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proof-writer

Draft rigorous mathematical proofs for ML/AI theory from formal claims and assumptions.

2|Updated Mar 29, 2026
Search Tags:#mathematics#proof#formalization#theorem#proof-writing#ml-ai
One-click install
npx skills add https://github.com/satsuki-64/MiniAgentWorkflow --skill proof-writer-satsuki-64
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: proof-writer
Source: https://github.com/satsuki-64/MiniAgentWorkflow/tree/main/.skills/proof-writer
Command: npx skills add https://github.com/satsuki-64/MiniAgentWorkflow --skill proof-writer-satsuki-64

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps researchers and practitioners generate rigorous mathematical proofs for ML/AI theory. It addresses the challenge of turning informal theorem sketches into formal, checkable argument structures, filling in missing steps and clarifying assumptions.

Core Features & Use Cases

  • Formalization: Converts user-provided theorem statements and assumptions into a structured proof skeleton with explicit lemmas and dependencies.
  • Step-by-step Drafting: Produces rigorous, justification-rich proof steps that can be reviewed or extended.
  • Assumption Management: Extracts and clarifies hypotheses, notations, and boundary conditions; supports common proof strategies like direct, contrapositive, and induction.
  • Use Case: A researcher requests a complete proof for a lemma in a ML theory paper, or asks to fill gaps in a proposed sketch.

Quick Start

Provide the exact theorem statement and its assumptions; the system will generate a detailed, checkable proof package ready for review.

Frequently Asked Questions about proof-writer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I formalize a machine learning theorem sketch into a complete proof?▼

To formalize a machine learning theorem, provide the precise claim, explicit assumptions, and notation to generate a structured proof skeleton with explicit lemmas and dependencies.

What is the best way to fill missing steps in a proposed ML theory proof?▼

Filling missing steps in an ML theory proof requires submitting the theorem, boundary conditions, and any user-provided proof sketch to produce a justification-rich, step-by-step argument structure ready for review.

Can I use automated proof drafting for lemma completion in AI research papers?▼

Automated proof drafting supports lemma completion for AI research papers by converting explicit hypotheses and theorem statements into a checkable proof package with a defined status.

Does formalizing ML proofs require providing explicit assumptions and notation?▼

Formalizing ML proofs requires explicit assumptions, precise notation, the formal claim, and any user-provided proof sketch to output a rigorous proof package.

What proof strategies are supported for formal verification in ML foundations?▼

Formal verification in ML foundations supports common proof strategies like direct, contrapositive, and induction, extracting and clarifying hypotheses and boundary conditions to complete the argument.

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