Writing Lean Proofs
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This skill provides a rigorous methodology for formalizing mathematics and software specifications in Lean 4. It emphasizes a top-down design approach where definitions and API statements are established before proofs are written. By treating statements as stable interfaces and proofs as disposable, it enables scalable, parallel development. The skill covers the entire lifecycle of a Lean project, from initial sorry-based skeleton construction and API design to mechanical verification, linter configuration, and performance optimization.
What it does
- Provides patterns for designing Lean 4 libraries and definitions.
- Outlines a top-down workflow for building proof skeletons with sorry placeholders.
- Defines best practices for proof structure, including calc blocks and focusing.
- Details strategies for mechanical verification and CI integration.
- Offers guidance on diagnosing performance issues and maxHeartbeats timeouts.
- Provides rules for writing custom linters and macros.
When to use it
- When starting a new formalization project in Lean 4.
- When refactoring long, fragile tactic proofs into maintainable lemmas.
- When designing new types or structures for a Lean library.
- When setting up CI pipelines and linters for Lean projects.
- When diagnosing slow proofs or unexpected timeouts.
Advantages
- Promotes highly maintainable and readable proof code.
- Enables parallel development through independent sorry-based work units.
- Reduces technical debt by enforcing API-first design.
- Provides clear, actionable criteria for proof extraction and refactoring.
- Ensures project stability through automated linter and axiom verification.
Usage examples
- “How should I structure a new Lean 4 library for group theory?”
- The agent will advise defining the structures first, bundling properties using FunLike or SetLike, and writing the full API (ext, simp, coercion lemmas) in the same file before implementing any theorems.
- “My proof is failing with a maxHeartbeats timeout. How do I fix it?”
- The agent will guide you to measure the cost using #count_heartbeats, suggest decomposing the goal into smaller lemmas, or optimizing the definition's reduction behavior rather than simply increasing the budget.
- “How can I ensure my Lean project is ready for multiple contributors?”
- The agent will recommend creating a sorry-based skeleton, setting up a leanblueprint for dependency tracking, and configuring a strict CI environment with project-specific linters.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/trailofbits/writing-lean-proofs/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip writing-lean-proofs.zip -d .agents/skills
Documentation by Trail of Bits
Writing Lean Proofs
Contents
When to Use
When NOT to Use
The workflow
The extraction ladder
Quick reference
Rationalizations to reject
References
Structured Lean 4 proof writing and library design, distilled from Mathlib's style and review conventions and from the methodology of large formalization projects (Liquid Tensor Experiment, PFR, Fermat's Last Theorem).
Core principle: design top-down, prove bottom-up. Lean propositions are
proof-irrelevant — only a theorem's statement can affect later declarations.
Statements are the stable interface; proofs are disposable and freely
replaceable. Put design effort into definitions and statements, then fill in
proofs against skeletons that already compile (modulo sorry).
When to Use
Proving theorems in Lean 4, from single lemmas to multi-file developments
Formalizing mathematics, protocols, or software specifications in Lean
Defining new types, structures, or functions in a Lean library
Reviewing Lean code for readability, maintainability, or Mathlib readiness
Refactoring a long or fragile tactic proof into lemmas
Setting up a formalization project that several people or agents will contribute to in parallel
Setting up CI, linters, or verification gates for a Lean project — do this at project start, before patterns propagate
Diagnosing slow proofs,
maxHeartbeatstimeouts, or expensive reductionWriting custom tactics, macros, or project-specific linters
When NOT to Use
Lean 4 as a general-purpose programming language (no proofs involved) — most of this skill targets proof and API structure
Coq, Isabelle, Agda, or Lean 3 — conventions and tactic names differ; Lean 3 idioms (
ge_or_gtlinting,discrete_field) are obsoleteVerified-software Lean projects with their own house style (e.g. spec-traceability-first codebases): Mathlib conventions are the community default, but check the project's CONTRIBUTING first and defer to it
The workflow
1. Design definitions and their API first
Definitions carry the design weight. Before proving anything about a new concept:
Prefer total functions with junk values over subtypes or
Optionin signatures (Mathlib:(0 : ℝ)⁻¹ = 0). Side conditions then appear only on the lemmas that need them, not at every use site.Bundle: new morphism kinds are structures with a
FunLikeinstance; new subobject kinds useSetLike; carry property proofs as structure fields, not separateIsHom-style predicates.Pick the canonical spelling (simp-normal form) for every concept with multiple equivalent forms, and state all API lemmas for that form only.
