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 agentmods add skills/lusha-oss/lusha-mcp-plugin/prospectnpx skills add lusha-oss/lusha-mcp-plugin --skill 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/prospect)<a href="https://agentmods.dev/skills/lusha-oss/lusha-mcp-plugin/prospect"><img src="https://agentmods.dev/badge/skills/lusha-oss/lusha-mcp-plugin/prospect.svg" alt="Measured on agentmods" 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 | $0.00078 | $0.01014 |
| Opus 5 | $0.00039 | $0.00507 |
| Sonnet 5 | $0.00016 | $0.00203 |
| Haiku 4.5 | $0.00008 | $0.00101 |
Grade A, and why
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 6d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prospect
Go from an ICP description to a ranked, phone-enriched lead list. Filters are resolved before search — never guess filter values.
Step 1 — Parse the ICP
Extract structured filters from the user's natural language description. Some filters take free-form text directly; others must be resolved to canonical values first.
Contact filters (prospecting_contact_search):
- Job titles → pass directly as
jobTitles(free-form strings, e.g. "VP of Sales"). No resolution call needed. - Department / seniority → resolve via
prospecting_contact_filters(type:departments,seniority). Use these for broad role targeting when a specific title isn't given. - Country → resolve via
prospecting_contact_filters(type:all_countries); Location → type:locations(requireslocationSearchText).
Company filters (prospecting_company_search):
- Industry → resolve via
prospecting_company_filters(type:industries_labels) - Size → resolve via
prospecting_company_filters(type:sizes) - Revenue → resolve via
prospecting_company_filters(type:revenues) - Location → resolve via
prospecting_company_filters(type:locations, requiresq) - Tech stack → resolve via
prospecting_company_filters(type:technologies, requiresq) - Buying intent → resolve via
prospecting_company_filters(type:intent_topics)
Resolve every non-title filter to canonical values before searching — passing raw natural-language strings as structured filter values is the most common cause of search failures. Each prospecting_*_filters call resolves one filter type; run the independent lookups in parallel.
If the ICP is too vague to resolve (no title, no industry, no company size), ask one clarifying question before proceeding. At minimum, a title or department and at least one company-level constraint are required.
See references/filter-guide.md for filter resolution details.
Step 2 — Search Companies
Use prospecting_company_search with resolved company filters. Request up to 25 results. This scopes the contact search to qualified accounts.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 85 lines · 78 tokens per session scan A d1c4b592ced4
prospect is a skill published in the GitHub repository lusha-oss/lusha-mcp-plugin (4 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 1,014 once invoked, about $0.0004 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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