264 件 ・ 関連度順
概要と使いどころ
Write idiomatic GDScript for Godot 4.7: static typing, the node lifecycle (_ready/_process/_physics_process), @export/@onready/@tool annotations, signals, and await for asynchronous flow. Use when editing .gd scripts in a Godot project (project.godot), writing or debugging GDScript, or porting 3.x GDScript to 4.x (function signatures, yield to await, export to @export).
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Records visual proof while testing UI behavior — the agent tests the app hands-on via computer use while a screen recording with structured test/assertion annotations captures the session — then posts the video and a results summary to the PR and tracker issue. Use whenever a change needs verifiable evidence that it works, instead of prose claims — including headless environments (scripted screenshots and probes) and non-UI changes (measured numbers, output pairs).
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。
Assesses the quality and completeness of a genome annotation with BUSCO (conserved single-copy ortholog recovery), OMArk (proteome completeness, consistency, and contamination), CheckM2 (prokaryotic completeness/contamination), and a gene-set sanity panel (gene count, mono-exonic fraction, protein-length distribution, mRNA:gene ratio, coding density). Covers the assembly-BUSCO-vs-proteome-BUSCO diagnostic, what BUSCO-Duplicated really means, why gene count is a vanity metric, and the QC of transferred annotations. Use when judging whether an annotation is good enough to publish or submit, diagnosing a suspect annotation, or comparing annotation completeness across pipelines.
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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。
Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/scaling choices, robust color mapping, optimal leaf ordering, and ComplexHeatmap/pheatmap/seaborn rendering. Covers the ward.D vs ward.D2 trap, the row-vs-column scaling decision, multi-track annotations, oncoPrint, and raster rendering for large matrices. Use when visualizing expression patterns across samples or identifying co-regulated clusters.
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。
Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.
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。
Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.
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Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.
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。
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
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 when implementing location services, maps, geofencing, or debugging location/MapKit issues. Covers Core Location, CLMonitor, MapKit, annotations, directions.
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, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, donut, or iOS 26 3D charts; when adding chart selection, scrolling, annotations, axes, scales, legends, or foregroundStyle grouping; when plotting functions with BarPlot, LinePlot, AreaPlot, PointPlot, Chart3D, or SurfacePlot; or when creating heat maps, Gantt charts, grouped bars, sparklines, threshold lines, or spatial visualizations.
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, review, or improve maps and location features in iOS/macOS apps using MapKit and CoreLocation. Use when working with Map views, annotations, markers, polylines, user location tracking, geocoding, reverse geocoding, search/autocomplete, directions and routes, geofencing, region monitoring, CLLocationUpdate async streams, or location authorization flows. Also use when working with maps, coordinates, addresses, places, directions, distance calculations, or location-based features in Swift apps.
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。
Display and manipulate PDF documents using PDFKit. Use when embedding PDFView to show PDF files, creating or modifying PDFDocument instances, adding annotations (highlights, notes, signature widgets), extracting text with PDFSelection, navigating pages, generating thumbnails, filling PDF forms, or wrapping PDFView in SwiftUI.
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Import recorded driving sensor data (GPS, camera, lidar, actor tracks, lanes) into scenariobuilder.* objects (GPSData, CameraData, LidarData, ActorTrackData, Trajectory, laneData) and run preprocessing — synchronize, offset correction, crop, normalizeTimestamps, convertTimestamps. Also: compute actor tracks from lidar when no annotations exist, attach camera/lidar mounting + intrinsics, export to MAT/workspace/timetable/script. Use for raw driving dataset files (KITTI, nuScenes, Waymo, Pandaset, ROS/ROS2 bags, .mat, .csv, .mp4) or driving/vehicle/sensor logs that need wrapping. drivingLogAnalyzer (DLA) is OPT-IN ONLY — invoke only on explicit user request ('DLA', 'open in DLA', 'inspect/explore/analyze the recording') or reported sensor problem (sync drift, timestamp mismatch, overlay misalignment). NEVER auto-launch DLA after wrapping (Rule 0). For 'build scenario / export to RoadRunner / drivingScenario / OpenSCENARIO / Unreal / simulate', hand off to matlab-use-scenario-builder.
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。
Organize a Power BI or Analysis Services semantic model end to end so it is ready for report authors, Excel users and Copilot - table groups, display folders, Title Case business names and naming conventions, hierarchies, sort-by columns, hiding, Analyze in Excel (IsAvailableInMDX), descriptions, format strings, data categories, context annotations, DAX formatting and UDFs, calculated-object justification, duplicate removal, perspectives, translations and security roles. Use whenever the user asks to "organize", "tidy", "clean up", "polish", "prepare", "standardize" or "make report-ready" a model or one table in it (for example "organize the date table"), to "fix naming conventions", "rename fields", "make names human readable" or "fix abbreviations", even when they name only one of these aspects, and whenever a model is driven through the Tabular Editor 3 MCP or the te CLI for that purpose. Not for DAX performance tuning (dax skill) or model design and star-schema changes (semantic-model skill).
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TOM and ADOMD.NET guidance via PowerShell for connecting to Power BI Desktop's local Analysis Services instance. Covers model enumeration, DAX queries, metadata modification, annotations, calendar definitions, field parameters, query tracing, DAX library package management (daxlib.org), and the Desktop Bridge for reloading and screenshotting the report canvas. Automatically invoke when the user mentions "Power BI Desktop", "Analysis Services port", "TOM", "ADOMD", "daxlib", "DAX library", "DAX UDF package", or asks to "connect to PBI Desktop", "query PBI Desktop with DAX", "modify PBI Desktop model", "add a measure to PBI", "capture visual queries", "create a field parameter", "validate DAX", "intercept DAX queries", "install daxlib", "add DAX SVG", "add IBCS", "reload the report canvas", "screenshot a report page", "Desktop Bridge", or to work with the model and report in Power BI Desktop together.
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。
Query CPIC pharmacogenomics clinical annotations database
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。
Query NCBI Gene database for gene information and annotations
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。
Query Ensembl genome browser for gene annotations and sequences
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。
Generates and validates technical documentation including docstrings, OpenAPI/Swagger specs, JSDoc annotations, doc portals, and user guides.
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 when writing GDScript — static typing, await/coroutines, lambdas, match patterns, export annotations, inner classes, and common idioms
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。
InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell comm
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。
Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including
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。
A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and
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。