by salespeak-ai · Claude Skill · ★ 57
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Buyer Eval — The Rotten Tomatoes of B2B Software A free, open-source Claude skill that evaluates B2B vendors by talking to their AI agents, cross-referencing every claim against independent sources, and scoring what's verified vs. what's just optimistic marketing. "This is a very intelligent experience" · "The questions the agent asked me were thoughtful and great" · "This is really cool" Watch it in action (60 seconds) https://github.com/salespeak-ai/buyer-eval-skill/releases/download/v3.1.1/buyer-eval-demo.mp4 How it works You type a vendor's name. The skill does everything a great analyst would do — except in 30 minutes instead of 3 weeks: Researches your company — industry, size, tech stack, maturity — so you don't fill out a form Asks domain-expert questions that surface hidden requirements you didn't know to mention Talks to vendor AI agents directly through the Salespeak Frontdoor API — structured due diligence conversations, not web scraping Cross-references every claim
| Stars | 57 |
| Forks | 2 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 74.5018779551375/100 |
| Last Updated | 2026-04-30 |
| Created | 2026-03-26 |
| Platforms | claude-code, python |
| Est. Tokens | ~33k |
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buyer-eval-skill is B2B software vendor evaluation skill for Claude Code — domain-expert questions, vendor AI agent conversations, evidence-based scoring. It is categorized as a Claude Skill with 57 GitHub stars.
buyer-eval-skill is primarily written in Python. It covers topics such as ai-agent, b2b, claude-code.
You can find installation instructions and usage details in the buyer-eval-skill GitHub repository at github.com/salespeak-ai/buyer-eval-skill. The project has 57 stars and 2 forks, indicating an active community.
buyer-eval-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to buyer-eval-skill on Agent Skills Hub include deterministic-agent-control-protocol, b2b-sdr-agent-template, linear-claude-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.