio/apollo-mcp-plugin/enrich-lead/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/apolloio/apollo-mcp-plugin/enrich-lead"><img src="https://agentmods.dev/badge/skills/apolloio/apollo-mcp-plugin/enrich-lead.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- 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 | $0.00041 | $0.00765 |
| Opus 5 | $0.00020 | $0.00382 |
| Sonnet 5 | $0.00008 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
enrich-lead scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to enrich-lead — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enrich Lead
Turn any identifier into a full contact dossier. The user provides identifying info via "$ARGUMENTS".
Examples
/apollo:enrich-lead Tim Zheng at Apollo/apollo:enrich-lead https://www.linkedin.com/in/timzheng/apollo:enrich-lead [email protected]/apollo:enrich-lead Jane Smith, VP Engineering, Notion/apollo:enrich-lead CEO of Figma
Step 1 — Parse Input
From "$ARGUMENTS", extract every identifier available:
- First name, last name
- Company name or domain
- LinkedIn URL
- Email address
- Job title (use as a matching hint)
If the input is ambiguous (e.g. just "CEO of Figma"), first use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with relevant title and domain filters to identify the person, then proceed to enrichment.
Step 2 — Enrich the Person
Credit warning: Tell the user enrichment consumes 1 Apollo credit before calling.
Use mcp__claude_ai_Apollo_MCP__apollo_people_match with all available identifiers:
first_name,last_nameif name is knowndomainororganization_nameif company is knownlinkedin_urlif LinkedIn is providedemailif email is provided- Set
reveal_personal_emailstotrue
If the match fails, try mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with looser filters and present the top 3 candidates. Ask the user to pick one, then re-enrich.
Step 3 — Enrich Their Company
Use mcp__claude_ai_Apollo_MCP__apollo_organizations_enrich with the person's company domain to pull firmographic context.
Step 4 — Present the Contact Card
Format the output exactly like this:
[Full Name] | [Title] [Company Name] · [Industry] · [Employee Count] employees
| Field | Detail |
|---|---|
| Email (work) | ... |
| Email (personal) | ... (if revealed) |
| Phone (direct) | ... |
| Phone (mobile) | ... |
| Phone (corporate) | ... |
| Location | City, State, Country |
| URL | |
| Company Domain | ... |
| Company Revenue | Range |
| Company Funding | Total raised |
| Company HQ | Location |
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.
- 10d ago First seen · 81 lines · 41 tokens per session scan A 3ef7d29ea515
enrich-lead is a skill published in the GitHub repository apolloio/apollo-mcp-plugin (20 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 765 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to enrich-lead, differing in 2 lines, and is treated as a copy.
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