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lean4-theorem-proving精益 4 定理证明

Agent Skill

lean4-theorem-proving 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

259

周安装

11

GitHub Stars

236

下载量

91
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:lean4-theorem-proving(精益 4 定理证明)
来源仓库:https://github.com/cameronfreer/lean4-skills
仓库路径:skills/lean4-theorem-proving
安装命令:
npx skills add https://github.com/cameronfreer/lean4-skills --skill lean4-theorem-proving
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/cameronfreer/lean4-skills --skill lean4-theorem-proving

简介

lean4-theorem-proving 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于研究检索类任务,可结合来源仓库和原始 README 核验具体用法。
  • 通过 npx skills add 命令从 GitHub 安装,需确认权限范围和维护状态。
  • 使用前建议检查是否会触发联网、命令执行或文件读写操作。
  • 当前维护状态和稳定性需结合仓库活跃度进一步确认。

SKILL.md

Lean 4 Theorem Proving

Core Principle

Build incrementally, structure before solving, trust the type checker. Lean's type checker is your test suite.

Success = lake build passes + zero sorries + zero custom axioms. Theorems with sorries/axioms are scaffolding, not results.

Quick Reference

ResourceWhat You GetWhere to Find
Interactive Commands10 slash commands for search, analysis, optimization, repairType /lean in Claude Code (full guide)
Automation Scripts19 tools for search, verification, refactoring, repairPlugin scripts/ directory (scripts/README.md)
Subagents4 specialized agents for batch tasks (optional)subagent-workflows.md
LSP Server30x faster feedback with instant proof state (optional)lean-lsp-server.md
Reference Files18 detailed guides (phrasebook, tactics, patterns, errors, repair, performance)List below

When to Use

Use for ANY Lean 4 development: pure/applied math, program verification, mathlib contributions.

Critical for: Type class synthesis errors, sorry/axiom management, mathlib search, measure theory/probability work.

Tools & Workflows

7 slash commands for search, analysis, and optimization - type /lean in Claude Code. See COMMANDS.md for full guide with examples and workflows.

16 automation scripts for search, verification, and refactoring. See scripts/README.md for complete documentation.

Lean LSP Server (optional) provides 30x faster feedback with instant proof state and parallel tactic testing. See lean-lsp-server.md for setup and workflows.

Subagent delegation (optional, Claude Code users) enables batch automation. See subagent-workflows.md for patterns.

Build-First Principle

ALWAYS compile before committing. Run lake build to verify. "Compiles" ≠ "Complete" - files can compile with sorries/axioms but aren't done until those are eliminated.

The 4-Phase Workflow

  1. Structure Before Solving - Outline proof strategy with have statements and documented sorries before writing tactics
  2. Helper Lemmas First - Build infrastructure bottom-up, extract reusable components as separate lemmas
  3. Incremental Filling - Fill ONE sorry at a time, compile after each, commit working code
  4. Type Class Management - Add explicit instances with haveI/letI when synthesis fails, respect binder order for sub-structures

Finding and Using Mathlib Lemmas

Philosophy: Search before prove. Mathlib has 100,000+ theorems.

Use /search-mathlib slash command, LSP server search tools, or automation scripts. See mathlib-guide.md for detailed search techniques, naming conventions, and import organization.

Essential Tactics

Key tactics: simp only, rw, apply, exact, refine, by_cases, rcases, ext/funext. See tactics-reference.md for comprehensive guide with examples and decision trees.

Domain-Specific Patterns

Analysis & Topology: Integrability, continuity, compactness patterns. Tactics: continuity, fun_prop.

Algebra: Instance building, quotient constructions. Tactics: ring, field_simp, group.

Measure Theory & Probability (emphasis in this skill): Conditional expectation, sub-σ-algebras, a.e. properties. Tactics: measurability, positivity. See measure-theory.md for detailed patterns.

Complete domain guide: domain-patterns.md

Managing Incomplete Proofs

Standard mathlib axioms (acceptable): Classical.choice, propext, quot.sound. Check with #print axioms theorem_name or /check-axioms.

