Writing Lean Proofs
trailofbits/skills
Structures Lean 4 proofs and library design along Mathlib conventions, from stating theorems to refactoring long tactic proofs and fixing slow or timing-out ones.
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep lean-formalize --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/lean-formalize .claude/skills/lean-formalize && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "lean-formalize" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/lean-formalize into .claude/skills/lean-formalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean-formalize", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/lean-formalizeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep lean-formalize --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/lean-formalize .agents/skills/lean-formalize && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "lean-formalize" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/lean-formalize into .agents/skills/lean-formalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean-formalize", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep lean-formalize --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/lean-formalize .cursor/skills/lean-formalize && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "lean-formalize" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/lean-formalize into .cursor/skills/lean-formalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean-formalize", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git --path skills/lean-formalize--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep lean-formalize --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/lean-formalize .gemini/skills/lean-formalize && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "lean-formalize" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/lean-formalize into .gemini/skills/lean-formalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean-formalize", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep lean-formalizeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/lean-formalize .github/skills/lean-formalize && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "lean-formalize" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/lean-formalize into .github/skills/lean-formalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean-formalize", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep lean-formalize --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/lean-formalize .opencode/skills/lean-formalize && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "lean-formalize" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/lean-formalize into .opencode/skills/lean-formalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean-formalize", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
lean-formalizeDevelop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement.
Lean Formalize is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep. Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific proof obligation benefits from formal verification; use proof-writer for ordinary mathematical drafting.
Its SKILL.md is about 5.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/adversarial-review.md`, `references/lean-working-loop.md` and `references/verification-and-handoff.md`).
The repository describes itself as: ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework… The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 26b95cf. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
Bash(*)ReadWriteEditGrepGlobAgentSkillmcp__codex__codexmcp__codex__codex-replyFrom allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Lean Formalize loads about 5.4k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 2,799 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, mcp__codex__codex, mcp__codex__codex-replyAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from wanshuiyin/Auto-claude-code-research-in-sleep at commit 26b95cf, republished under its MIT licence (© wanshuiyin). 2,799 words, ~5,397 tokens.
.claude/skills/lean-formalize/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Turn the user's mathematical statement into a checked Lean theorem with its meaning preserved. A compiled conditional lemma is progress; completion concerns the original statement and the trust basis actually used.
Use this skill when the user requests Lean, when continuing an existing Lean
proof, or when formal verification addresses a concrete uncertainty in a central
claim—for example, a long dependency chain, a delicate reduction, or coverage of
a finite classification. State the obligation it will help resolve and proceed
within the authorized task. Difficulty alone is not a reason to formalize.
Ordinary derivations and short proofs can stay in formula-derivation or
proof-writer; do not make Lean a prerequisite for every mathematical result.
Respect the user's chosen proof method and the scale of the requested work.
Original statement and Lean definitions
→ A: cross-family adversarial statement alignment
→ Proof obligations, representations, and lemma interfaces
→ Lean implementation ↔ B: adversarial review of key arguments and connections
→ Actual inputs connected; original theorem assembled
→ Executed type, definition, and transitive-axiom audit
→ C: cross-family adversarial review of the final exported result
→ Reproducible delivery and research-state updateFor substantial new proof projects, A/B/C are part of the workflow. For a continuation, reuse completed checks on unchanged claims and revisit affected ones. Small routine formalizations need checks proportional to the actual claim; an explicit user request for cross-family review still applies to them.
Use the authorized reviewer families available in the current host. If the user specifies both Grok and Gemini, obtain and record both; a same-family agent or another provider does not silently satisfy either request. Unavailability leaves that checkpoint pending while independent proof work continues. A checked theorem and a fully completed requested review workflow are separate deliverables.
Follow the requested scope: implement, continue, audit, or package. For an audit, inspect and report rather than silently repairing or weakening the theorem. For implementation, develop the mathematical argument as well as its Lean proof: search relevant literature or library results, explore alternative routes, and discharge the missing lemmas. Continue through the remaining interfaces and top-level assembly; do not stop at a target declaration or the first successful compilation.
