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概要と使いどころ
Render a deterministic text-only video ad from approved copy, font, colors, timing and CTA. Use when a brand needs an animated message without product photography or a creator.
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Render a 'brand identity reveal' video from a config — a single poster frame in a real, softly-lit space (real wall, soft-focus plant in the corner, dappled leaf shadow, illuminated poster) whose artwork HARD-CUTS through ~10 on-brand poster mockups (hero product, IG post, hanging banners, sticker sheet, logo lockup, poster, two lifestyle stills, packaging, big icon) then holds on a brand end card. The environment plate is one create-image-fal generation; the mockups are real-DOM HTML frame-stepped via Playwright, perspective-composited into the detected frame quad with the plate's real leaf-shadow multiplied back onto each poster (reads as behind glass), sequenced by FFmpeg. Deterministic assembly, FREE (the plate comes from create-image-fal, the bed from create-music-elevenlabs), music bed only and approved brand copy only. Use for the brand-identity-reveal format.
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Render a two-host podcast ad from a brand config and dialogue script. Includes the working planner, full-frame and split-screen assembly, captions from measured character timings, brand end card, approved paid-step adapters, and 65 quality-check falsification cases. Use for short conversational ads with two stable hosts in one room.
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Ports HDInsight Spark and Hive workloads to Fabric, converting HiveContext and SparkContext to SparkSession, WASB and ABFS paths to OneLake shortcuts, Hive DDL to Delta tables in a Lakehouse, and Oozie coordinators and actions to pipeline activities and triggers. Use when the source workload is HDInsight.
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Estimates Fabric capacity cost before a migration by profiling Spark, SQL, Power BI, and Real-Time workloads, then recommending SKU size and comparing Reserved, Pay-As-You-Go, and Autoscale billing. Use only for capacity sizing and pricing questions, not for general migration or connectivity work.
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Governs Event Schema Sets, the Fabric registries of event types and message payload schemas. Covers create, rename, update and delete of a definition, and listing, property reads or base64 decoding. Every Event Schema Set task belongs here. Ingestion pipelines are eventstream-cli.
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Prove the next page of a list, or the next batch of an infinite scroll, loads NEW rows from the server, repeats none, and that the end of the list is handled. Use when pagination or infinite scroll was added or changed, when a list shows the same rows twice or skips some, or when the last page still offers a next control.
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Find out why something in a running web app does not work, when the console is empty and the code looks correct. Reads the click, the request, the store and the console together and returns the file:line to open. Use when a button does nothing, a form will not submit, data will not load, a page renders blank or stale, a modal will not close, or the user says "it's broken" and the code review says it is fine.
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Create, populate, and manage safety analysis spreadsheets (FMEA, FHA, HARA, custom) and fault trees (FTA) in Safety Analysis Manager. Use when the user asks to perform FMEA, hazard analysis, fault tree analysis, safety analysis, or work with Safety Analysis Manager documents. Requires Simulink Fault Analyzer.
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Use this skill when the user asks to summarize, or asks why Simulink Coverage objectives are missing, unsatisfied, or uncovered on a coverage result they already have — why specific decision, condition, MCDC, relational-boundary, or saturation/overflow outcomes weren't exercised, whether uncovered logic is dead code, or how to close the gap (a test, a coverage filter, or a design change). It reads an existing cvdata, .cvt file, sim-with-coverage output, or Simulink Test result; it never runs cvsim to collect coverage.
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。
Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.
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Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.
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Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.
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Discovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-split step, orthogonal CAGE/poly-A/short-read-junction validation, and why long-read isoform quantification needs EM. Use when building a full-length isoform catalog, classifying/filtering long-read transcripts, running Iso-Seq or ONT cDNA/dRNA analysis, or judging novel-isoform reliability.
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Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline; DELFI is a method, not a package. Use when deriving fragment-based signal from cfDNA, choosing a feature family for detection vs subtyping, or diagnosing why a fragmentomic model failed validation.
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Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.
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Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification. Covers why the model string is the experiment (no auto-detection, silent degradation on mismatch), why ONT homopolymer/STR indels are the residual error whole-genome F1 hides, and the somatic/trio/RNA boundary to the ClairS/Clair3-Trio family. Use when calling germline SNVs/indels from ONT or HiFi BAMs, choosing a Clair3 model, phasing variants, or benchmarking long-read calls.
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Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse). Covers why N50 is the most-gamed metric, why QV measured on the polishing reads is circular, distinguishing uncollapsed haplotigs from real WGD, and the EBP/VGP 6.C.Q40 standard. Use when judging whether an assembly is good enough to annotate or publish, comparing assemblers, diagnosing a fragmented or duplicated assembly, or assessing a phased diploid assembly.
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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
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Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.
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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
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Assembles haplotype-resolved diploid and telomere-to-telomere (T2T) genomes from PacBio HiFi reads with hifiasm (HiFi-only, Hi-C, or trio phasing) and verkko (HiFi + ultralong ONT for T2T), extracting contigs from GFA and routing phasing QC to k-mer/trio metrics. Covers why a primary assembly is a haplotype mosaic that exists in no cell, partial-vs-full phasing (the .bp. vs .dip. filename convention), the purge-default trap on inbred samples, the --hom-cov coverage-estimate alarm, and verkko-vs-hifiasm for T2T. Use when assembling a diploid eukaryote from HiFi, phasing haplotypes with parents (trio) or Hi-C, deciding whether to chase T2T, or diagnosing switch errors invisible to N50/BUSCO/QV.
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Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.
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Download raw sequencing reads from NCBI SRA using sra-tools (prefetch, fasterq-dump, vdb-validate) or the ENA mirror. Use when pulling FASTQ for SRR/ERR/DRR accessions, deciding between SRA-direct, ENA mirror, or AWS/GCP cloud mirror (STRIDES), handling --include-technical for 10x and other single-cell records, validating with MD5/vdb-validate, navigating SRR/SRX/SRS/SRP/PRJNA hierarchy, or finding accessions via pysradb. Encodes SRA cloud-egress economics, the fasterq-dump uncompressed-scratch trap, and the --max-size default that silently truncates large prefetches.
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。