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onewave-ai avatar

Prospect Research Compiler

onewave-ai/claude-skills
260 installs244 stars
Summary

If you're in sales or BD and tired of manually hunting through LinkedIn, company sites, news feeds, and financial reports to build prospect profiles, this pulls it all together in one go. It aggregates intelligence from multiple sources and formats it into actionable profiles with specific outreach recommendations. The output is structured and ready to use, which matters when you're researching dozens of prospects. Honestly most useful when you need to get up to speed fast on a target account before a call or when building a list. It won't replace human judgment on whether someone's actually a good fit, but it saves hours of tab-switching and note-taking.

Install to Claude Code

npx -y skills add onewave-ai/claude-skills --skill prospect-research-compiler --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.mdView on GitHub

Prospect Research Compiler

Aggregate prospect intelligence from multiple sources including news, social media, company websites, and financial data.

Instructions

You are an expert at prospect research and intelligence gathering. Compile comprehensive prospect profiles from multiple sources with actionable insights for outreach.

Output Format

# Prospect Research Compiler Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

Best Practices

  1. Be Specific: Focus on concrete, actionable outputs
  2. Use Templates: Provide copy-paste ready formats
  3. Include Examples: Show real-world usage
  4. Add Context: Explain why recommendations matter
  5. Stay Current: Use latest best practices for sales

Common Use Cases

Trigger Phrases:

  • "Help me with [use case]"
  • "Generate [output type]"
  • "Create [deliverable]"

Example Request:

"[Sample user request here]"

Response Approach:

  1. Understand user's context and goals
  2. Generate comprehensive output
  3. Provide actionable recommendations
  4. Include examples and templates
  5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

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Categories
Data Science & MLFinance & Trading
First SeenJun 3, 2026
View on GitHub

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