draft — for Claude Code lean-homology, community, for Claude Code, ide skills, structure, needed, larger, result, builtin, command

v1.0.0

Sobre este Skill

Perfeito para Agentes focados em Matemática que precisam de geração de código Lean para estruturas de teorema e lema. Resumo localizado: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Recursos

Draft the theorem/lemma structure needed to prove a larger result.
Topic / proof sketch: $ARGUMENTS
Research first — search Mathlib and this project to understand what already exists.
Draft sorryd theorem/lemma structure for a larger result from a proof sketch.
Draft sorryd theorem/lemma structure for a larger result from a proof sketch

# Tópicos principais

jeffrey-dot-li jeffrey-dot-li
[1]
[0]
Atualizado: 3/5/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

Perfeito para Agentes focados em Matemática que precisam de geração de código Lean para estruturas de teorema e lema. Resumo localizado: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Por que usar essa habilidade

Habilita os agentes a gerar código Lean compilável diretamente em arquivos de origem, utilizando Mathlib e suportando estruturas de teorema e lema, permitindo o desenvolvimento e validação eficientes de provas por meio dos protocolos Lean e Mathlib.

Melhor para

Perfeito para Agentes focados em Matemática que precisam de geração de código Lean para estruturas de teorema e lema.

Casos de Uso Práticos for draft

Criar estruturas de teorema para provas matemáticas complexas
Gerar código de lema para validação e verificação
Criar código Lean compilável para integração direta em arquivos de origem

! Segurança e Limitações

  • Exige conhecimento de Lean e Mathlib
  • Limitado à geração de código Lean
  • Necessita acesso a Mathlib e recursos de projeto para pesquisa

About The Source

The section below is adapted from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Demo Labs

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ e etapas de instalação

These questions and steps mirror the structured data on this page for better search understanding.

? Perguntas frequentes

O que é draft?

Perfeito para Agentes focados em Matemática que precisam de geração de código Lean para estruturas de teorema e lema. Resumo localizado: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Como instalar draft?

Execute o comando: npx killer-skills add jeffrey-dot-li/lean-homology. Ele funciona com Cursor, Windsurf, VS Code, Claude Code e mais de 19 outros IDEs.

Quais são os casos de uso de draft?

Os principais casos de uso incluem: Criar estruturas de teorema para provas matemáticas complexas, Gerar código de lema para validação e verificação, Criar código Lean compilável para integração direta em arquivos de origem.

Quais IDEs são compatíveis com draft?

Esta skill é compatível com Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use a CLI do Killer-Skills para uma instalação unificada.

draft tem limitações?

Exige conhecimento de Lean e Mathlib. Limitado à geração de código Lean. Necessita acesso a Mathlib e recursos de projeto para pesquisa.

Como instalar este skill

  1. 1. Abra o terminal

    Abra o terminal ou linha de comando no diretório do projeto.

  2. 2. Execute o comando de instalação

    Execute: npx killer-skills add jeffrey-dot-li/lean-homology. A CLI detectará sua IDE ou agente automaticamente e configurará a skill.

  3. 3. Comece a usar o skill

    O skill já está ativo. Seu agente de IA pode usar draft imediatamente no projeto atual.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below is adapted from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

draft

Resumo localizado: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and

SKILL.md
Readonly
Upstream Repository Material
The section below is adapted from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

Draft Mode

Draft the theorem/lemma structure needed to prove a larger result.

This is NOT the builtin /plan command. The builtin /plan enters a read-only planning mode that produces a markdown plan for user approval before any code is written. /draft writes actual Lean code — sorry'd declarations that compile — directly in the source files.

Topic / proof sketch: $ARGUMENTS

Procedure

  1. Research first — search Mathlib and this project to understand what already exists.
  2. Work interactively with the user to decompose the proof into lemmas.
  3. Write all declarations with sorry proofs — no filled proofs in this mode.
  4. Each lemma should be provable independently in ~30 lines or fewer.
  5. Verify each sorry'd statement compiles with lean_diagnostic_messages before moving on.
  6. Present the full dependency structure: which lemmas feed into which.

Decomposition principle

The top-level theorem should read like a proof outline — each step composing named lemmas with simple plumbing (rw, exact, simp, apply). If the top-level proof still needs >10 lines of non-trivial tactics at any step, a lemma might be missing from the decomposition.

Prefer general, reusable lemma statements over proof-specific helpers. A good decomposition builds tools (e.g., sigmaι_cancel, sigmaι_comp_fst_eq) that apply beyond the current theorem.

Output

A compilable file (or section) of sorry'd declarations with clear names and docstrings. Iterate with the user until the decomposition is right.

Rules

  • Every declaration must compile (with sorry) after writing.
  • Use clear, descriptive names following Mathlib conventions.
  • Include /-- ... -/ docstrings explaining the mathematical content.

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