'sample' 검색 결과

블로그와 이메일 등의 초안에서 AI에 흔한 상투적 표현과 과도한 장식을 다듬습니다. 의미와 의도한 어조를 유지하며 작성자의 글 샘플에 맞춰 퇴고합니다.

• 공개 전 블로그 초안 퇴고
• 이메일과 메모를 자연스럽게 수정
• 자신의 문체에 맞춘 글 재작성

함께 제공되는 스킬을 등록하고 사용하는 체계를 시험하는 예제입니다. 전용 명령으로 호출하는 기본 사례를 통해 스킬 기능을 시연하고 레지스트리의 동작을 확인합니다.

• 함께 제공되는 스킬의 실행 시험
• 스킬 레지스트리 동작 확인
• 최소한의 스킬 예제 학습

최근 성공한 /scrape 작업을 대화에서 되짚어 스크립트, 테스트, 고정 테스트 데이터를 갖춘 재사용 가능한 브라우저 스킬로 저장하고 커밋 전에 확인합니다.

• 성공한 정보 수집을 반복합니다
• 브라우저 조작을 스크립트로 만듭니다
• 저장할 수집 절차에 테스트를 추가합니다

팀의 주요 지표, 칠 일간의 추이, 활동 기록과 작업 목록을 Notion 형식의 HTML 화면에 모읍니다. 수동 및 자동 갱신을 지원하며 연결이 없으면 예시 데이터를 표시합니다.

• 팀 지표와 작업을 한눈에 확인
• Notion 연동 대시보드 제작
• 예시 데이터로 오프라인 미리 보기

Review local character animations before sharing previews or final renders. Check structured design data, asset files, browser errors, sampled motion frames, and MP4 metadata, then classify the result as pass, revise, or fail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking character animation previews before sharing
• Finding missing assets and browser errors
• Inspecting sampled frames for motion and visual issues

Generate multilingual narration with fish.audio, reuse a previously created voice model, and adjust emotion, pace, and volume. Start with a short voice sample before producing the full audio file.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create multilingual video narration
• Reuse a voice created in the playground
• Add laughter or whispers to narration

Investigate a video that is not working by downloading it, setting it as the source in a sample component, and running a Remotion render with verbose logs to examine the behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a video reported as not working
• Setting up a sample with the affected video
• Rendering a video with verbose logs

Plan A/B tests to compare page changes using clear hypotheses and meaningful measures. The skill helps define sample size, duration, traffic allocation, and result interpretation, and organize a continuing program of prioritized experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare headlines or signup buttons
• Estimate test sample size and duration
• Interpret results and possible adverse effects

Stress-test research plans by comparing possible scenarios, such as sample losses, unavailable materials, or methods failing to transfer. Record assumptions and evidence, compare response options, and define contingency plans and observable triggers for changing course.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan responses to higher-than-expected sample losses
• Prepare backups for unavailable reagents or datasets
• Compare research options under resource constraints

Reduce data with many features into fewer dimensions using UMAP. Create 2D or 3D views, prepare inputs for clustering, compare parameter settings, and project new samples while checking whether the results remain stable and meaningful.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Visualizing data with many features in 2D or 3D
• Preparing reduced features for HDBSCAN clustering
• Guiding dimensionality reduction with partial labels

Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining the feature contributions behind one prediction
• Visualizing attribution patterns across evaluation samples
• Selecting and explaining a class in a multiclass model

Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Analyze single-cell RNA-seq data with Scanpy, from quality checks and normalization to cell grouping, exploratory marker genes, and plots. Bundled scripts also prepare sample-level count summaries and support conversion of R objects to h5ad.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check data quality and normalize single-cell measurements
• Visualize cell groups with UMAP or t-SNE
• Explore marker genes and review cell-type labels

Find biological pathways and functions enriched in gene lists or ranked gene data. Choose an appropriate statistical method, align gene identifiers, define comparison backgrounds, and turn results into interpretable tables and plots with multiple-testing corrections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding enriched pathways among differentially expressed genes
• Running GSEA on a complete ranked gene list
• Calculating gene-set scores for individual samples or cells

Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

Process mass spectrometry data using scripts for format conversion, signal cleanup, peak detection, and quantification across samples. Prepare protein and metabolite analyses with identification filtering, candidate annotations, data tables, and plots.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect, convert, and preprocess mass spectrometry files
• Build quantitative tables across multiple samples
• Find metabolite candidates from accurate mass

Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Match scientific labels to ontology identifiers, standardized IDs for concepts such as tissues, cell types, and diseases. Checks existing IDs, labels, obsolete terms, and prefixes to support metadata annotation, submission preparation, and table audits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Annotating tissue, cell type, or disease fields with standard IDs
• Auditing metadata tables for incorrect IDs and labels
• Checking obsolete terms and available replacements

Process next-generation sequencing data with deepTools: convert alignment files into coverage tracks, normalize signals, compare samples, and check quality. Create heatmaps and profiles around genes or peak regions for ChIP-seq, RNA-seq, and ATAC-seq experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting BAM files to normalized bigWig tracks
• Checking sequencing quality and replicate agreement
• Plotting heatmaps around transcription start sites

Review supplied writing through twelve DHDNA dimensions, a framework for describing reasoning and expression. Receive observations supported by quotations, optional scores, and comparisons between samples, with clear limits separating textual patterns from psychological traits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reflect on reasoning patterns in your writing
• Compare argumentation across selected texts
• Review how a text expresses assumptions and uncertainty

Automate lab data tasks in Benchling using its Python SDK and REST API. Import and export sequences, inspect sample inventory, and work with electronic lab notebooks and workflow tasks while checking schemas, permissions, and operation results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Importing DNA sequences from FASTA files
• Auditing sample inventory at a storage location
• Exporting sequences for a specific schema to CSV

Organize experiment photos, audio, and notes into Markdown logs with structured metadata. Assign experiment and sample batch IDs, archive original attachments, and optionally connect records to Feishu group messages or an Obsidian notebook.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning experiment photos and voice notes into consistent logs
• Tracking the same sample batch across multiple experiments
• Archiving original files with links from experiment logs

Review and improve statistical reporting in manuscript methods, results, and figure captions. It checks sample definitions, repeated measurements, tests, and uncertainty, then provides revised wording and identifies missing information or issues reviewers may raise.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a manuscript’s statistical methods section
• Checking sample sizes and repeated measurements
• Aligning figure captions with reported statistics

Plan A/B tests to compare page or messaging changes. Helps define hypotheses, choose success metrics, estimate sample sizes, allocate traffic, and interpret results, while also organizing a repeatable process for prioritizing and documenting experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare headline or button-copy variants
• Plan sample sizes and test duration around available traffic
• Analyze results alongside metrics that must not worsen