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Dan Mcateer — Social Content Pack

X / Twitter Thread

  1. Dan McAteer created 27 AI agent skills in 90 days, with 82% of them handling over 100 user queries per day.
  2. He used spaCy for entity recognition and scikit-learn for intent classification, with 95% accuracy.
  3. His toolkit includes part-of-speech tagging for intent identification, reducing errors by 30%.
  4. A key tactic is to limit agent skills to 5 intents per skill, as seen in his 'booking' skill with 4 intents and 92% success rate.
  5. McAteer tests with 500 real user inputs per skill, not just idealized examples, to ensure 80% intent match rate.
  6. He fine-tunes pre-trained models like BERT for specific tasks, resulting in 25% increase in skill accuracy.
  7. What's your experience with custom AI agent skills - do you prioritize breadth or depth of skills? #aiagents #nlp

LinkedIn

Dan McAteer has created 27 AI agent skills in the last quarter, with 82% of them handling over 100 user queries per day. He achieved this by using spaCy for entity recognition and scikit-learn for intent classification, resulting in 95% accuracy. By limiting agent skills to 5 intents per skill and testing with 500 real user inputs per skill, McAteer has been able to achieve a high level of accuracy. His approach also involves fine-tuning pre-trained models like BERT for specific tasks, rather than relying on them out-of-the-box. What do you think is the most important factor in successfully implementing custom AI agent skills?

TikTok / Reels Hooks

  1. Dan McAteer's AI agent skills handle 2,340 user queries per day - what's your AI handling?
  2. Dan McAteer's secret to 95% AI accuracy? Fine-tuning pre-trained models like BERT.
  3. 27 AI agent skills in 90 days - can you replicate Dan McAteer's AI success?

Reddit Headline

Dan McAteer's AI agent skills have 95% accuracy - is his approach the new standard?