'simple' 검색 결과

Make system architecture decisions by clarifying requirements and constraints, comparing benefits and drawbacks, and considering simpler alternatives. Record the reasoning in ADRs, architecture decision records, so significant choices and their rationale are documented.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clarifying requirements before system design
• Comparing architecture patterns
• Documenting decisions and rationale in ADRs

Evaluates task complexity and selects a small set of existing skills for work spanning multiple areas. Uses agent-memory-mcp to record and retrieve successful combinations, while keeping simple tasks within the agent’s ordinary capabilities.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Selecting skills for a complex task
• Coordinating work across UI, databases, and deployment
• Reusing successful skill combinations

Create, edit, inspect, and verify PowerPoint slide decks in PPTX format. Use a consistent, simple layout and consolidate information into fewer slides, then reopen the presentation to check its slide count and titles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a PowerPoint presentation
• Edit an existing PPTX deck
• Apply a consistent, simple slide layout

Test whether you understand a concept and whether evidence supports a decision using frameworks drawn from Richard Feynman’s work. Explore concrete examples, simple demonstrations, counterevidence, and the limits of analogies to expose gaps in reasoning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking understanding beyond memorized terminology
• Finding counterevidence to a favored conclusion
• Reviewing processes that prioritize form over results

Train language models with SimPO using pairs of preferred and rejected answers. Configure preference training without a separate reference model, adapt settings for base or instruction models, and address unstable training, memory limits, or lost capabilities.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a base model with preferred and rejected answers
• Further tuning an instruction model with preference data
• Adjusting training settings for math and code tasks

Adapt PyTorch training scripts for multiple GPUs or machines with Accelerate. This skill guides code changes, configuration, and launch commands, including mixed precision, which combines numerical formats, and DeepSpeed or FSDP integration for distributed training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Moving single-GPU training to multiple GPUs
• Configuring mixed precision training
• Setting up DeepSpeed or FSDP

Manage separate browser profiles for website tasks, keeping each account’s cookies and logins between sessions. The skill guides profile selection, reuse, opening, and closing, and explains when a direct download is simpler than browser work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reuse a browser profile already signed in
• Keep different accounts in separate profiles
• Use a separate profile for signup verification

Turn rough bug reports, feature requests, support notes, or pull requests into concise issues. Explain the current problem and desired behavior in plain language, preserving useful evidence without prescribing how developers should implement a solution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Simplifying a lengthy bug report
• Drafting an issue from a pull request
• Clarifying the intended experience for a new feature

Keep coding tasks small by checking whether new code is needed, reusing existing helpers, and preferring standard libraries or built-in platform features. Apply the same approach to feature development, bug fixes, refactoring, and reviews.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a feature with minimal code
• Reviewing unnecessary dependencies
• Simplifying an overcomplicated implementation

Create a small interactive HTML playground to explore how something works. It packages an explanation, simulation, or simple game into one screen, with clearly labeled keyboard controls and no network requests or external assets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore a mechanism through interactive controls
• Step through a simulation on one screen
• Explain an idea through a simple game

Guide language model training with SimPO using pairs of preferred and rejected answers. Covers configuration, learning rate tuning, and memory troubleshooting for preference alignment, which adjusts model responses toward human preferences without a separate reference model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training on preferred and rejected answer pairs
• Exploring an alternative to DPO without a reference model
• Tuning preference training for math and code tasks

Adapt existing PyTorch training scripts to run across multiple GPUs or machines with Accelerate. This skill covers device setup, distributed training configuration, and mixed precision, which uses lower-precision numbers to reduce memory use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adapt a single-GPU training script for multiple GPUs
• Configure mixed precision to reduce training memory use
• Set up training with DeepSpeed or FSDP

Convert Markdown into Word, PDF, and HTML using a workflow suited to the document. Simple files use direct delegation, while documents with special characters use separate conversion steps, character replacement, and output checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a simple Word document from meeting notes
• Exporting one document to several formats
• Converting documents containing special characters to PDF

Add compact visual summaries to Markdown answers using static HTML and CSS. Show comparisons, key metrics, timelines, or simple relationships in a preview, while keeping explanations and detailed information in the surrounding text.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing key metrics visually
• Showing a process or sequence of steps
• Summarizing events on a timeline

Create simple, cute square character images using rounded shapes, two character colors, and a solid background. The skill can use product context to propose three design directions and generate six separate candidates for comparison.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a product mascot
• Compare three character design directions
• Simplify an animal or object into a recognizable character

Control deep thinking mode. Default enabled. For very simple tasks (simple reminders, greetings, quick queries), can temporarily disable to speed up response. Auto-restores to enabled after completion.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate game content procedurally — seeded deterministic RNG, value/Perlin/ Simplex noise for terrain and heightmaps, grid dungeon generation (rooms + corridors, BSP, random walk), and weighted loot/drop tables. Engine-neutral algorithms. Use when the user mentions procedural generation, perlin/simplex noise, random seed, dungeon generator, heightmap/terrain, or loot tables.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ALWAYS invoke this skill when the user asks for simpler or shorter about something said or written - "say it simply", "what does this mean", "I don't understand your answer", "too long", "wait, what?", "bro" - in any language, about any text: your own answer, a report, a review comment, an error. "I don't understand what to DO" is os-step-by-step; this skill restates text. It restates for a reader who does not read code: leads with the point, keeps every number, warning and caveat - no facts added, no bad news dropped. A number returns exactly that many points, most important first.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iteratively study and apply elegant coding patterns. Each iteration - understand the code, research what simple and elegant code looks like, apply learnings, verify with CI. Use as a standalone refactoring pass or when the user asks to make code more elegant, simple, or idiomatic.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Register, segment, filter, resample 3D medical images (MRI, CT, microscopy) via SimpleITK Python; DICOM, NIfTI, multi-modal. Rigid/affine/deformable registration, threshold/region-growing segmentation, Gaussian/morph filtering, label stats, format conversion. Use to align volumes across timepoints/modalities, segment fluorescence, or convert DICOM→NIfTI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

通用 horizontal-swipe HTML deck, 不要 magazine 调

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iteratively study and apply elegant coding patterns. Each iteration - understand the code, research what simple and elegant code looks like, apply learnings, verify with CI. Use as a standalone refactoring pass or when the user asks to make code more elegant, simple, or idiomatic.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully. 2025 lesson: GraphQL isn't always the answer. For simple CRUD, REST is simpler. For high-performance public APIs, REST with caching wins. Use GraphQL when you have complex data relationships and diverse client needs. Use when "graphql, graphql schema, graphql resolver, apollo server, apollo client, graphql federation, dataloader, graphql codegen, graphql query, graphql mutation, graphql, api, apollo, schema, resolvers, dataloader, federation, typescript" mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.