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概要と使いどころ
Use when explaining or planning around AAMAS peer review, covering OpenReview review release, the double-blind rebuttal on preliminary reviews, area-chair discussion, the mixed game-theory, MARL, and systems reviewer pool, the public posting of reviews and decisions, and how acceptance criteria weigh the interaction contribution.
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Use when revising an AAMAS paper so the interaction contribution leads the first page, the solution concept or incentive property is named, the game and agents are legible, claims are paired with proofs or multiagent experiments, the argument fits eight two-column pages plus references, and the wording stays double-blind.
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Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
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Generates Instagram Reels where product image cuts are synced to audio beats. Accepts audio as a local file, URL, or search query. Uses librosa for beat detection, FFmpeg Ken Burns for scene animation, and Pillow for text overlays. No AI video generation — fully free, fast, and scalable.
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Builds personalized demo assets for top prospects using the founder's product API/MCP/SDK. Researches prospect, proposes demo concepts, builds working prototype, tests it, and generates comparison report with live demo link.
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Research a company's ideal customer profiles and build detailed synthetic buyer personas. Identifies 4-6 distinct buyer segments through web research, then creates rich, realistic personas with demographics, motivations, skepticism profiles, decision criteria, and language patterns. Use when you need to understand who your buyers are at a deep level — their motivations, objections, and how they evaluate solutions.
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End-to-end news-triggered signal composite. Takes any piece of news — an article, LinkedIn post, tweet, announcement, event, trend, regulation, product launch, acquisition, layoff, expansion, or any other public event — and evaluates whether the companies or people mentioned are ICP fits. If yes, identifies the connection between the news and your product, finds the right people to contact, and drafts personalized outreach using the news as the hook. Tool-agnostic. Accepts both company-level and person-level news triggers. AUTO-TRIGGER: Load this composite whenever a user shares a URL (LinkedIn post, article, tweet, blog post) or mentions a company/person they "came across", "saw", or "found" from any external source and asks about relevance, fit, ICP match, or whether to reach out. The user does NOT need to explicitly say "outreach" — any signal evaluation request from an external source triggers this.
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Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
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Find, show, open, or identify shipped MathWorks example models that demonstrate a concept, block, or application domain, and return the command to open them.
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Guidelines for making .NET libraries and applications trimming-safe and Native AOT compatible. Covers the trimming/AOT model, the MSBuild properties that enable analysis (IsTrimmable, IsAotCompatible, PublishTrimmed, PublishAot), the trimming attributes (RequiresUnreferencedCode, RequiresDynamicCode, DynamicallyAccessedMembers, UnconditionalSuppressMessage), IL2xxx/IL3xxx warning codes, and a pattern playbook: source generators and UnsafeAccessor, generated-interception diagnostic suppressors, intentional runtime scanning boundaries, trimming-safe islands and feature switches, migration analyzers, and temporary warning-approval baselines. Use when introducing trimming/AOT support, resolving IL2xxx/IL3xxx warnings, or making reflection-heavy code trimming-safe in C# / .NET codebases. For System.Text.Json AOT scenarios, see also the serialization skill.
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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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Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
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Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.
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Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
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Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.
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Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.
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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
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Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.
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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.
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Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
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Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary). Use when downloading sequences, fetching GenBank/GenPept records, getting document summaries, parsing nested XML, navigating GI deprecation, choosing between rettype+retmode combinations, and parsing into Biopython SeqRecord/SwissProt objects. Covers nucleotide, protein, gene, pubmed, sra, gds, taxonomy, snp, clinvar.
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。