Write the API in the same file, immediately:
ext,@[simp], coercion, and injectivity lemmas — before the definition is used anywhere. Downstream proofs use the API, neverunfold/show ... from rfl.
See library-design.md for the full set of design rules with rationale.
2. Build a sorry skeleton
State everything before proving anything, at every scale:
Project scale: state the target theorem and the lemmas it needs, all with
:= sorry, and make the file compile. Eachsorryis now an independent work unit — a contributor (human or LLM) can discharge one without understanding the rest. This is how LTE, PFR, and FLT scale to dozens of parallel contributors.Proof scale: inside a proof, lay out the
have/suffices/calcskeleton withsorryjustifications, get Lean to accept the structure, then fill each step. Keeping the structure intact is what produces useful error messages while you work.
example (a b c d : ℝ) (h : c = d * a + b) (h' : b = a * d) : c = 2 * a * d := by
calc
c = d * a + b := sorry
_ = d * a + a * d := sorry
_ = 2 * a * d := sorry3. Fill goals, one focused goal at a time
Every new subgoal gets a focusing dot
·with an indented block — never leave several goals active in unfocused sequence (Mathlib'smultiGoallinter enforces this). This is what kills fragile goal-ordering dependence.Open each block with a redundant
showstating its goal. The proof works without it; reviewers and future editors need it. Ifshowwould change the goal, usechangeinstead — keep stated goals honest.Chained rewrites of (in)equalities become
calcblocks, relations aligned vertically.havefor forward stepping stones ("we first establish X");sufficesfor backward reduction ("it suffices to show X").While drafting, annotate the goal state as a comment before non-obvious tactics — emitted by Lean, never imagined. In a headless workflow, insert
trace_stateat the point of interest or a deliberatedonewhere goals should be closed, then runlake env lean Path/To/File.lean; copy the reported hypotheses, case name, and target. Strip routine probes after the proof works. This is the single most effective technique for LLM-written proofs (see llm-techniques.md).
See proof-style.md for the full tactic-style rules, and naming-conventions.md for naming lemmas so their names are guessable from their statements.
4. Verify mechanically
Do not eyeball-check style — run the checkers. lake build is the floor,
and it is only the floor: sorry is a warning, so a green build exits 0
with sorries still present.
Gate unproved obligations by asking the kernel, never by grepping.
#print axioms myTheoremfor a spot check; for CI, collect axioms per declaration withLean.collectAxiomsand assert the whole expected footprint ([propext, Classical.choice, Quot.sound]unless deliberately widened), so a straysorryor a new trust assumption likenative_decidefails loudly. Grep is wrong in both directions: it matches the word in comments, and it misses a theorem whose own text is clean but which applies an unproved helper. Working script in linting.md.Choose lints by project role and put them in CI at project start. Do not enable
linter.mathlibStandardSetwholesale in a downstream project: it combines proof-maintenance checks with public-API checks, house style, and Mathlib-specific repository policy. For a self-contained proof, start withlinter.auxLemma,linter.style.maxHeartbeats,linter.style.multiGoal,linter.style.setOption, andlinter.style.show. A reusable library should additionally enablelinter.flexible,linter.style.missingEnd,linter.style.openClassical, and the twounused*InTypechecks. TreatnativeDecideas a trust-policy choice and formatting or deprecated-syntax checks as project style. No warning gates anything unless warnings fail the build. Run Batteries' declaration-level#lintchecks, includingsimpNF, separately. Verify every option against the pinned Mathlib source and with a known-trigger fixture: a misspelledweak.option is intentionally ignored. The complete 26-member audit and lakefile profiles are in linting.md.Write a custom linter for every project-specific convention (simp-set discipline, summary-lemma coverage, required attributes) — a declaration-level
@[env_linter]is one structure, and it is the only thing that reliably catches "the attribute is missing on 29 of 30 declarations". See linting.md for the recipe and the engineering rules (vacuity anchors, prove-it-can-fail, allowlists).
The extraction ladder
When does proof structure graduate into separate lemmas?
