'lightweight' 검색 결과

Create interactive 3D scenes for websites, including product views and scroll-driven animations. Covers choosing Three.js, React Three Fiber, or Spline, preparing lightweight models, showing loading progress, and adapting experiences for mobile devices.

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• Building an interactive 3D product configurator
• Adding 3D scenes to a portfolio
• Animating models and cameras as visitors scroll

Build text tokenizers that split multilingual text into small units for AI models. Learn how to train SentencePiece on raw text, configure BPE or Unigram splitting methods, and convert text to token IDs and back.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing Japanese, Chinese, or Korean text for AI models
• Training a custom vocabulary from raw text
• Choosing between BPE and Unigram

Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.

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• Building custom extraction and processing steps
• Saving custom data types to graph and vector databases
• Adding information to an existing graph

Review business contracts, including NDAs and vendor agreements, for payment, liability, ownership, and other risks. Get plain-English findings ranked by severity, negotiation suggestions, and an optional DOCX showing proposed deletions and additions.

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• Checking an NDA or vendor agreement before signing
• Reviewing payment terms and cash-flow risks
• Preparing negotiation points for a customer’s contract

Create animated Codex pets from character art, text ideas, or brand cues. Generate nine animation states, assemble a transparent sprite sheet, check appearance and movement, and package the results with a pet configuration file.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn character art into an animated Codex pet
• Create a pet in plush, clay, or other non-pixel styles
• Design a mascot from company or brand cues

Turn small game changes into concise specifications with clear reasons, rules, and acceptance criteria. Check existing design documents and earlier specifications, then document tuning adjustments, minor behavior changes, or small features before implementation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Documenting game balance adjustments
• Specifying minor behavior changes
• Defining a small mechanic for an existing system

Document a solved and verified problem when its reasoning is missing from code, tests, and existing documentation. Save one durable lesson in the repository so future agents can find it and avoid repeating substantial investigation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording reasoning behind a verified fix
• Preserving lessons to prevent recurring mistakes
• Updating a lesson that has become inaccurate

Track machine learning experiments with SwanLab by recording training settings, metrics, and media such as images and audio. Learn to compare runs, inspect training charts, and connect existing training code to local, cloud, or self-hosted dashboards.

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• Comparing experiments with different training settings
• Viewing training metrics in a local dashboard
• Logging sample images and audio alongside metrics

Build text tokenizers, which split text into units for AI models, directly from raw multilingual text. Learn custom vocabularies with BPE or Unigram, convert text to pieces or numeric IDs, and decode the results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing text for multilingual AI models
• Training a vocabulary from Japanese text
• Reusing a tokenizer for reproducible text splitting

Keep context for work that spans multiple sessions without belonging to a specific project phase. The skill manages lightweight persistent threads, letting you create, list, resume, check, and close them as work progresses.

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• Keeping context for work across sessions
• Recording work outside a specific phase
• Listing open or resolved threads

Analyze an existing codebase with parallel agents and produce seven structured documents covering technology, integrations, architecture, conventions, testing, and concerns. Use the results to understand an unfamiliar project or prepare for major changes.

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• Understand an existing project before taking it over
• Document the current codebase before major refactoring
• Refresh codebase documentation after significant changes

Design or extend DataFusion benchmarks using SQL where practical. Select representative workload variations, keep unrelated processing lightweight, and integrate runs with bench.sh so measurements reflect useful execution paths and remain practical to run.

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• Add SQL benchmarks for DataFusion operators
• Select representative input sizes and filter conditions
• Reduce unrelated processing overhead in measurements

Manage OpenRig development work from setup to completion using shared specification, progress, and evidence files. Record what was actually checked and pass work to the next owner so handoffs and conversation compaction preserve the context.

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• Starting a development task with clear completion criteria
• Recording progress and verification evidence
• Handing work to another agent

Create Word, PDF, and HTML documents from Markdown, choosing pandoc or ReportLab to suit formatting and layout needs. The workflow covers character handling, output checks, and an alternative generation method when PDF conversion fails.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Export a Markdown report to Word, PDF, and HTML
• Create PDFs containing multilingual text and symbols
• Generate PDFs without LaTeX

Choose a prioritization method that fits your product stage, team, stakeholder needs, and available data. Get a recommended framework, practical setup steps, a scoring example, and an alternative approach with its tradeoffs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a method for recurring roadmap prioritization
• Selecting a lightweight approach when data is scarce
• Agreeing on prioritization rules with stakeholders

Evaluate an AI product or feature idea by connecting customer problems, business outcomes, testable assumptions, risks, and differentiation. Produce a structured recommendation with lightweight experiments, success measures, and next steps for an investment decision.

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• Deciding whether to invest in an AI feature
• Comparing AI solution options by outcomes and risk
• Preparing a proposal for executive or budget approval

Create formula-based Excel valuation workbooks for public companies using discounted cash flow, a method that converts future cash flows into present value. Include per-share estimates, assumption sensitivities, source records, and checks that flag model limitations.

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• Build a DCF valuation workbook for a public company
• Refresh a valuation with updated assumptions
• Compare values across capital costs and growth rates

Add focused runtime logs to a macOS app and verify that expected events appear. Uses Apple’s Logger to trace window, sidebar, menu, and background activity while keeping logs concise and excluding sensitive data.

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• Trace window opening and closing
• Verify sidebar selection events
• Investigate menu and keyboard commands

Reduce GitHub API calls during issue monitoring by scanning basic issue details first, then fetching full descriptions and comments only for selected issues. Filters use assignees, status labels, and title patterns to narrow the list.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing API calls during recurring issue monitoring
• Filtering out assigned or blocked issues
• Fetching descriptions and comments only for relevant issues

Assess factual claims in AI-generated text by separating facts from opinions, choosing suitable independent evidence, and explaining what is supported or uncertain. Use it for a completed fact-check or an optional exercise in evaluating AI answers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fact-checking an AI-generated answer
• Choosing independent sources for a claim
• Identifying unverified numbers and assertions

Generate PNG or SVG charts from data for reports, monitoring alerts, and dashboards. Choose from line, bar, pie, candlestick, and heatmap charts, then adjust labels, colors, and annotations using Node.js without a browser or external API calls.

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• Creating chart images for reports
• Comparing multiple time-series trends
• Charting prices alongside trading volume

Check whether the MCP connection responds by sending a message and receiving it unchanged, or getting 'pong' when no message is supplied. Useful for CLI health checks, basic SignalR connection tests, and round-trip timing.

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• Checking connectivity before running heavier tools
• Running a basic SignalR connection test
• Measuring communication round-trip time

Open-ended scientific ideation partner. Use for research gaps, mechanism exploration, interdisciplinary connections, assumptions, possible research directions, and lightweight literature matrix or A+B paper-combination idea mapping. For structured testable hypotheses and validation plans, use hypothesis-generation instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.