「collect」の検索結果

Design accessibility training. TRIGGERS - Use when user needs help with accessibility-training-program related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Implement A/B tests with code and analytics. TRIGGERS - Use when user needs help with ab-test-implementation related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Design A/B tests with hypotheses, variants, sample sizes, and analysis plans. TRIGGERS - Use when user wants to run A/B tests, split tests, or experiment with variations.

日本語の概要は準備中です。原文の説明を表示しています。

Write abandoned cart email series. TRIGGERS - Use when user needs help with abandoned-cart-series related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Write accessibility requirements. TRIGGERS - Use when user needs help with accessibility-requirements related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Guide 409A valuation processes. TRIGGERS - Use when user needs help with 409a-valuation-guide related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Guide academic writing with standards. TRIGGERS - Use when user needs help with academic-writing-guide related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Design for accessibility compliance. TRIGGERS - Use when user needs help with accessibility-design-architecture related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Write accelerator applications. TRIGGERS - Use when user needs help with accelerator-application related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Plan accessibility testing. TRIGGERS - Use when user needs help with accessibility-testing-plan related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Guide 8(a) certification processes. TRIGGERS - Use when user needs help with 8a-certification-guide related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Plan 5G network deployment with coverage. TRIGGERS - Use when user needs help with 5g-deployment-plan related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze 5G use cases. TRIGGERS - Use when user needs help with 5g-use-case-analysis related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Design academic advising models. TRIGGERS - Use when user needs help with academic-advising-model related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Implement accessibility. TRIGGERS - Use when user needs help with a11y-implementation-guide related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Answers "can CARLA do X here, and is there a vetted procedure for it" before you promise anything, and detects client/server/tree version skew — a support matrix over the whole collection (covered by a skill / works but undocumented / present but untested / broken or removed), an index of the features that are verified BROKEN on 0.10.0 (gbuffers crash the server, map layers do nothing, Landmark.waypoint is None, standalone asset packages are gone), and a probe that reports what this machine's build flags and running server actually offer. Use when the user asks whether CARLA supports something, when a feature has no skill, when a UE4 tutorial's steps do not exist, or before improvising a procedure.

日本語の概要は準備中です。原文の説明を表示しています。

Use for the frontend phase after the API contract exists — deciding the screens as units of work (one job a person finishes in one go, per actor of the event timeline), collecting the views and commands each screen needs, keeping the screen map in frontend/README.md, building against the Lean CLI, then handing over to frontend-ux-review and the human screen check. Not for the spec-animation mockup (domain-mockup).

日本語の概要は準備中です。原文の説明を表示しています。

Triage a single GitHub PR by PR number. Check out the PR's branch, detect conflicts with the target branch via `gh pr status` (and label the PR with `cc-resolve-conflict` if any are found), collect unresolved review comments and CI status and judge only whether each item must be fixed, then take action (add cc-fix-onetime label if fixes are needed; if release-ready, add cc-release-ready label for an Epic PR marked `cc-epic-issue` instead of merging, otherwise merge the PR).

日本語の概要は準備中です。原文の説明を表示しています。

Read the network instead of the DOM. Snoop JSON API responses, wait for a request instead of sleeping, collect items while scrolling, record a QA flight trace (jsonl, HAR, screenshots), and intercept requests with routes.

日本語の概要は準備中です。原文の説明を表示しています。

Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.

日本語の概要は準備中です。原文の説明を表示しています。

Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

日本語の概要は準備中です。原文の説明を表示しています。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

日本語の概要は準備中です。原文の説明を表示しています。

Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。