Generate rigorous mathematical proofs for ML/AI theoretical claims.
npx skills add https://github.com/goupup-ai/miccai25 --skill proof-writer-goupup-ai
Please help me install this Agent Skill. Skill: proof-writer Source: https://github.com/goupup-ai/miccai25/tree/main/ARIS/skills/proof-writer Command: npx skills add https://github.com/goupup-ai/miccai25 --skill proof-writer-goupup-ai
This Skill eliminates the risk of producing incomplete, incorrect, or unrigorous mathematical proofs for ML/AI theoretical claims (theorems, lemmas, propositions, corollaries), which can invalidate research findings, lead to retracted papers, or waste hours of revision time.
Use the proof-writer skill to generate a complete, rigorous proof package for the lemma stating that the multi-granularity SSM in FMC-Net captures long-range vertebral feature dependencies with linear computational complexity.
High-intent search queries and answers about installing and using this skill.
To generate rigorous mathematical proofs for machine learning theoretical claims, use a proof generation workflow that produces step-by-step justifications for every nontrivial implication. This ensures your ML theorems and lemmas have no hidden gaps or hand-waving.
Yes, you can identify missing assumptions that block AI theory proof validity through feasibility triage. This process transparently classifies claims as provable as stated, provable with extra assumptions, or currently non-justifiable to prevent fabricating invalid proofs.
The best way to validate theorem proofs before academic paper submission is to enforce strict mathematical rigor by requiring explicit justifications for all nontrivial implications and explicit handling of boundary cases. This transparent reporting verifies theorem validity.
Automated proof verification works for completing proof sketches in ML research by rigorously drafting missing steps and verifying the entire mathematical argument. It applies to academic workflows involving theoretical claim validation and proposition completion.
You should not use automated mathematical proof generation for AI theorems when a claim is classified as currently non-justifiable. Forcing proof generation in such cases risks fabricating invalid proofs and producing incomplete mathematical arguments for your research.
To handle boundary cases when drafting mathematical proofs for machine learning models, enforce strict mathematical rigor by requiring explicit handling of all edge conditions. This transparent reporting ensures nontrivial implications are fully justified without hidden gaps.
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