Read the authoritative statement, existing Lean entry point, toolchain and lockfile, and latest progress record. Reuse the project and its document names. Do not reinstall tools, change dependencies, or start a new proof framework when the existing environment is suitable. Resolve APIs against the pinned library.
Use the existing proof route when it works. If the mathematical argument itself
is missing, isolate that obligation and use proof-writer or ordinary proof work.
A delegated proof-writer task develops that argument and returns its proof or
remaining gap to this run; it must not invoke lean-formalize again. Syntax
automation cannot discharge an unproved premise. Do not promise that an arbitrary
open problem can be formalized or solved.
| Current input | First useful action |
|---|---|
| Only a mathematical statement | Fix definitions and quantifiers, then develop a proof route and its first difficult obligation |
| A prose proof | Identify nontrivial dependencies and choose Lean representations; expose any missing argument before coding it |
| A partial Lean project | Inspect the target and its actual callers, read the last useful build/error record, and continue at the highest unclosed connection |
| An audit request | Read definitions and exported types, run applicable checks, and report; do not silently repair the target |
| A completed proof to hand off | Check the current entry point and evidence, then prepare portable sources and commands without restarting the mathematical search |
Name the intended main module, exported declaration, and current next obligation early. If no complete mathematical route is known, say which statement is being attempted; do not mark it provable merely because the implementation has started. Tool/API problems and missing mathematical arguments require different next steps.
Record a short mathematical specification, or reference the existing one:
Separate original hypotheses from properties introduced by a reduction. Identify where finiteness, nonemptiness, decidability, normalization, and index conventions change the statement or require a bridge. Check plausible vacuity and quantifier failures in the actual theorem, rather than inventing unrelated edge cases.
Keep the user's original statement as the comparison baseline until the user changes the goal. A working specification rewritten to match the implementation does not change that baseline. Record authorized scope changes explicitly; do not request confirmation again for a change already authorized in the session. If a repair yields only a stronger assumption or weaker conclusion, identify the proved variant and the original obligation still open. A stronger proved result can establish the original claim when its implication is supplied.
Run checkpoint A on the actual definition bodies and proposed target. Ask for independent back-translation before comparison with the original mathematics. The input packet and prompt are in references/adversarial-review.md. A reviewer who saw only the intended prose has not checked the encoding.
Map the path from an arbitrary original input to the conclusion. For every substantial interface, record:
| Declaration / obligation | What it assumes | Where actual inputs come from | Evidence / remaining gap |
|---|---|---|---|
| A conditional result | Its extra hypotheses | A named construction or theorem from the original input | Actual state |
Prioritize the highest unclosed connection. In particular, distinguish:
All three may require separate proofs. A structure that stores its desired properties as fields, a supplied probability bound, or an assumption equivalent to the conclusion does not remove the obligation to construct that input.
Use conditional lemmas as development interfaces, explicitly marked as such.
Keep incomplete experiments outside the certified target's dependency chain;
any temporary sorry remains visible as unfinished work and cannot survive the
final target audit. Prefer enough intermediate compilation to localize errors,
without interpreting file counts or proved arithmetic statements as completion.
Split parallel work along stable lemma signatures and module ownership. Give each worker its assumptions, conclusion, dependencies, and concrete compile target. Integrate its result against the actual caller before closing the ledger. Do not let parallel workers silently redefine shared objects to suit their proofs.
Read references/lean-working-loop.md when implementing or repairing an obligation. It develops the search → actual-caller experiment → diagnostic → repair cycle, including representation choices and performance problems, with a compiled library-application example.
#check; use existing equivalent representations
when they simplify a real bottleneck.For example, a theorem of type Certificate x → Desired x is useful only after
the project constructs Certificate x for every original input it needs. Closing
that construction and connecting the caller is a distinct result from proving
the conditional theorem. Kernel checking does not discharge a parameter merely
because its type has a reassuring name.
