22건 · 관련도순
Use when reviewing a DataFusion Comet pull request that touches native or JVM columnar shuffle, the shuffle writers and readers, partitioning, the Arrow IPC block format, shuffle compression, or the Celeborn integration. Load alongside review-comet-pr.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Improve slow Apache Spark jobs by reviewing data partitions, caching, memory settings, and shuffles, which move data between workers. Use the guidance to investigate bottlenecks and tune pipelines for larger datasets.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build a card game: card data, deck/hand/discard zones, draw/shuffle/reshuffle, a turn structure, costs, and effect resolution. Use for a deckbuilder, TCG/CCG, or roguelike deckbuilder.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Letters shuffle through random neighbors then settle into the word — a quick playful reveal for a short label. Final text is the real content; instant under prefers-reduced-motion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Shuffles repetitive JSON objects safely: validates the file and schema consistency first, then randomizes order without corrupting data. Use when test fixtures or datasets in JSON need reordering or randomizing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Media player traps on Media3/ExoPlayer and desktop engines: crossfade, audio focus, fades, loudness, DSP, queues and shuffle, position restore, service lifecycle, group listening. Use when playback state, queue or audio levels go wrong.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when reviewing a DataFusion Comet pull request. Covers the workflow that applies to every PR and routes to the area-specific review skills for expressions, FFI, memory management, shuffle, and Iceberg writes. Provides guidance to a human reviewer rather than posting comments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Recommend runtime and V2 scene management: run recommendation requests, manage recommend scenes and rules, and verify the deployed recommendation path.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert blueprint for digital card games (CCG/Deckbuilders) including card data structures (Resource-based), deck management (draw/discard/reshuffle), turn logic, hand layout (arcing), drag-and-drop UI, effect resolution (Command pattern), and visual polish (godot-tweening, shaders). Use for CCG, deckbuilders, or tactical card games. Trigger keywords: card_game, deck_manager, card_data, hand_layout, drag_drop_cards, effect_resolution, command_pattern, draw_pile, discard_pile.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert blueprint for digital card games (CCG/Deckbuilders) including card data structures (Resource-based), deck management (draw/discard/reshuffle), turn logic, hand layout (arcing), drag-and-drop UI, effect resolution (Command pattern), and visual polish (godot-tweening, shaders). Use for CCG, deckbuilders, or tactical card games. Trigger keywords: card_game, deck_manager, card_data, hand_layout, drag_drop_cards, effect_resolution, command_pattern, draw_pile, discard_pile.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Flaky test fixes: detect nondeterministic tests empirically (repeat/shuffle/parallel runs), diagnose the root cause, fix it — never retry/skip/sleep — and verify across many randomized runs. Triggers on "absolute deflake", "fix flaky tests", "CI is flaky", "this test fails randomly/intermittently".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.