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

v1.0.0

このスキルについて

数学に焦点を当てたエージェントが、定理と補題の構造に対するスリムなコード生成を必要とする場合に最適です。 ローカライズされた概要: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

機能

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

# Core Topics

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

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
8/11
Quality Score
44
Canonical Locale
en
Detected Body Locale
en

数学に焦点を当てたエージェントが、定理と補題の構造に対するスリムなコード生成を必要とする場合に最適です。 ローカライズされた概要: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

このスキルを使用する理由

Mathlibを利用し、定理と補題の構造をサポートして、エージェントが直接ソースファイル内でコンパイル可能なLeanコードを生成できるようにし、LeanとMathlibのプロトコルを通じた効率的な証明開発と検証を可能にします。

おすすめ

数学に焦点を当てたエージェントが、定理と補題の構造に対するスリムなコード生成を必要とする場合に最適です。

実現可能なユースケース for draft

複雑な数学的証明のための定理構造のドラフト作成
検証とバリデーションのための補題コードの生成
ソースファイルへの直接統合のためのコンパイル可能なLeanコードの作成

! セキュリティと制限

  • LeanとMathlibの知識が必要
  • Leanコード生成のみ
  • 研究のためのMathlibとプロジェクトリソースへのアクセスが必要

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

FAQ & Installation Steps

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

? Frequently Asked Questions

What is draft?

数学に焦点を当てたエージェントが、定理と補題の構造に対するスリムなコード生成を必要とする場合に最適です。 ローカライズされた概要: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

How do I install draft?

Run the command: npx killer-skills add jeffrey-dot-li/lean-homology. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for draft?

Key use cases include: 複雑な数学的証明のための定理構造のドラフト作成, 検証とバリデーションのための補題コードの生成, ソースファイルへの直接統合のためのコンパイル可能なLeanコードの作成.

Which IDEs are compatible with draft?

This skill is compatible with 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 the Killer-Skills CLI for universal one-command installation.

Are there any limitations for draft?

LeanとMathlibの知識が必要. Leanコード生成のみ. 研究のためのMathlibとプロジェクトリソースへのアクセスが必要.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add jeffrey-dot-li/lean-homology. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use draft immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

draft

ローカライズされた概要: Draft sorryd theorem/lemma structure for a larger result from a proof sketch. This AI agent skill supports Claude Code, Cursor, and Windsurf

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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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