Agent Night demo recap: Evil Martians on making your docs discoverable to agents

Irina Nazarova of Evil Martians argued at Agent Night that developer tools must increasingly be designed for discovery by AI agents, which find products through both web documentation and public code repositories. Her company’s Ruby & Rails LLM Discoverability Scorecard evaluates retrieval, or whether agents can access documentation at request time, separately from training-data presence and model recall. She emphasized hosting documentation repositories publicly on GitHub under permissive licenses, with clear descriptions and topics, while supporting crawlers and Markdown-based documentation routes for easier retrieval. Nazarova said website content faces substantial filtering before entering model training corpora, whereas public repository documentation can avoid some of those barriers. Evil Martians’ tests suggested that repeated independent copies of content correlate more strongly with model recall than GitHub stars or forks, leading to recommendations such as distributing well-commented integration boilerplate. The company offers a public analyzer and an open-source agent skill for improving retrieval signals, while cautioning that tools such as llms.txt currently have limited practical value and that training-data visibility can only affect future model corpus updates.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.