「ara」の検索結果

Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.

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Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.

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Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.

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Design and implement real-time audio processing chains using Audio Toolbox streaming objects. Use when building frame-based audio processing loops, multiband filters, dynamic range control, parametric EQ, level metering, loudness metering, SPL metering, octave-band analysis, sample rate conversion, frequency-domain filtering (long impulse responses, custom filter banks), or audio chains in Simulink. Covers visualization (visualize method), interactive tuning (parameterTuner), MIDI control, and Audio Toolbox Simulink blocks. Use when the user says "real-time audio", "streaming audio", "audio filter", "compressor", "equalizer", "level meter", "loudness meter", "SPL meter", "octave bands", "crossover filter", "audio chain", "MIDI control", "convolution reverb", "impulse response streaming", "frequency-domain filter", or asks to process audio frame-by-frame.

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Microstrip, stripline, CPW, differential pairs, and crosstalk analysis for impedance-controlled PCB interconnects. TRIGGER: user asks to design or analyze a transmission line (microstrip, stripline, CPW, coplanar, differential pair), extract RLGC or per-unit-length parameters, compute trace impedance, analyze a PCB trace cross-section, or perform crosstalk/coupling analysis. Invoke BEFORE writing code — preferred over RF Toolbox analytical functions (txlineMicrostrip, txlineStripline, txlineCPW). SKIP: EM simulation/S-parameter extraction of an existing component (use matlab-analyze-em), material/stackup definition only (use matlab-manage-pcb-material), building custom non-catalog geometry (use matlab-assemble-pcb-layout), optimization sweeps (use matlab-optimize-pcb-design).

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Analyze signal spectra in MATLAB using nonparametric and parametric estimators. Use when computing frequency content, PSD, power spectrum, spectral peaks, bandwidth, or streaming spectral analysis. Covers pspectrum, pwelch, periodogram, pmtm, pburg, pmusic, rootmusic, plomb, poctave, spectrumAnalyzer, dsp.SpectrumEstimator. TRIGGER: FFT, PSD, power spectrum, spectral analysis, frequency content, bandwidth measurement, spectral leakage, window selection, streaming spectrum, periodicity, resolve close frequencies, multitaper. DO NOT TRIGGER: filter design, spectrogram/time-frequency, audio features.

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Generate and structure MATLAB unit tests using matlab.unittest and matlab.uitest features, including class-based tests, parameterized testing, fixtures, mocking, and app testing with gestures. Use when writing, generating, or adding tests, creating test classes, adding test methods, parameterizing tests, setting up fixtures, mocking dependencies, or testing App Designer apps. Do NOT use for running tests, collecting coverage, or CI/CD configuration.

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Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile instead).

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Upgrade C, C++, and Fortran MEX source files from the Separate Complex (SC) API to the Interleaved Complex (IC) API. Use when converting mxGetPr/mxGetPi to mxGetComplexDoubles, migrating MEX files to -R2018a, adding MX_HAS_INTERLEAVED_COMPLEX guards for SC/IC guarded builds, or modernizing legacy MEX code that uses mxGetData/mxSetData/mxGetImagData/mxSetImagData. Covers C (.c), C++ (.cpp, .cxx), and Fortran (.F, .f90) MEX functions. Triggers on: interleaved complex, IC MEX, mex upgrade, mex migration, separate complex, mxGetPi, mxGetPr replacement, -R2018a, complex MEX, Fortran MEX, .F MEX file, C++ MEX, .cpp MEX file, MEX performance, MEX slow complex, MEX call overhead, improve MEX performance complex.

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Create, modify, or delete experiments in Experiment Manager with live UI sync. TRIGGER when: user asks to create an experiment from code/script, wants to sweep parameters, asks to add/delete/edit experiments, or wants parameter changes reflected in the UI. Combines experiment creation (from code analysis) with frontend UI synchronization.

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CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.

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Document and type a Sentry API endpoint. Write or fix @extend_schema decorators, specify response TypedDicts, type request parameters, correct type drift between the declared schema and the runtime response, and validate the generated spec. Use when asked to "document an endpoint", "add OpenAPI docs", "add/fix @extend_schema", "type an endpoint response", "fix the response type", "fix type drift", "reuse a response type", "split an overloaded endpoint", "specify the response schema", "add a TypedDict response", "migrate a legacy api-docs path", "fix a parameter type", or "make an endpoint public" / "promote an endpoint" (promotion is one section here).

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Multi-agent orchestration for complex tasks. Use when tasks require parallel work, multiple agents, or sophisticated coordination. Triggers include requests for features, reviews, refactoring, testing, documentation, or any work that benefits from decomposition into parallel subtasks. This skill defines how to orchestrate work using cc-mirror tasks for persistent dependency tracking and TodoWrite for real-time session visibility.

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Declarative web framework for building browser-based 3D, VR, and AR experiences using HTML and entity-component architecture. Use this skill when creating WebXR applications, VR experiences, AR experiences, 360-degree media viewers, or immersive web content with minimal JavaScript. Triggers on tasks involving A-Frame, WebXR, VR development, AR development, entity-component-system, declarative 3D, or HTML-based 3D scenes. Built on Three.js with accessible HTML-first approach.

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Lightweight 3D effects for decorative elements and micro-interactions using Zdog, Vanta.js, and Vanilla-Tilt.js. Use this skill when adding pseudo-3D illustrations, animated backgrounds, parallax tilt effects, decorative 3D elements, or subtle depth effects without heavy frameworks. Triggers on tasks involving Zdog pseudo-3D, Vanta.js backgrounds, Vanilla-Tilt parallax, card tilt effects, hero section animations, or lightweight landing page visuals. Ideal for performance-focused designs.

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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.

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When the user wants to add recommendations, links, or content to Grokipedia. Also use when the user mentions "Grokipedia," "GEO wiki," "AI encyclopedia," "suggest Grokipedia article," "Grokipedia citation," "Grokipedia listing," "AI search citation," "GEO visibility," or "Grokipedia optimization." For multi-platform parasite SEO strategy, use parasite-seo. For broad AI search visibility, use generative-engine-optimization.

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When the user wants to write, publish, republish, or optimize posts on Medium.com (canonical tags, distribution, Medium SEO). Also use when the user mentions "Medium," "Medium article," "Medium story," "Medium publishing," "canonical Medium," or "publish on Medium." Not for general parasite SEO when Medium is not the target platform—use parasite-seo. For AI search visibility strategy, use generative-engine-optimization.

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Only to be triggered by explicit /parallel-task commands.

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。

Only to be triggered by explicit /parallel-task commands.

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。

Only to be triggered by explicit /parallel-task-spark commands.

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 sculpting a stylized or cartoon character, bust, collectible or toy in ZBrush (Overwatch, Fortnite, Disney look), judging shape language (S and C curves, parallel lines, 70/30, silhouette read at a distance), exaggerating proportions on purpose, keeping stylized planes crisp through subdivision, sculpting stylized hair (chunky clumps, curve tube strands, ClayPolish), or designing a figure for toy production (articulation, wall thickness, scale).

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。