'lean' 검색 결과

Clean up stored agent state in `.squad/` by compressing histories, pruning old logs, archiving stale decisions, and removing orphaned inbox files. Use it after long sessions or before starting work with many agents to reclaim context space.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing for work that starts many agents
• Cleaning up after long sessions or sprints
• Managing oversized history files or stored state

Find search terms, placements, audiences, and ads that spend money but generate few or no conversions in Google and Meta accounts. The skill prepares uploadable exclusion lists and groups spending by category to help prioritize account cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Cleaning up wasted advertising spend each month
• Auditing an advertising account for a new client
• Reviewing search terms and placements after campaign expansion

Review concrete code changes for defects, compatibility problems, repository-rule violations, and unnecessary code. Produce evidence-based findings and, when explicitly requested, make minimal cleanup changes while checking that existing behavior is preserved.

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• Reviewing a pull request or commit range
• Inspecting staged and unstaged changes
• Finding debug residue and duplicate code

Prepare conference LaTeX template archives for writing in Overleaf. The skill identifies the main file and dependencies, proposes cleanup for approval, and organizes sections, resources, and a README with submission and compilation guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a conference template for Overleaf
• Remove sample text and reorganize template files
• Separate paper sections into individual files

Clean survey datasets using their codebooks, which explain variables and response codes. Check missing values and outliers, harmonize variables across years, and prepare combined data with a cleaning report, variable definitions, and recorded transformation history.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking newly downloaded survey data
• Harmonizing variables across survey years
• Preparing panel data for research

Refactor React components that have accumulated many boolean options. Apply component composition, meaning building interfaces from cooperating parts, and shared state patterns to design reusable component libraries and clearer APIs, with guidance specific to React 19.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refactoring components with too many boolean props
• Designing reusable component library APIs
• Reviewing shared state and provider responsibilities

Inspect BB storage across machines, identify cleanup candidates, and remove archived files or unused development data. Configure automatic thread archiving and deletion, with previews showing how many threads would be affected before saving retention settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find threads and files using the most storage
• Clean up files from archived threads
• Remove development data for deleted checkouts

Review a branch against main and clean up unnecessary AI-generated code, including excess comments, unusual defensive checks, type workarounds, and deep nesting. Make focused edits that match surrounding code while preserving behavior unless fixing a clear bug.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean up a branch after AI-assisted coding
• Remove unnecessary comments and defensive checks
• Review any casts used to bypass type issues

Review dependency handling, package downloads, credentials, and deployed file cleanup in APM. The skill defines checks for safe paths, centralized credential access, and refusing content when its integrity or signature cannot be verified.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing dependency resolution and lockfile changes
• Checking package integrity and signature verification
• Inspecting token handling for possible leaks

Refactor React components into reusable pieces with clearer interfaces. Provides patterns for replacing excessive boolean settings, sharing state through providers, and designing component libraries, with additional guidance for React 19 APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refactor components with too many boolean settings
• Design a reusable component library
• Review shared state and provider structure

Review changed code for duplication, quality, efficiency, and clarity. Use review-only mode for findings, or request behavior-preserving cleanup by the main agent, with focused tests or other validation when the chosen mode calls for it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check changed code for reuse opportunities
• Simplify redundant state and complex control flow
• Find duplicate reads and unnecessary computation

Choose LLM settings for theorem proving, code generation, and creative exploration. It provides examples for output limits and response variety, plus proof planning prompts and a method for generating multiple proof attempts and selecting the best candidate.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing LLM settings for theorem proving
• Adjusting output settings for code generation
• Increasing response variety for creative exploration

Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing and joining data files
• Exploring trends and distributions
• Cleaning data before analysis

Design and implement repeatable workflows that clean, encode, transform, and validate raw data for machine learning. Get a preprocessing plan or implementation outline, with clear steps and guidance on where further checks are needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing raw data for machine learning
• Automating recurring data cleaning
• Planning data extraction, transformation, and loading

Clean up AI-written code by reviewing branch changes against main. Remove unnecessary comments, excessive defensive checks, and casts that bypass type issues, while preserving legitimate changes and matching the surrounding code style.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Removing unnecessary comments from AI-written code
• Cleaning up excessive checks and exception handling
• Reviewing any casts used to bypass type issues

Develop reusable external Holoscan Modules with the Holoscan CLI, from scaffolding and testing to Python bindings and package creation. Verify that applications can use installed packages in a clean environment, with checks for each requested format.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a reusable Holoscan operator module
• Adding and testing Python bindings
• Using an installed module from another application

Troubleshoots and debugs AWS Clean Rooms collaboration issues related to IAM roles, S3 bucket policies, KMS keys, Lake Formation permissions, and CloudWatch logging for custom ML model training and inference jobs. Use when a customer reports permission failures, access errors, or log publishing issues in Clean Rooms.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guided journey from a working-but-untested vibe-coded prototype to a production-ready product with tests, clean structure, a business-rules boundary, and resilience at scale. Orchestrates nine skills phase by phase - working-with-legacy-code, clean-code, refactoring-patterns, software-design-philosophy, clean-architecture, pragmatic-programmer, release-it, system-design, ddia-systems - asking the user questions at every decision point and recording results in the project docs/ folder (TESTING.md, TECH-DEBT.md, IMPROVE-CODE-QUALITY-PLAN.md) so the journey resumes across sessions. Use when the user wants to harden an AI-generated prototype, add tests before refactoring, make code safe to change, or says 'this works on my machine but I am scared to touch it'. For an aged codebase, remove-technical-debt; for greenfield structure, design-code-architecture; for a product and UX pass, improve-app; to optimize speed and structure, architecture-optimization. For one framework in isolation, invoke that skill directly.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Choose and audit startup metrics using Croll and Yoskovitz's "Lean Analytics". Use when the user mentions "what metrics should we track", "KPIs", "north star metric", "One Metric That Matters (OMTM)", "vanity metrics", "analytics dashboard", "DAU/MAU", "churn benchmark", or "measure product-market fit". Also trigger when choosing metrics for a startup or feature, auditing a dashboard for vanity metrics, setting metric targets and baselines, or instrumenting a product by business model and stage. Covers good-vs-vanity metrics, the One Metric That Matters, metrics by business model, the five startup stages, and benchmarks. For the build-measure-learn loop, see lean-startup. For fixing activation and retention, see improve-retention.

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Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", "decompose conditional", or "clean up this messy code". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation fits a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code-quality foundations, see clean-code. For managing complexity, see software-design-philosophy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Uninstall or hard-clean OpenClaw Companion, Windows node, native Gateway/MXC, WSL Gateway, and managed llama.cpp/Local AI state for a clean retest. Choose the existing dev CLI uninstall or the reviewed hard-clean procedure, inventory exact targets, and obtain destructive confirmation before acting.

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Build MCP Apps with interactive React UIs using the Leanmcp UI SDK. Use this skill when users ask for "MCP with UI", "MCP App", "interactive MCP", "leanmcp UI", "MCP dashboard", or want to create rich visual interfaces for their MCP tools. This skill covers @UIApp decorator, React components with useTool/useResource hooks, ToolDataGrid, ToolForm, ToolButton, and ChatGPT Apps support.

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Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data fo...

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Use when working with Lean 4 (.lean files), writing mathematical proofs, seeing "failed to synthesize instance" errors, managing sorry/axiom elimination, or searching mathlib for lemmas - provides build-first workflow, haveI/letI patterns, compiler-guided repair, and LSP integration

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.