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linkedin-sales-navigator-alt

linkedin-sales-navigator-alt

github.com/onewave-aiMarketing & growth393498 installsUpdated 1w ago
Repository

About linkedin-sales-navigator-alt

linkedin-sales-navigator-alt is a prospect research skill for business development. It defines ideal customer profiles, finds and qualifies leads using public information, and delivers segmented lists with personalization context and export-ready data.

Key features

  1. 01Define ideal customer profiles
  2. 02Map decision-maker personas
  3. 03Build segmented prospect searches
  4. 04Enrich and score public lead data
  5. 05Prepare campaign lists and exports

Use cases

  1. 01Finding prospects by industry or role
  2. 02Prioritizing leads after job changes
  3. 03Preparing account-based outreach lists

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/onewave-ai/claude-skills --skill linkedin-sales-navigator-alt

After installing, type/linkedin-sales-navigator-altin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
campaigns.md
examples.md
output-template.md
tools-and-export.md

Details

From the project README

LinkedIn Sales Navigator Alternative

Find and qualify prospects on LinkedIn without expensive subscriptions, using only publicly available information.

Contents

  • references/output-template.md — full prospect-list Markdown structure and section layout
  • references/campaigns.md — segmented campaign playbooks (new-in-role, account-based, event follow-up)
  • references/tools-and-export.md — contact-finding methods, tool stack, CSV/CRM export formats
  • references/examples.md — trigger phrases, example request, best practices

Compliance

Work only with publicly available information and respect LinkedIn's Terms of Service. Encourage official LinkedIn tools where appropriate. Aggregate public data for legitimate business development only.

Workflow

  1. Define the Ideal Customer Profile (ICP). Capture industry/sector, company size (employees, revenue), geography, tech stack, and funding stage.

  2. Identify decision-maker personas. Map the primary buyer, influencers, the economic buyer (budget holder), and the technical buyer (evaluation team).

  3. Build the search strategy. Set job-title patterns, company criteria, location filters, seniority level, and description keywords. Choose a list-building approach: account-based, role-based, industry-based, event-based (job changes, funding rounds), or geography-based.

  4. Collect and enrich data (public info only). Compile name, title, company, size, location, industry, public activity, and shared connections. Verify company details, find contact information, add personalization context, and score lead quality. See references/tools-and-export.md for discovery and verification methods.

  5. Prioritize and segment. Tag hot leads (recent job changes), warm leads (shared connections), and cold leads (no prior contact). Group by account for campaigns.

  6. Deliver the prospect list. Format the output per references/output-template.md, attach relevant campaign playbooks from references/campaigns.md, and provide export-ready data per references/tools-and-export.md.

Deliver a targeted, researched set of qualified prospects with personalization context — not just a list of names. See references/examples.md for trigger phrases and best practices.

Comments

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