rickoslyder
8 verified skills
content-filter
Filter and classify AI research content for relevance, topic, and author category. Use for bulk triage of raw content before detailed claim extraction.
development
digest-generation
Generate a weekly AI intelligence digest from synthesized topic analyses and hype assessments. Use after synthesis and hype assessment to produce a readable, opinionated summary for sophisticated technical readers.
data-ai
topic-synthesis
Synthesize claims across multiple sources to identify consensus, disagreements, and emerging narratives on AI research topics. Use when you have claims from both lab researchers and critics on the same topic and need to understand where they agree, disagree, and what the overall hype level is.
data-ai
claim-extraction
Extract structured claims, predictions, hints, and opinions from AI research content. Use when processing tweets, blog posts, substacks, or other content from AI researchers to identify substantive assertions about AI capabilities, limitations, and progress.
data-ai
hype-assessment
Assess overall hype levels across AI topics by comparing lab researcher enthusiasm against critic skepticism. Use after topic synthesis to identify which topics are overhyped, underhyped, or accurately assessed by the field.
testing
content-filter
Filter and classify AI research content for relevance. Use when processing raw content from Twitter, Substacks, blogs, or podcasts to determine if it's worth extracting claims from. Assigns relevance scores, topics, and author categories.
development
hint-detection
Detect hints about unreleased AI research or capabilities from lab researcher communications. Use when analyzing tweets, posts, or interviews from people at major AI labs to identify signals about upcoming work.
data-ai
prediction-tracking
Track and evaluate AI predictions over time to assess accuracy. Use when reviewing past predictions to determine if they came true, failed, or remain uncertain.
data-ai