CRITICAL: Sorries/axioms are NOT complete work. A theorem that compiles with sorries is scaffolding, not a result. Document every sorry with concrete strategy and dependencies. Search mathlib exhaustively before adding custom axioms.

When sorries are acceptable: (1) Active work in progress with documented plan, (2) User explicitly approves temporary axioms with elimination strategy.

Not acceptable: "Should be in mathlib", "infrastructure lemma", "will prove later" without concrete plan.

Compiler-Guided Proof Repair

When proofs fail to compile, use iterative compiler-guided repair instead of blind resampling.

Quick repair: /lean4-theorem-proving:repair-file FILE.lean

How it works:

  1. Compile → extract structured error (type, location, goal, context)
  2. Try automated solver cascade first (many simple cases handled mechanically, zero LLM cost)

- Order: rfl → simp → ring → linarith → nlinarith → omega → exact? → apply? → aesop

  1. If solvers fail → call lean4-proof-repair agent:

- Stage 1: Haiku (fast, most common cases) - 6 attempts - Stage 2: Sonnet (precise, complex cases) - 18 attempts

  1. Apply minimal patch (1-5 lines), recompile, repeat (max 24 attempts)

Key benefits:

  • Low sampling budget (K=1 per attempt, not K=100)
  • Error-driven action selection (specific fix per error type, not random guessing)
  • Fast model first (Haiku), escalate only when needed (Sonnet)
  • Solver cascade handles simple cases mechanically (zero LLM cost)
  • Early stopping prevents runaway costs (bail after 3 identical errors)

Expected outcomes: Success improves over time as structured logging enables learning from attempts. Cost optimized through solver cascade (free) and multi-stage escalation.

Commands:

  • /repair-file FILE.lean - Full file repair
  • /repair-goal FILE.lean LINE - Specific goal repair
  • /repair-interactive FILE.lean - Interactive with confirmations

Detailed guide: compiler-guided-repair.md

Inspired by: APOLLO (https://arxiv.org/abs/2505.05758) - compiler-guided repair with multi-stage models and low sampling budgets.

Common Compilation Errors

ErrorFix
"failed to synthesize instance"Add haveI: Instance:=...
"maximum recursion depth"Provide manually: letI:=...
"type mismatch"Use coercion: (x: ℝ) or ↑x
"unknown identifier"Add import

See compilation-errors.md for detailed debugging workflows.

Documentation Conventions

  • Write timeless documentation (describe what code is, not development history)
  • Don't highlight "axiom-free" status after proofs are complete
  • Mark internal helpers as private or in dedicated sections
  • Use example for educational code, not lemma/theorem

Quality Checklist

Before commit:

  • lake build succeeds on full project
  • All sorries documented with concrete strategy
  • No new axioms without elimination plan
  • Imports minimal

Doing it right: Sorries/axioms decrease over time, each commit completes one lemma, proofs build on mathlib.

Red flags: Sorries multiply, claiming "complete" with sorries/axioms, fighting type checker for hours, monolithic proofs (>100 lines), long have blocks (>30 lines should be extracted as lemmas - see proof-refactoring.md).

Reference Files

Core references: lean-phrasebook.md, mathlib-guide.md, tactics-reference.md, compilation-errors.md

Domain-specific: domain-patterns.md, measure-theory.md, instance-pollution.md, calc-patterns.md

Incomplete proofs: sorry-filling.md, axiom-elimination.md

Optimization & refactoring: performance-optimization.md, proof-golfing.md, proof-refactoring.md, mathlib-style.md

Automation: compiler-guided-repair.md, lean-lsp-server.md, lean-lsp-tools-api.md, subagent-workflows.md

适合场景

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需要参考平台分布和安装热度时

能力概览

能力 1

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能力 2

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

25.74%
按下载量换算23

Codex

23.56%
按下载量换算21

OpenCode

16.45%
按下载量换算15

Antigravity

11.66%
按下载量换算11

github-copilot

7.61%
按下载量换算7

Gemini CLI

3.54%
按下载量换算3

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