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B2B software vendor evaluation skill for Claude Code — domain-expert questions, vendor AI agent conversations, evidence-based scoring

60 starsPython

Claude skill that evaluates B2B vendors by talking to their AI agents

by ogotlieb·Mar 26, 2026·45 points·6 comments

AI Analysis

●●SolidBig BrainNiche Gem

AI agents interrogating other AI agents is a genuinely novel vendor evaluation approach.

Strengths
  • Adversarial questioning surfaces hidden requirements buyers didn't know to ask
  • Evidence-tracked scoring shows which claims are vendor-verified vs public sources
  • Automated company research eliminates manual form-filling for context
Weaknesses
  • Requires vendors to have Salespeak Frontdoor API agents, limiting real-world applicability
  • Niche workflow that procurement teams may not trust for high-stakes decisions
Category
Target Audience

B2B buyers, procurement teams, software evaluators

Similar To

G2 · Gartner · Capterra

Post Description

I built this because I was evaluating software vendors and realized the process hadn't changed in 20 years: fill out forms, read G2 reviews, sit through demos designed to avoid your real questions. The skill takes a different approach. You give it your company name and the vendors you're comparing. It:

Researches your company automatically -- industry, size, stack -- so you don't fill out a form Asks 2-4 category-specific questions before evaluating anything. Not generic. For a CS platform evaluation it might ask "is your team high-touch or low-touch? Most CS platforms are built for one and barely work for the other." These surface requirements buyers didn't know they had. Tries to find and talk directly to each vendor's AI agent -- a REST API call that checks for a Company Agent, then runs a structured due diligence conversation if one exists Asks adversarial questions: "What are your customers' most common complaints?" and "What use cases are you NOT a good fit for?" -- and flags when agents deflect instead of answering Cross-references every vendor claim against independent sources (G2, Gartner, press) in a Claims vs. Evidence table Produces a scorecard with transparent evidence tracking -- each score shows whether it's backed by vendor-verified evidence or public sources only

The agent-to-agent piece is technically new. When a vendor has an AI agent, Claude (working for the buyer) interrogates it directly, then fact-checks its answers. When vendors have different evidence levels, the skill quantifies what would change if the missing evidence were confirmed -- so it doesn't silently favor vendors that happen to have AI agents. It works fully for any vendor, with or without an AI agent. Vendors without one get evaluated on public sources with the same scoring framework. We built this at Salespeak -- we help B2B vendors build AI Company Agents. So yes, there's a connection: when an agent finds a vendor's Company Agent, it uses our Frontdoor API to talk to it. But the skill is genuinely useful without that, and we wanted to be honest about that rather than ship something that only works as a product demo. MIT licensed. To install, just ask Claude Code: "Install the buyer-eval skill from salespeak-ai on GitHub." Then /buyer-eval to run it. Felt appropriate that installing a skill for AI agents works the same way. Repo: https://github.com/salespeak-ai/buyer-eval-skill Happy to answer questions about how the agent-to-agent conversation works technically.

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