Do not use a timer, repeated unchanged builds, or repeated model calls as a proxy for progress. After a concrete failure, try another justified representation or proof path; preserve the exact blocker if the requested work cannot yet finish. Distinguish an implementation failure from a failed intermediate claim and an obstruction to the whole method. Retire an unsuccessful route with its reason; rejecting that route does not refute the original theorem. Parallel exploration is most useful when the proposed approaches can fail for different reasons.
Distinguish finite examples, exhaustive computation over a proved domain, checked certificates, and symbolic/general proof. A bounded search is evidence about its searched inputs. Exhaustive finite verification can be a proof when coverage, encoding correspondence, and the verification procedure are established and the user's constraints permit it. For an end-to-end Lean result, the coverage and interpretation must themselves lie in the proved dependency chain under the declared foundations. A comment claiming that a finite list is exhaustive does not turn checked list entries into a universal Lean theorem.
For numeric arguments, use exact arithmetic where the claim needs exactness. Prove the connection between actual objects/events and the finite data before using the numeric conclusion. Document conventions that matter: ordered versus unordered pairs, multiplicities, indices, zero cases, and rounding.
Agree on the intended trust basis from the specification and project conventions.
Do not silently introduce axioms or compiler-backed computation to make an
otherwise incomplete kernel-level proof appear complete. Conversely, do not turn
one project's prohibition on enumeration or native_decide into a universal
ban. Explain any extra trust assumption and whether it satisfies this task.
Run A, B and C as described in the core workflow. Read references/adversarial-review.md for stage triggers, source packets, adversarial prompts, objection handling, resumption and stopping rules. These are mathematical checks, not votes on whether to trust the executor's summary.
Use proof-checker for the existing ARIS proof-audit/submission role. This skill
does not replace its artifact or reviewer contract. When invoked by a parent
proof audit, return the checked scope and evidence to that run; do not recursively
invoke proof-checker. A standalone Lean task does not automatically require a
separate paper-audit workflow. Where installed, use
research-review / auto-review-loop for targeted discussion and repair, with
the current host's reviewer routing.
For direct model consultations, use available authorized tools and their actual contracts. A Grok or Antigravity MCP consultation is not automatically an ARIS reviewer overlay. Do not hard-code vendor availability, quotas, retry counts, proxy settings, or a particular model into the portable workflow.
Ask concrete questions: translate this declaration; discharge these caller hypotheses; find a counterexample to this new lemma; check this reduction's coverage. Preserve raw responses, actual reading/execution scope, and the executor's disposition of each issue. Model agreement is not a proof. Same-family opinions remain provisional; cross-family opinions are additional review evidence, not mathematical truth certificates.
An unread artifact, an unanswered question, a failed call or an empty response provides no substantive review verdict. A plan review is not a final-code review; reading existing logs is not executing a build. Preserve which claim/version was actually examined. Retry only within current authorization and provider rules; do not rewrite failures as passes after a later success.
Close objections with a specific derivation, verified witness, correction or justified rejection. Preserve unresolved disputes. Apply an invoked ARIS loop's own round/continuation contract; do not create an outer polling loop around it. For direct consultations, re-review changed obligations and stop when settled or when a real availability/budget limit is reached. Unresolved mathematics remains open; an unavailable requested review remains pending. Neither a reviewer score nor exhaustion of rounds is a proof-completion rule.
Read the local host's reviewer routing for its current model/effort policy and tool contracts. Use the A/B/C mathematical packets in this skill; they do not require a paper, publication score, or a new review service. For delegated proof work, follow the applicable fan-out convention, assign stable lemma interfaces, and integrate each result at its real caller.