Before extracting, state the fragment's type and search by shape. Put the proposed statement in a scratch
example, runexact?andapply?on the bare goal, then try a type-pattern and source search. If an existing theorem fits, use it. Do not report an API gap without recording the searches that failed.A sub-argument repeats within one proof → name it as a local
have.theorem min_comm (a b : ℝ) : min a b = min b a := by have h : ∀ x y : ℝ, min x y ≤ min y x := by intro x y apply le_min · show min x y ≤ y exact min_le_right x y · show min x y ≤ x exact min_le_left x y apply le_antisymm · show min a b ≤ min b a exact h a b · show min b a ≤ min a b exact h b aThe statement is independently interesting, or extraction sheds hypotheses the sub-argument does not need → standalone lemma. Dropping unneeded hypotheses is the stronger trigger: the extracted lemma becomes more general than the proof it came from.
The proof reads as "long and unwieldy" → split it. This is Mathlib's review criterion, and it is deliberately qualitative — there is no line threshold. Resolve doubt by attempting the extraction: if a fragment has a clean statement, it wanted to be a lemma.
Quick reference
| Rule | Why | Enforced by |
|---|---|---|
Never unfold definitions downstream; erw or trailing rfl = missing API | API lemmas are the abstraction boundary | review ("missing API" smell) |
Terminal simp stays unsqueezed; non-terminal simp becomes simp only [...] | squeezed terminal calls bury the key lemmas and break on renames | style guide |
One focused goal at a time (· blocks) | kills goal-ordering fragility | linter.style.multiGoal |
show must not change the goal (use change) | stated goals stay honest | linter.style.show |
No set_option debug/trace/profiler or unscoped maxHeartbeats in final code | debugging scaffolding | linter.style.setOption |
State lemmas in simp-normal form, < not > | simp matches syntactically | simpNF linter |
| Golf only when the result is at least as readable; trivial results exempt | short ≠ better | review |
Fact instances are local, never global | global instances degrade all typeclass search | review |
| Name lemmas from their statements (see naming reference) | names become guessable without search | linter.style.nameCheck catches only __; #lint defsWithUnderscore and review cover more |
| Search a bare goal by shape before writing a helper or claiming an API gap | names are not always guessable from the target | exact?, apply?, type/source search |
| Generally one tactic invocation per line; a one-line closing proof is the exception | preserves readable proof structure without inventing an absolute rule | style guide |
Gate sorry with collectAxioms/#print axioms, never grep | grep matches comments, misses unproved helpers | axiom audit in CI |
| Prefer simp-lemma LHSs keyed on structure, not numerals; one spelling per constant | 2 ^ 32 never matches a goal normalized to 4294967296 | simpNF, review |
Re-derive every simp only list with simp? at its own site | lists do not transfer between look-alike goals | linter.flexible |
Every maxHeartbeats override is an unproven claim — measure before believing | copy-pasted budgets carry no information | #count_heartbeats, bisection |
Conditional simp lemma fires shallow but not deep → raise maxDischargeDepth (default 2) | chained side conditions truncate silently, no diagnostic | diagnosis (proof-style, simp discipline) |
| Every project-specific convention gets a custom linter, in CI from day one | review misses the 29-of-30 failure mode | @[env_linter] + #lint |
Full rationale for each row, plus the library-level anti-patterns, in anti-patterns.md.
