Find agent skills by outcome

- The methodology mirrors approaches in recent literature

but potential differences in dataset preprocessing and parameter tuning may exist.

- The current accuracy is 78%

which is lower by 7% compared to similar methods.

3. Conclusions/Recommendations: After the reasoning

highlight your top suggestions or next steps.

Our experiment on X algorithm yielded an accuracy of 78%

but similar methods are achieving 85%. Any suggestions?

- When needed

use bullet points or numbered lists for clarity.

2. Brainstorm/Reasoning Steps: List possible improvements

novel approaches

- Generating a list of possible improvements

extensions

- Critically assessing any gaps

weaknesses

- Carefully analyze the provided materials

extract key findings

- Identifying foundational concepts

assumptions

Nurse

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- Suggest different genres such as fantasy

horror

To use for future Games: Replace or specify the edition in the prompt (e.g.

for the LA 2028 Summer Olympics) when running in future years.

If literally no major Olympic events in the week (e.g.

outside Games period)

- General availability (e.g.

many events stream live on Olympics.com or Peacock; replays often available)

Data

- Popularity sorting rationale (e.g.

based on historical viewership data from previous Olympics)

Creative

- Time zone translation details (e.g.

'All times converted to EST based on user location in East Hartford

- Broadcast caveats (e.g.

regional availability

- Local Time (translated to user's timezone

e.g.

Focus on events broadcast/streamed on major official Olympic broadcasters (e.g.

NBC/Peacock in US

- Broadcast/Streaming (primary platforms

e.g.

Sort events within each day's table by estimated popularity (descending: use general viewership

global interest

- Sport/Discipline (e.g.

'Figure Skating' or 'Ice Hockey')

- Name (e.g.

'Men's Figure Skating Short Program' or 'USA vs. Canada Ice Hockey Preliminary')