On Claude Code, use the host's file/shell tools for Lean and Agent for useful
parallel proof work. The default reviewer is Codex MCP: start with
mcp__codex__codex, an explicit project cwd, read-only sandbox, and the
model/effort pair resolved from the routing reference. Continue that review with
mcp__codex__codex-reply using its saved session identity and prompt. Start fresh
contexts for A and C; B repair checks may continue the reviewer that found the
issue. Verify the executor/reviewer families rather than assuming that every
host running this mainline skill is Claude.
Honor an explicitly selected authorized reviewer. Direct Claude, Grok and Gemini transports have different file-access and continuation contracts; read review transport notes when using them. These Lean consultations do not change another skill's reviewer backend or its acceptance rules.
Use references/verification-and-handoff.md when preparing the final build, trust audit, or portable package.
For completed delivery, establish the applicable evidence below. Keep a proved theorem distinct from any still-pending reproduction or review requirement:
theorem, lemma, or def may supply a proof term of
the target proposition. Merely defining that proposition, proving P → P, or
proving an unrelated proposition does not discharge the original target.sorryAx or unapproved extra axiom lies on the target's
dependency chain. Explain the actual dependencies against the intended basis;
the required list need not be exactly any fixed set of axiom names.These checks establish the formal result under its stated foundations. A prose proof or HTML edition requires its own correspondence check; Lean does not certify every sentence in a separate exposition. An explicitly required import audit, external reproduction, or review may remain pending even after the formal theorem is checked. Report that missing evidence as such, not as a newly discovered mathematical gap.
An illustrative audit can distinguish available_interface : Target → Target
from original_proof : Target, even if both compile without extra axioms. The
first leaves the original claim open; the second supplies its proof when Target
matches the user's statement. A failed reviewer call changes neither type.
Reuse the existing specification, proof ledger, and continuation file. A short progress report plus Lean sources, lockfiles, an audit entry point, and logs is usually enough. Add new files only for a concrete reader or execution need.
Report distinct facts rather than a single ambiguous PASS:
For unfinished work, save the smallest remaining mathematical/Lean obligation, its known dependencies, the last useful error, and the next action. For completed work, record completion so the next session does not revive obsolete gaps.
Use a compact continuation note rather than a second state system:
Original target and source:
Current exported declaration / main module:
Closed connections and actual build evidence:
Next unclosed obligation or completion:
A/B/C reviews: source scope, reviewer, raw record, remaining issue:
Next action:Resolve current state from actual artifacts, not an old narrative saying either “finished” or “not finished.” On resume, check what changed before repeating work. Do not merge historical and current reviewer scopes into a larger claimed audit.
If a research wiki is active, update the affected claim through the existing
research-wiki / proof-checker workflow with precise evidence and scope; do not
overwrite its generated graph or invent a second acceptance format. Literature
search should resolve a mathematical gap, API question, or relevant prior result,
not become an unrelated requirement to exhaust the literature.
For handoff, provide a reader-oriented proof, source, pinned dependencies,
independent audit command, and known trust boundary. Use render-html when a
single-file reading edition would help. Mathematical source changes require
appropriate rebuild and statement/axiom checks. A theorem or hypothesis changed
in Markdown or HTML is also a claim change: redo alignment and affected requested
review. Layout or wording that preserves the claim needs presentation checks.
Keep change histories in the research record and present the finished proof
directly. Do not restart completed proof work solely to satisfy a workflow ritual.
© wanshuiyin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (references) in skills/lean-formalize of wanshuiyin/Auto-claude-code-research-in-sleep.
Open the folder on GitHubat commit 26b95cf
Lean Formalize next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Lean Formalize this skillwanshuiyin/Auto-claude-code-research-in-sleep | 17k | — | ~5.4k | Automated safety check: Notes | MIT | |
| Writing Lean Proofstrailofbits/skills | 7.5k | — | ~4k | Automated safety check: Pass | CC-BY-SA-4.0 | |
| Continuetelegramdesktop/tdesktop | 33k | 2 repos | ~9.4k | Automated safety check: Pass | GPL-3.0 | |
| Math Formalizationtradecatlabs/vibe-coding-cn | 17k | — | ~717 | Automated safety check: Pass | MIT | |
| MCP Developmentcoollabsio/coolify | 63k | 1 repos | ~949 | Automated safety check: Pass | MIT | |
| Game Developmentsickn33/agentic-awesome-skills | 47k | 1 repos | ~1.3k | Automated safety check: Pass | MIT |
trailofbits/skills
Structures Lean 4 proofs and library design along Mathlib conventions, from stating theorems to refactoring long tactic proofs and fixing slow or timing-out ones.
