Log, review, and generate content angles from enterprise AI signals.
npx skills add https://github.com/mentilead/growthOS --skill signal-mentilead
Please help me install this Agent Skill. Skill: signal Source: https://github.com/mentilead/growthOS/tree/main/skills/signal-monitor Command: npx skills add https://github.com/mentilead/growthOS --skill signal-mentilead
This Skill helps practitioners stay informed about the rapidly evolving enterprise AI landscape by systematically logging, classifying, and analyzing external signals, ensuring their personal experiments remain aligned with or informed by broader industry trends.
Log a new signal by describing the signal, its source, and whether it confirms or contradicts your experiment.
High-intent search queries and answers about installing and using this skill.
To track enterprise AI signals, you log signals from sources like reports and announcements, classify them as confirmations or contradictions, and generate content angles bridging them with your personal experiment thesis.
Generating content ideas from market intelligence involves logging external industry trend signals, grouping them by confirmation or contradiction status, and identifying content opportunities that align with your ongoing experiment findings.
You log AI industry signals by describing the signal, noting its source, and classifying it as a confirmation, contradiction, or unknown to maintain context for your content strategy and experiment thesis.
Yes, you can review logged market intelligence signals over a 60-day period, grouped by their classification status, which highlights unused signals and identifies potential content opportunities for your strategy.
Yes, AI signal tracking requires reading status and memory files to maintain context and your experiment thesis, which is essential for accurately framing generated content angles against external industry trends.
AI signal classification focuses on confirmations and contradictions to systematically bridge external enterprise trends with personal experiment findings, directly identifying whether new capabilities validate or challenge your current approach.
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