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Optimize AI Hypotheses with BT Tournament Skill

Optimize AI Hypotheses with BT Tournament Skill

Regular price £40.99
Regular price £40.99 Sale price
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Works withClaude CodeCursorCodex
Optimize AI Hypotheses with BT Tournament Skill

Optimize AI Hypotheses with BT Tournament Skill

Regular price £40.99
Regular price £40.99 Sale price
SAVE Sold out

Optimize AI Hypotheses with BT Tournament Skill

The BT Tournament Skill facilitates the ranking of competing hypotheses or proof skeletons by employing a joint batch Maximum A Posteriori (MAP) Bradley-Terry fit and generating approximate posterior intervals. This skill is designed for use in scenarios where there are three or more competing candidates for the next experiment, or when a user inquires about which candidate seems most promising.

What this skill does

  • Collects candidate hypothesis node IDs and texts from the system's active memory frontier.
  • Uses pairwise judgments for hypothesis comparison without spawning additional sub-agents.
  • Records judgments of winners between pairs and updates the overall leaderboard accordingly.
  • Retrieves and examines the top entries from the BT leaderboard reflecting the current strongest hypotheses.

Who it is for

This skill is best suited for developers and research teams utilizing AI coding agents such as Claude Code, Cursor, and Codex. It supports those involved in AI development who need to systematically assess multiple hypotheses efficiently within their workflow.

Use cases

  • Approximately ranking three or more candidate hypotheses when determining the best course for further experiments or development.
  • Utilizing explicit user commands such as `/bt-tournament` to determine leading candidates when multiple options remain unresolved.
  • Integrating into AI systems to assist with decision-making processes related to hypothesis testing.

Technical details

This skill utilizes academic-tools, aiapplication, and bt-tournament capabilities to execute its functions. It records pairwise judgments and updates comparison ledgers to maintain current data on hypothesis strengths. Importantly, the intervals produced are 95% approximate posterior intervals based on a centered Laplace approximation and should not be construed as calibrated frequentist confidence intervals.


Source & Licence

This package is built on open-source work published by whenpoem (whenpoem/aiscientist) and distributed under MIT. The original licence text and copyright notice are included in your download.

Personal and commercial use, modification and redistribution are permitted, provided the original copyright and licence notice are retained.

Your purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.

Delivery & Support

  • Delivery: instant — a secure download link is emailed to you as soon as payment is confirmed.
  • Format: ZIP archive containing the skill files, documentation and the original licence.
  • Support: support@mcpcart.com — we aim to reply within 2 business days.
  • Updates: updates are included only where stated on this page.

Refunds

This is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.

Claude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.

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