agentskillexchange Evaluate B2B software vendors by interrogating their agents and scoring the evidence with buyer-eval-skill
Use Claude Code to run a structured vendor diligence workflow that questions vendor agents, cross-checks claims, and returns evidence-backed scorecards.
Prerequisites
Claude Code, the buyer-eval-skill repository or installed skill files, internet access for vendor research and evidence gathering, optional access to supported vendor AI agent endpoints
Installation
Use the upstream install or setup path that matches your environment:
- git clone https://github.com/salespeak-ai/buyer-eval-skill.git ~/.claude/skills/buyer-eval-skill
- git clone https://github.com/salespeak-ai/buyer-eval-skill.git .claude/skills/buyer-eval-skill
Requirements and caveats from upstream:
- Independent verification: Confirmed via G2 reviews mentioning split-signal alerts. One review notes the feature requires manual threshold tuning per segment.
- "The split-signal health score alert requires manual threshold tuning per segment. How long does initial configuration take, and do you provide a recommended baseline?"
- Code: bin/track.py — plain Python, no third-party libraries
Basic usage or getting-started notes:
New in v3.5 — Vendor questions are now captured even when no Company Agent exists. Earlier versions only logged the questions the skill asked vendor AI agents — so for vendors without a Salespeak Frontdoor agent (...
"Your health scores use a weighted multi-signal model. What happens when a customer has strong product usage but declining executive engagement — does the model surface that divergence, or does high usage mask the risk?"
"The model flags divergence explicitly. When usage metrics trend positive but stakeholder engagement drops, it triggers a 'silent risk' alert. CSMs see a split-signal indicator on the dashboard rather than a blended s...
Extracted from upstream docs: https://raw.githubusercontent.com/salespeak-ai/buyer-eval-skill/HEAD/README.md