lean-prover
Skill17 starsChanged 9 months agoApache-2.0View on GitHub
A skill for using Lean 4, a modern interactive theorem prover with powerful type theory, dependent types, and mathematical structures.
In this repository98 other files it gives its agents.
- abstract-interpretation-engineSkill
- abstract-machineSkill
- acsl-annotation-assistantSkill
- actor-model-implementerSkill
- algebraic-effectsSkill
- alias-and-points-to-analysisSkill
- axiomatic-semanticsSkill
- bidirectional-type-checkingSkill
- bisimulation-checkerSkill
- capability-systemSkill
- closure-converterSkill
- common-subexpression-eliminatorSkill
- concurrency-verifierSkill
- constant-propagation-passSkill
- contextual-equivalenceSkill
- control-flow-analysisSkill
- coq-proof-assistantSkill
- cps-transformerSkill
- dafny-verifierSkill
- dataflow-analysis-frameworkSkill
- dead-code-eliminatorSkill
- defunctionalizationSkill
- denotational-semantics-builderSkill
- dependent-type-implementerSkill
- dsl-embeddingSkill
- effect-handlers-implementerSkill
- effect-systemSkill
- effect-type-systemSkill
- escape-analysisSkill
- existential-typesSkill
- ffi-designerSkill
- fuzzer-generatorSkill
- gadt-implementerSkill
- garbage-collector-implementerSkill
- graalvm-truffle-implementerSkill
- gradual-typing-implementerSkill
- higher-order-abstract-syntaxSkill
- hoare-logic-verifierSkill
- incremental-computationSkill
- information-flow-analyzerSkill
- inline-expanderSkill
- interprocedural-analysisSkill
- invariant-generatorSkill
- jit-compiler-builderSkill
- lambda-calculus-interpreterSkill
- language-server-protocolSkill
- lexer-generatorSkill
- linear-type-implementerSkill
- liveness-analysisSkill
- llvm-backend-generatorSkill
- lock-free-data-structureSkill
- loop-optimizerSkill
- loop-termination-proverSkill
- macro-expanderSkill
- memory-allocatorSkill
- message-passing-systemSkill
- mlir-dialect-designerSkill
- model-checkerSkill
- module-systemSkill
- monad-transformerSkill
- The first 60 shown.
The file
Files
Lean Prover
Domain: Proof Assistant / Formal Verification
Overview
A skill for using Lean 4, a modern interactive theorem prover with powerful type theory, dependent types, and mathematical structures.
Capabilities
- Define mathematical structures and theories
- Prove theorems interactively
- Build certified programs
- Create mathematical libraries
- Implement custom automation
Key Concepts
- Dependent Types: Types depending on values
- Lean's Type Theory: Inductive types, structures, classes
- Tactics: Proof automation and construction
- Monads: State, IO, reader monads in proofs
- Metaprogramming: Custom proof automation
Lean Features
- Extensible syntax and semantics
- Natural number game
- Mathematical library (mathlib)
- Continuous predicates
- Homotopy type theory support
Use Cases
- Formal mathematics
- Program verification
- Compiler certification
- Language meta-theory
- Mechanized proofs
Canonical References
| Reference | Why It Matters |
| Avigad, Massot, "Mathematics in Lean" | Official Lean tutorial |
| Carneiro, "The Type Theory of Lean" (2019) | Lean's type theory foundation |
| mathlib documentation | Mathematical library examples |
| Lean 4 manual | Language reference |
Research Tools & Artifacts
Lean ecosystem:
| Tool | What to Learn |
| mathlib | Mathematics library |
| Lean 4 | Implementation |
| Lean 3 | Previous version |
| Verso | Documentation tool |
Research Frontiers
1. Metaprogramming
- Goal: Custom automation and tactics
- Approach: Lean 4 metaprogramming API
- Papers: "Metaprogramming in Lean 4" (2020)
2. Automation
- Goal: More powerful proof automation
- Approach: AI-assisted proving, auto-params
- Papers: "ProofNet" experiments
Implementation Pitfalls
| Pitfall | Real Consequence | Solution |
| Proof complexity | Large proofs | Structure with lemmas |
| Universe issues | Universe level errors | Explicit universe annotations |
| Definitional vs propositional | Wrong equality type | Use appropriate eq |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

