What problem does it solve? Recruiting product teams drown in trend noise—vendor announcements, viral posts, and syndicated headlines—without a disciplined way to decide which signals deserve attention. This Skill applies a Hung Lee–inspired manual curation lens to separate noise from genuine market shifts and translate them into concrete product decisions. ## Core Features & Use Cases - Signal Classification: Labels each finding as noise, weak_signal, emerging_pattern, or established_shift based on evidence independence, maturity, and counter-signals. - Structured Research Workflow: Enforces source diversity (primary, empirical, practitioner, counterarguments, APAC context), date separation, and disconfirming-evidence searches before any conclusion. - Product Translation: Converts relevant signals into watch, test, build, avoid, or no_action recommendations with revisit triggers. - Use Case: A product lead asks whether AI candidate-screening tools are an established shift; the Skill browses current sources, weighs vendor claims against practitioner evidence, and returns a calibrated review packet with a falsifiable experiment. ## Quick Start Use the recruiting-trend-radar skill to investigate current AI-in-hiring signals and recommend what Talent Signal should watch, test, or avoid this quarter.
