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 signal-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/signal-prospect)<a href="https://agentmods.dev/skills/lusha-oss/lusha-mcp-plugin/signal-prospect"><img src="https://agentmods.dev/badge/skills/lusha-oss/lusha-mcp-plugin/signal-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/signal-prospect"><img src="https://agentmods.dev/badge/skills/lusha-oss/lusha-mcp-plugin/signal-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 | $0.00105 | $0.01818 |
| Opus 5 | $0.00053 | $0.00909 |
| Sonnet 5 | $0.00021 | $0.00364 |
| Haiku 4.5 | $0.00011 | $0.00182 |
Grade A, and why
signal-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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Signal-Prospect
Start from a buying signal — company or contact level — and end with a call-ready list of enriched decision makers. This is Lusha's core differentiated workflow: trigger → identify → phone reveal.
Step 1 — Identify the Signal Mode
Determine which mode applies based on the user's request:
Company signal mode — the trigger is something happening at a company (funding, hiring surge, headcount change, news event). Goal: find the right people at those companies and get their phones.
Contact signal mode — the trigger is something happening to a person (got promoted, changed company). Goal: find those people and get their updated contact details.
If unclear, ask: "Are you looking for companies showing a specific signal, or individual contacts who recently changed roles or employers?"
Step 2 — Discover Available Signal Types and Sub-Filters
These tools are the authoritative source of valid signal identifiers — never assume a signal type exists without confirming it here, since invalid values are rejected with a 400.
Company signals: Call signals_company_filters with no filterType to get the directory: { signalTypes, availableFilters: [{ filterType, requiresQuery }] }. To enumerate the values for a sub-filter, call it again with filterType set to newsEventTypes, hiringByDepartments, or hiringByLocations (hiringByLocations requires a query).
Contact signals: Call signals_contact_filters to get the supported contact signal types (e.g. promotion, companyChange, allSignals).
Step 3 — Map User Intent to a Signal Type
Match the user's phrasing to a signal identifier returned in Step 2. The table is a starting point — always validate the identifier against the live directory before using it:
| User says | Signal type (names) |
News sub-type (applied in Step 5) |
|---|---|---|
| "raised funding / Series A/B/C / IPO" | financialEventsNews |
Funding Round, IPO, Strategic Investment |
| "surging in hiring / lots of open roles" | surgeInHiring |
— |
| "growing fast / headcount up" | headcountIncrease3m / headcountIncrease6m |
— |
| "hiring sales reps" | surgeInHiringByDepartment (+ filterByDepartment) |
— |
| "new partnership / new customer" | commercialActivityNews |
New Customer, Partnership, New Location |
| "new product launch" | productActivityNews |
Product Launch, Product Integration |
| "executive just joined / new CRO" | peopleNews |
Executive Hire, Executive Departure |
| "contact just got promoted" | contact signal promotion |
— |
| "contact changed company / new job" | contact signal companyChange |
— |
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.
- 11d ago First seen · 124 lines · 105 tokens per session scan A 149008c85561
signal-prospect is a skill published in the GitHub repository lusha-oss/lusha-mcp-plugin (4 stars, last pushed 1mo ago), licensed MIT. It adds 105 tokens to every session and 1,818 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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