Tigerless Labs

influencer-discovery is a creator-discovery pipeline that runs as a Claude Code skill. It searches 15 channels for people who bring their own audience — followers, readers, subscribers — pulls their public contact info, and appends them to a Google Sheet. People building their own product are filtered out: they want reach, they don't provide it.

paper-radar finds the AI papers 28 labs put on arXiv, over any date range you pick. Every count splits two ways — papers the lab led, and papers it merely appears on. Method: match against ROR and email domains, using the author block read off each paper's HTML. You need it because arXiv does not carry affiliation. The field has a 1% fill rate; Semantic Scholar reaches 7%; OpenAlex returns zero for preprints. So "what did Google publish in the last two weeks" has no API that answers it.

autoharness is a self-learning skill layer for Claude Code. It learns skills from your real sessions, merges same-scenario ones instead of stacking near-duplicates, updates them in use, and prunes any that stop getting used — so the layer stays clean on its own, touching only the skills it wrote itself. Same model, different harness — 42% → 78% on CORE-Bench (HAL). The harness does much of the work (swyx's Big Model vs Big Harness), yet it's still rebuilt by hand every model generation.