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概要と使いどころ
Research a company or idea, define the Ideal Customer Profile, and route to the right next step — either mapping the TAM or finding leads/prospects directly. The entry point for any "find me leads", "map my market", or "who should I sell to" request. Auto-loads when a user provides a company URL or idea and asks for leads or market mapping.
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Extract commenters from LinkedIn posts via Apify. Returns commenter names, titles, LinkedIn profile URLs, and comment text. Use to find warm leads engaging with relevant discussions. No LinkedIn cookies required.
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Find warm leads by searching LinkedIn for pain-language posts — the frustrations, complaints, and operational struggles your ICP talks about publicly. Asks clarifying questions to understand your product, ICP, and their pain points, then generates pain-language search keywords, scrapes LinkedIn for posts and engagers, enriches profiles, and ICP-filters the results. Use when someone wants to "find leads who are complaining about X" or "find people discussing problems we solve" or "LinkedIn pain-based prospecting."
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Competitor intelligence system. Research competitors across web, Reddit, Twitter/X, LinkedIn, and blogs. Build deep competitor profiles, monitor content and positioning changes, track what gets traction, and identify competitive gaps. Covers data collection, content tracking, and strategy analysis. Pure research skill — uses web search, web fetch, and optionally Apify for social scraping. No scripts required.
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Final QC gate for a rendered 9:16 video ad, run before it is published. One script checks what a machine can decide — exact 1080x1920 size, a hook that moves and speaks in the first second, no frozen stretches, no dead air, no black frames, the brand's real logo on the end card (never a favicon, never a redrawn or wrong logo), end-card colours near the brand palette — and builds one contact sheet (frames with the TikTok/Reels UI safe zones shaded, beside the logo, product images and a font specimen) for the checks that need eyes — font, product likeness, product consistency across scenes, safe zones. A declared silent-text profile covers silent, text-led formats such as kinetic text (no audio track needed, short blank beats between text beats allowed within fixed bounds). Exit 0 PASS / 2 FAIL / 3 ERROR. Use on every finished video master before pinning it.
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Analyze a company's published content to extract their brand voice, writing style, and tone guidelines. Reads 10-20 of their best content pieces and produces a brand voice profile covering tone, vocabulary level, sentence structure, formatting patterns, CTAs, and target persona. Useful before writing outreach, content, or campaigns that should match a client's existing voice.
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Monitor competitor content across blogs, LinkedIn, and Twitter/X on a recurring basis. Surfaces new posts, trending topics, and content gaps you can own. Chains blog-feed-monitor, linkedin-profile-post-scraper, and twitter-mention-tracker. Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement.
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Configure Simulink models for Embedded Coder (ERT), Simulink Coder (GRT rapid-prototyping), or AUTOSAR code generation. Use when the user asks to generate embedded C or C++ code, run a full build of a model, produce a code generation report, configure a model for production/ECU deployment or rapid-prototyping code, target ARM or x86 hardware, apply MISRA C/C++ compliance (ERT/AUTOSAR only — Simulink Coder does not ship MISRA profiles), or set up GRT, ERT, AUTOSAR, or shared-library targets. Handles target selection, hardware mapping, model hierarchy propagation, and constraint introspection via the configure_for_codegen function. Do NOT use for GRT shared-library variants (grt_malloc.tlc), DDS, or ROS, or for iterative optimization workflows that measure baseline metrics, apply targeted changes, and re-measure to confirm improvement.
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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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Segment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting over/under-segmentation.
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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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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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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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Profiles a genome from raw reads BEFORE assembly with a k-mer spectrum (KMC or Jellyfish histogram), then models it with GenomeScope2 to estimate genome size, heterozygosity, repeat content, and ploidy, and Smudgeplot to infer ploidy from heterozygous k-mer pairs (diploid AB vs triploid AAB vs tetraploid AABB). Covers choosing k via Merqury best_k.sh, the k-mer-coverage vs sequencing-coverage confusion, reading het/repeat/contamination/organelle peaks, why noisy ONT must not be used for counting, and how the estimate becomes the NG50 denominator, the Flye -g value, the hifiasm --hom-cov/purge setting, and the 1.5-2x-too-big haplotig sanity check. Use when starting any de novo assembly, deciding whether short reads can work, estimating genome size for an unknown organism, diagnosing ploidy, or sanity-checking an assembly's size against expectation.
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Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.
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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.
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Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
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Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.
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Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
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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 2024 bioRxiv; 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.
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Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
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Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.
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Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
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Use when storing credentials securely, encrypting data, implementing passkeys, securing AI/agentic features against prompt injection, code signing, or managing certificates and provisioning profiles.
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。