'lean' 검색 결과

Translate C or C++ programs into equivalent Lean4 code, preserving program semantics and ensuring the generated code is well-typed, executable, and can run successfully. Use when the user asks to convert C/C++ code to Lean4, port C/C++ programs to Lean4, translate imperative code to functional Lean4, or create Lean4 versions of C/C++ algorithms.

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Use when the user wants to simplify, clean up, refactor, tidy, or refine code — their staged/unstaged git changes or a target file/path. Reduces complexity, flattens nesting, removes redundancy and dead code, scores each change by value (holding low-value churn), then runs tests to prove nothing broke. Invoke on: "simplify", "simplify this", "simplify my code/changes", "clean up", "clean this up", "clean up my changes", "refactor this", "make this cleaner", "tidy this up", "reduce complexity", "flatten this", "remove dead code", "make it more readable", "polish before commit", or "absolute simplify". Acts on your working diff; for repo-wide dead code use absolute-prune; for lint/type debt use absolute-debt.

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AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Use when asked to run /clean-clean-cut to cut accumulated records and residue. Not for untracked or non-VCS changes, or branch/worktree cleanup: use git-cleanup.

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Fix missing theorem proof terms when analyzing Lean 4 environments via importModules. Use when: (1) ConstantInfo.value? returns none for theorems despite TheoremVal.value being Expr, (2) building code graph / dependency extractor for Lean 4 and getting 0 proof dependency edges, (3) Lean 4.30+ project where theorem proofs appear missing from loaded environment, (4) analyzing Mathlib or any Lean 4 project and proof terms are empty. Root cause: breaking change in Lean 4.30 — value? treats theorems as opaque by default.

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Integrate ManagedCode.Orleans.Graph into an Orleans-based .NET application for graph-oriented relationships, edge management, and traversal logic on top of Orleans grains. Use when the application models graph structures in a distributed Orleans system.

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최근 코드 변경을 재사용, 품질, 효율, 근본 원인 해결 여부로 검토하고 중복과 불필요한 복잡성을 정리합니다. 위험도에 따라 수정안을 처리하고 적용 후 테스트합니다.

• 최근 코드 변경 정리
• 중복 로직과 공통화 후보 찾기
• 마지막 커밋의 효율 점검

Clean messy tabular data through OpenRefine using command-line operations. Import files, inspect rows, apply reusable JSON transformation histories, and export cleaned CSV or TSV data, with session support for undoing and redoing changes.

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• Remove extra spaces from names in a CSV file
• Reuse the same cleaning steps on another dataset
• Inspect sample rows before and after transformation

Structure Python code to release files and database connections reliably, including when errors occur. Covers context managers, which handle setup and cleanup, asynchronous resources, nested cleanup, and streaming responses that retain accumulated state.

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• Releasing database connections when errors occur
• Cleaning up files and temporary files
• Creating asynchronous context managers

Design or refactor backend services with clear layers and dependency rules. Using Clean Architecture, Hexagonal Architecture, and Domain-Driven Design, it separates business logic from databases and frameworks and defines interfaces that support testing without external services.

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• Designing the structure of a new backend service
• Separating business logic from database and HTTP code
• Resolving circular dependencies between layers

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

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• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Simplify bloated AI-generated code by removing dead code, duplicated logic, and unnecessary layers while checking that existing behavior stays intact. Use a limited cleanup scope, verify changes with relevant checks, or request a review without edits.

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• Removing duplicate logic and unused code
• Simplifying unnecessary wrappers without changing behavior
• Cleaning up only a specified set of changed files

Prepare readable Markdown from HTTPS article pages using an optional external Defuddle cleaner. The workflow checks URLs, requires network consent and executable review, previews extracted content, and can retain the cleaned source separately from wiki ingestion.

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• Extract readable Markdown from a web article
• Prepare a web source for later wiki ingestion
• Save cleaned content with source metadata

Work through eight canvas sections to clarify a business problem, target users, desired outcomes, and possible solutions. Turn assumptions into testable hypotheses and choose the smallest experiment to investigate the most important uncertainty before development.

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• Framing the problem behind a new product idea
• Aligning a cross-functional team on assumptions
• Choosing which hypothesis to test first

Add burned-in captions to a finished video using Whisper timings from its own audio. Choose paper, bold, or clean styling, and optionally use narration text to preserve intended spelling. Outputs include the captioned video and an SRT file.

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• Adding captions to a finished video
• Restyling captions for social media shorts
• Preserving brand names and numbers from a narration script

Clean up unwanted Dropbox files and folders through a review process. Identify candidates, check metadata and sharing status, optionally preserve copies, and delete only explicitly confirmed items, with a report of each outcome.

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• Review and delete duplicate PDFs in a Dropbox folder
• Remove obsolete draft files after confirmation
• Clean up temporary exports stored in Dropbox

Prepare large Excel datasets by combining sheets, using Parquet, a format for efficient table storage, and cleaning a selected text column to retain only Chinese characters. Export the cleaned data as an Excel file with a local download link.

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• Removing symbols and numbers from Chinese text columns
• Preparing large Excel datasets for analysis
• Combining sheets and exporting cleaned data

Build MCP servers using the Leanmcp SDK with decorator-based TypeScript. Use this skill when users ask for "leanmcp", "MCP with decorators", "MCP with authentication", "MCP with elicitation", "MCP with environment injection", or want a simpler, more elegant way to build MCP servers. Leanmcp provides automatic schema generation, dependency injection, authentication, and user input collection.

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Translate Python programs to equivalent Lean4 code while preserving semantics and ensuring type safety. Use when users ask to convert, translate, or port Python code to Lean4, or when they need to verify Python algorithms using Lean4's theorem proving capabilities. Handles functions, classes, data structures, control flow, and ensures the generated Lean4 code is well-typed, executable, and can successfully run.

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Facilitate a structured conversation to define clean code principles for a repository. Produces a formal clean-code.md document that the clean-code atom will use as its override. Use when setting up coding standards, defining code quality rules, or when the user says 'setup clean code', 'define coding standards', 'code quality principles', 'coding guidelines', or 'help me define my code standards'.

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Systematic code cleanup and refactoring agent that identifies and fixes code quality issues without changing functionality. Focuses on: - Dead code removal - Import organization - Type improvements - Consistent patterns - File organization Use when "User asks to "clean up" code, User mentions "dead code" or "unused", User wants to "organize imports", User asks about "code quality", User wants to "refactor" without changing behavior, Code review identifies cleanup items, After major feature completion, cleanup, refactoring, code-quality, maintenance, organization" mentioned.

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AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Use when safely removing dead frontend code, unused exports, stale components, obsolete routes, unused dependencies, or cleanup targets found by tools such as knip, depcheck, ts-prune, TypeScript, ESLint, or manual review. Do not use for feature rewrites that change behavior; Chinese triggers include dead code, clean up unused, refactor clean, dependency cleanup, delete useless code.

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Deep codebase cleanup — kills dead code, fixes all lint/format warnings, removes orphaned files, cleans stale TODOs, strips security hazards, tightens TypeScript strict mode, and organizes imports. Triggers: clean up, dead code, unused imports, lint, technical debt cleanup.

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