Rationalizations to reject
| Excuse | Reality |
|---|---|
| "The proof compiles, ship it" | Compiling is the floor. A monolithic tactic block that only Lean can read will break silently at the next Mathlib bump and no one will be able to repair it. |
| "Unfolding the definition is simpler than writing API lemmas" | Every downstream unfold couples a proof to the implementation. The first refactor breaks all of them at once. Write the missing lemma. |
| "Squeezing every simp makes the proof faster and more robust" | Backwards for terminal simp calls: the squeezed list breaks on every rename and drowns the signal. Squeeze non-terminal calls only. |
| "It's shorter, therefore better" | Mathlib review policy: golfing is fine only when it does not sacrifice readability. Length is not the target; legibility is. |
| "I'll restructure it into lemmas after it works" | After it works, the structure is load-bearing and tangled. State the skeleton first; the lemmas fall out for free. |
"Adding show lines is redundant noise" | They are redundant to the kernel and essential to every human or model that reads the proof next. |
| "This helper is too specific to be a lemma" | If it has a clean statement, extract it — dropping the hypotheses it doesn't need usually reveals it was general all along. |
| "We'll add linters once the library stabilizes" | Backwards: patterns propagate by copy-paste, so a deferred linter meets a 400-warning backlog instead of one bad line. Enable what is already clean and gate it now. |
| "The check passed, so we're clean" | A check that can't fail proves nothing — sweeps reach zero files, misspelled weak. options are ignored, pipelines swallow exit codes. Prove every gate can fail before trusting that it passes. |
| "The proof is slow, raise maxHeartbeats" | An unmeasured budget is a claim, not a fix — and it masks the regression the next reader needs to see. Measure with #count_heartbeats; restructure the definition or decompose the goal. |
References
library-design.md — definitions, APIs, bundling, abstraction boundaries, spec-driven project decomposition
proof-style.md — tactic proof structure: calc, have/suffices, focusing, and simp discipline including the why-doesn't-this-lemma-fire diagnoses (discharge depth, traversal order, numeral spellings)
naming-conventions.md — Mathlib naming so lemma names are computable from statements
anti-patterns.md — recognized anti-patterns, why each is harmful, and which linter catches it
llm-techniques.md — evidence-based techniques specific to LLM-written proofs
linting.md — axiom-based sorry gates, enabling project-specific linter profiles in CI early, the full Mathlib standard-set audit, adopting linters with a backlog, writing custom linters for project-specific constructs, and proving every gate can fail
performance.md — measuring per-declaration cost, where reduction cost comes from, optimizing definitions without losing semantics
tactics.md — metaprogramming discipline: extension-point selection, metavariable and recovery safeguards, bounded search, actionable errors, structured tracing, generated declarations, and failure-surface testing
Files
- ATTRIBUTION.mdAdded by AutoGineer951 B
- LICENSELicence file added by AutoGineer19.7 KiB
- SKILL.md15.6 KiB
- references/anti-patterns.md6.9 KiB
- references/library-design.md7.7 KiB
- references/linting.md22.1 KiB
- references/llm-techniques.md5.4 KiB
- references/naming-conventions.md2.5 KiB
- references/performance.md5.3 KiB
- references/proof-style.md7.8 KiB
- references/tactics.md16.1 KiB
references/library-design.md
Library design: definitions, APIs, and project decomposition
How Mathlib and the large formalization projects structure theory development. Sources are Mathlib's style and PR review guides, the Mathlib papers (CPP 2020; van Doorn–Ebner–Lewis, CICM 2020), the perfectoid spaces retrospective (Buzzard–Commelin–Massot, CPP 2020), and Commelin–Topaz, "Abstraction boundaries and spec driven development in pure mathematics" (Bull. AMS, from the Liquid Tensor Experiment).
Spec-driven development
Decompose before proving. The LTE methodology, recursively:
Isolate a target (definition or theorem).
Write its spec: the statement, plus the API lemmas it should satisfy.
Break both into lower-complexity parts.
Recurse, using
sorryas a placeholder for both data and proofs.
State API lemmas before the definition exists:
def condensedAb : Type := sorry -- data placeholder
lemma val_app_add (f g : condensedAb) : val (f + g) = val f + val g := sorryNobody can depend on an implementation detail, because there is no implementation yet. Collaborators immediately build on the sorried assertions in parallel and fill targets independently. Tao's PFR project formalized a 33-page proof with ~20 collaborators in three weeks this way; Tao: formalization "allows for individual subtasks in the project to be precisely defined and verified independently of the other subtasks", so projects "routinely involve scores of people who may have had no prior interaction". Buzzard (FLT): "you do not have to understand the whole proof of FLT in order to contribute."
For multi-contributor projects, consider a
leanblueprint — a
human-readable outline whose \uses{...} annotations generate the
dependency graph and whose \lean{...}/\leanok links track formalization
status per node. No source prescribes node size; the working criterion is
that one contributor can complete one node without global context.
Statements are the interface; proofs are disposable
Lean propositions are proof-irrelevant: only a theorem's statement can affect later declarations. Consequences:
Design effort concentrates on definitions and statements. Mathlib requires doc strings on definitions but not on ordinary theorems — statements are self-documenting; definitions need justification.
Refactoring a proof is always safe; refactoring a statement or definition is a breaking change. Get statements right first (the sorry skeleton).
