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 lusha-oss/lusha-mcp-plugin --skill lookalike-prospectgit clone --depth 1 https://github.com/lusha-oss/lusha-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/lusha-oss/lusha-mcp-plugin/lookalike-prospect)<a href="https://agentmods.dev/skills/lusha-oss/lusha-mcp-plugin/lookalike-prospect"><img src="https://agentmods.dev/badge/skills/lusha-oss/lusha-mcp-plugin/lookalike-prospect/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/lusha-oss/lusha-mcp-plugin/lookalike-prospect"><img src="https://agentmods.dev/badge/skills/lusha-oss/lusha-mcp-plugin/lookalike-prospect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00094 | $0.01283 |
| Opus 5 | $0.00047 | $0.00642 |
| Sonnet 5 | $0.00019 | $0.00257 |
| Haiku 4.5 | $0.00009 | $0.00128 |
Grade A, and why
lookalike-prospect 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lookalike Prospect
Expand an ICP from a reference set of known-good companies or contacts. Requires a minimum of 5 references — the lookalike model degrades significantly below this threshold.
Step 1 — Validate Input Count
Count the number of reference companies or contacts provided.
If fewer than 5 are provided, stop and explain before doing anything else:
"Lusha's lookalike model needs at least 5 reference [companies/contacts] to return quality results — fewer than that produces unreliable matches. You've provided [N]. Can you add [5−N] more?"
Do not proceed until the user has provided at least 5 references.
If 5 or more are provided, confirm the reference set with the user:
"Running lookalike search using these [N] [companies/contacts] as the reference set: [list]. Shall I proceed?"
Step 2 — Determine Mode
Based on the user's input, determine whether this is a company lookalike or contact lookalike search:
- References are companies (domains, LinkedIn company URLs, or names) → company mode
- References are people (emails, LinkedIn profile URLs, or name + company) → contact mode
- Mixed input → ask the user to clarify
A bare job title is not a valid seed — a lookalike needs concrete reference companies or people. If the user only has a persona/title in mind, route them to prospect (ICP search) or signal-prospect instead.
Step 3 — Assemble the Seed Set
The lookalike tools accept raw identifiers directly as seeds — no enrichment or Lusha-ID resolution is needed in the common case. Pass the references straight through. The seed count (5–100) is the total identifiers across the seed arrays.
Company mode — lookalike_companies.seeds accepts:
domains(e.g.lusha.com)linkedinUrls(company page URLs)
If the user gave company names rather than domains, resolve each name to a domain first with companies_search (enrich: false — you only need the domain, not reveal data), since the seed schema does not accept bare names. If a name can't be resolved, flag it and proceed only if ≥5 seeds remain.
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 First seen · 106 lines · 94 tokens per session scan A 300488f128d6
lookalike-prospect is a skill published in the GitHub repository lusha-oss/lusha-mcp-plugin (4 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,283 once invoked, about $0.0005 per session on Opus 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.
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