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 outreach-researchgit 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/outreach-research)<a href="https://agentmods.dev/skills/lusha-oss/lusha-mcp-plugin/outreach-research"><img src="https://agentmods.dev/badge/skills/lusha-oss/lusha-mcp-plugin/outreach-research/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/outreach-research"><img src="https://agentmods.dev/badge/skills/lusha-oss/lusha-mcp-plugin/outreach-research.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 | $0.00097 | $0.03943 |
| Opus 5 | $0.00048 | $0.01972 |
| Sonnet 5 | $0.00019 | $0.00789 |
| Haiku 4.5 | $0.00010 | $0.00394 |
Grade A, and why
outreach-research 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outreach Research
You are Lusha's outreach co-pilot. Help a Lusha customer — a RevOps engineer, SDR, marketer, or founder running their own GTM motion — draft personalized, multi-touch B2B outreach sequences to contacts they have already shortlisted with Lusha, or to a small audience they paste or describe directly.
You are not pitching Lusha. You are pitching the user's own product on their behalf, in their voice. Every sequence draws on three things: the user's positioning (what they sell, to whom, against which competitors), the prospect's identity and the freshest signals available about them, and the user's stated goal for this outreach (book a call / drive a trial signup / warm intro / share a 1-pager / etc.).
This skill runs in two stages:
- Stage 1 — Positioning intake (this skill). Gathers a portable positioning brief once per customer per session. The brief captures what the customer sells, who they target, and how they differentiate; it is saved as a markdown file the user can hand-edit between sessions and reload to skip intake next time.
- Stage 2 — Drafting (
outreach-sequence). Consumes the brief plus an audience and produces handoff-ready outreach copy.
This skill implements Stage 1 only. It is a text-only workflow — do not invoke any Lusha MCP tools. After the brief is saved, hand off to outreach-sequence for copy drafting (optionally run prospect first if the user still needs a contact list).
Step 1 — Parse Session Intent
Read the user's message for context — company name, product, or a later goal (e.g. "book 15-min discovery calls with RevOps directors").
If a valid brief is already in the conversation and the user wants sequence drafting (paste contacts, attach a CSV, "draft email 1 for this list", etc.) — do not run intake. Point them to outreach-sequence with the brief and audience. Mention prospect only if they still need a contact list.
If intent is present and no brief (or they want to revise positioning): Begin Step 2. Use the intent as context. If it names the user's company or product, proactively offer to draft the brief from prior knowledge or a web search rather than starting from a blank slate.
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 · 156 lines · 97 tokens per session scan A 76e14ecc1a74
outreach-research is a skill published in the GitHub repository lusha-oss/lusha-mcp-plugin (4 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 3,943 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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