Every definition ships its API, immediately, in the same file
Before a definition is used anywhere, write:
the
@[simp]lemmas that compute with it in its canonical form,an
@[ext]lemma (stated partially applied — Mathlib's "partially-applied ext lemmas" convention — so later ext lemmas compose step-wise),coercion/
FunLikelemmas, injectivity lemmas, and the rewrite lemmas that let users avoid definitional reasoning.
Explain the design decisions (typeclass choices, simp-normal form) in the module docstring.
The smell test: needing erw, or a trailing rfl after simp/rw, is
the style guide's official signal of missing API. The fix is a new lemma,
never unfolding. Do not confuse this with the API lemmas themselves: an
@[simp] projection lemma proved by rfl next to its definition is the
boundary working as intended — the smell is a downstream proof needing
rfl to see through the definition.
Abstraction boundaries are explicit decisions
Definitions default to semireducible transparency; any deviation must be justified (in Mathlib: in the PR description).
A sealed boundary is a one-field structure wrapper, not
irreducible:structure MyDef where underlying : UnderlyingTermirreducible_defonly with a documented profiling reason.
Optimized definitions keep a readable reference
When a definition must be rewritten for elaboration or reduction speed, do
not replace it — keep the readable definition as the reference semantics,
add the fast form beside it, and prove them equal for every input
(including degenerate and failure cases). Downstream proofs keep using the
readable form and repoint with a single rw where they touched the old
shape; the optimization cannot silently change meaning; and the intended
semantics stay legible. This is "statements are the interface" applied to
definitions under optimization. Mechanics and the reduction-cost diagnosis
that motivates it: performance.md.
Simp-normal form
When a term has multiple equivalent spellings, designate one canonical form
— prefer the generic one (‖x‖ over a concrete padicNorm, < over >) —
orient @[simp] lemmas to rewrite toward it, and state every subsequent API
lemma only for it. The simplifier matches up to syntactic equality; without
a normal form, every lemma needs restating for every spelling. The simpNF
linter checks that simp lemma left-hand sides are themselves in normal form.
Total functions with junk values
Prefer totalizing a function with a junk value over restricting its domain:
in Mathlib, (0 : ℝ)⁻¹ = 0 and division is total. The payoff is
modularity: the x ≠ 0 side condition disappears from every use site, and
hypotheses appear only on the lemmas that genuinely need them —
theorem div_add_div_same (a b c : α) : a / c + b / c = (a + b) / c := ...
-- unconditional: holds also when c = 0, both sides are junk
theorem mul_inv_cancel₀ (h : a ≠ 0) : a * a⁻¹ = 1 := ...
-- the hypothesis lives only where the mathematics requires itThe perfectoid-spaces authors tried the alternative (a bundled subtype of units) and reported that it made "every step slightly painful, because inclusions are harder to ignore in formalised type theory."
Bundling
Morphisms: define a bundled structure plus a
FunLike-style class, never anIsHom fpredicate — instance search cannot reliably solve goals likeIsHom (f ∘ g), while a bundledHomtype gets its own composition and its own algebraic structure.structure MonoidHom (M N : Type*) [Monoid M] [Monoid N] where toFun : M → N map_one' : toFun 1 = 1 map_mul' : ∀ a b, toFun (a * b) = toFun a * toFun bSubobjects: bundled carrier +
SetLikeinstance.Type classes: semi-bundled — bundle all operations, leave only the carrier type as a parameter (
Monoid M, not fully-bundledMonoidnor unbundledIsMonoid M mul one).
Restraint on new abstractions
Introduce a new algebraic typeclass only when there is real mathematics to do with it, or a genuine simplification from factoring out a shared substructure. Prefer
extendsover mixin parameters.Factbridges a proposition into typeclass search locally:theorem foo (p : ℕ) (hp : p.Prime) : ... := by have := Fact.mk hp -- local instance, scoped to this proof ...Never declare global
Factinstances — they degrade instance search everywhere.
Choosing the abstraction level is the dominant proof-shortener
Work at the highest level that expresses the mathematics. The perfectoid retrospective's example: stating uniform continuity via filters instead of unfolding to sets of pairs "allows to break the proofs into small lemmas that are needed anyway", collapsing "uniformly continuous implies continuous" to roughly twice the length of the sentence "this follows immediately from definitions".
When informal mathematics silently identifies isomorphic constructions, don't fight the identification — introduce a predicate characterizing the object by its properties (the "abstract completion" move) and prove your theorems for anything satisfying the predicate.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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