telegramdesktop/tdesktop
Continue autonomous Telegram Desktop development from the shared ai-tdesktop repository.
tradecatlabs/vibe-coding-cn
Turns a mathematical claim into a small Lean 4 and Mathlib formalization checked by the proof assistant kernel, and refuses to report a pass without real evidence.
coollabsio/coolify
A skill your agent uses for Laravel MCP development. An agent skill from coollabsio/coolify.
sickn33/agentic-awesome-skills
Game development orchestrator. An agent skill from sickn33/agentic-awesome-skills.
openclaw/openclaw
Add subtitles, captions, narration cues, or zoom to a proof video or PR recording using repo-local capture helpers and a system ffmpeg renderer.
wanshuiyin/Auto-claude-code-research-in-sleep
Builds an academic conference poster as a single HTML and CSS file with measurement-based gates, real paper figures and a print-ready PDF rendered through headless Chromium.
wanshuiyin/Auto-claude-code-research-in-sleep
Runs a mathematical proof project as a stateful pipeline of run directories: a local attempt first, then a manual GPT Pro handoff package, with an optional DeepSeek audit.
wanshuiyin/Auto-claude-code-research-in-sleep
Render an ARIS Markdown / JSON artifact (IDEAREPORT, AUTOREVIEW, KILLARGUMENT, PAPERPLAN, research-wiki state, etc.) into a single-file HTML view designed for human reading.
wanshuiyin/Auto-claude-code-research-in-sleep
Audit experiment integrity before claiming results. An agent skill from wanshuiyin/Auto-claude-code-research-in-sleep.
wanshuiyin/Auto-claude-code-research-in-sleep
Run the Anti-Autoresearch integrity-forensics DETERMINISTIC slice (numeric core + rules-only reporter) against a paper via a SHA-pinned thin launcher, then convert the verdict into a typed policy…
wanshuiyin/Auto-claude-code-research-in-sleep
Generate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic — formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题 (L1 必会 / L2 进阶 / L3 顶级 lab).
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Lean Formalize is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep. Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement.
Lean Formalize fits situations like: lean is requested; A specific proof obligation benefits from formal verification; use proof-writer for ordinary mathematical drafting.
Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a claude-code`. Or copy the skill folder (skills/lean-formalize in wanshuiyin/Auto-claude-code-research-in-sleep) into .claude/skills/lean-formalize in your project. Claude Code loads it when a task matches its description.
Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a codex`. Or copy the skill folder (skills/lean-formalize in wanshuiyin/Auto-claude-code-research-in-sleep) into .agents/skills/lean-formalize in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill lean-formalize -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lean-formalize, .gemini/skills/lean-formalize, .github/skills/lean-formalize and .opencode/skills/lean-formalize in your project.
SKILL.md names no scripts, command-line tools or credentials: Lean Formalize is instructions for the agent only. Its frontmatter pre-approves these tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Lean Formalize is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.4k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.8k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Lean Formalize: Writing Lean Proofs (trailofbits/skills, 7.5k stars), Continue (telegramdesktop/tdesktop, 33k stars), Math Formalization (tradecatlabs/vibe-coding-cn, 17k stars) and MCP Development (coollabsio/coolify, 63k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
wanshuiyin (a GitHub user) maintains it in wanshuiyin/Auto-claude-code-research-in-sleep, which has 17,205 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 7, 2026.
Source: wanshuiyin/Auto-claude-code-research-in-sleep on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.