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

v1.0.0

Acerca de este Skill

Ideal para Agentes centrados en matemáticas que necesitan generación de código Lean para estructuras de teoremas y lemas. Resumen 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.

Características

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

# Temas principales

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

Skill Overview

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

Ideal para Agentes centrados en matemáticas que necesitan generación de código Lean para estructuras de teoremas y lemas. Resumen 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 qué usar esta habilidad?

Permite a los agents generar código Lean compilable directamente en los archivos fuente, utilizando Mathlib y dando soporte a estructuras de teoremas y lemas, permitiendo un desarrollo y validación eficiente de pruebas mediante los protocolos de Lean y Mathlib.

Mejor para

Ideal para Agentes centrados en matemáticas que necesitan generación de código Lean para estructuras de teoremas y lemas.

Casos de uso accionables for draft

Redacción de estructuras de teoremas para demostraciones matemáticas complejas
Generación de código de lemas para validación y verificación
Creación de código Lean compilable para integración directa en archivos fuente

! Seguridad y limitaciones

  • Requiere conocimiento de Lean y Mathlib
  • Limitado a la generación de código Lean
  • Necesita acceso a Mathlib y recursos del proyecto para investigación

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 y pasos de instalación

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

? Preguntas frecuentes

¿Qué es draft?

Ideal para Agentes centrados en matemáticas que necesitan generación de código Lean para estructuras de teoremas y lemas. Resumen 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.

¿Cómo instalo draft?

Ejecuta el comando: npx killer-skills add jeffrey-dot-li/lean-homology. Funciona con Cursor, Windsurf, VS Code, Claude Code y más de 19 IDE adicionales.

¿Cuáles son los casos de uso de draft?

Los casos de uso principales incluyen: Redacción de estructuras de teoremas para demostraciones matemáticas complejas, Generación de código de lemas para validación y verificación, Creación de código Lean compilable para integración directa en archivos fuente.

¿Qué IDE son compatibles con draft?

Esta skill es compatible con 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. Usa la CLI de Killer-Skills para una instalación unificada.

¿Tiene limitaciones draft?

Requiere conocimiento de Lean y Mathlib. Limitado a la generación de código Lean. Necesita acceso a Mathlib y recursos del proyecto para investigación.

Cómo instalar este skill

  1. 1. Abre tu terminal

    Abre la terminal o línea de comandos en el directorio de tu proyecto.

  2. 2. Ejecuta el comando de instalación

    Ejecuta: npx killer-skills add jeffrey-dot-li/lean-homology. La CLI detectará tu IDE o agente automáticamente y configurará la skill.

  3. 3. Empieza a usar el skill

    El skill ya está activo. Tu agente de IA puede usar draft de inmediato en el proyecto actual.

! 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

Resumen 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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