「profiles」の検索結果

Look up Twitter/X profiles - get bio, followers, tweets, and engagement

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。

Get LinkedIn profiles, company pages, and posts

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Pull real SEO metrics for any domain using Apify scrapers for Semrush and Ahrefs data. Gets domain authority, organic traffic estimates, keyword rankings, backlink profiles, top performing pages, and auto-discovers competitors from keyword overlap. No Semrush/Ahrefs subscription needed — uses Apify actors that scrape public pages.

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。

Research LinkedIn profiles and write personalized messages for any LinkedIn message type — connection requests, InMails, DMs, message requests, post comments, and comment replies. Takes LinkedIn URLs as input, researches each person (profile data + recent posts via Apify), and generates messages tailored to each lead's background, interests, and recent activity. Exports tool-ready CSVs for Dripify, Expandi, Botdog, PhantomBuster, or generic format. No LinkedIn cookies or login required.

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。

Look up information about a person - work history, social profiles, contact info

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。

Find ICP-fit leads from KOL audiences on LinkedIn. Given a list of KOLs, scrapes their most relevant high-engagement post from the last 30 days, extracts engagers (reactors + commenters), pre-filters by position, enriches top profiles, and ICP-classifies. Cost-controlled: 1 post per KOL. Use when someone wants to "find leads from KOL audiences" or "scrape engagers from influencer posts" or after running kol-discovery.

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。

Intelligence and enrichment - person and company data, social profiles, events, and funding

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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."

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。

Find emails, phone numbers, and enrich profiles using ContactOut. LinkedIn enrichment, people search, company enrichment, and batch operations.

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。

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.

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。

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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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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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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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.

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。

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.

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。

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.

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。

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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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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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 asking about Axiom skills or axbuild build/test capture, xclog console, xcsym .ips/MetricKit/.crash symbolication, xcui simulator UI/accessibility, or xcprof xctrace/CPU profiles.

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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.

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Use when capturing or analyzing ETTrace profiles for a focused iOS launch or runtime flow, including exact-build dSYM UUID matching, Simulator or device capture, processed per-thread flamegraph JSON, sampled inclusive/exclusive time, unresolved symbols, and comparable verification. Use debugging-instruments or swiftui-performance for generic profiling instead.

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。

Compute aerospace environment properties including atmosphere (ISA, COESA, NRLMSISE-00, non-standard, CIRA), gravity (spherical harmonic, WGS84, zonal, centrifugal), horizontal wind (HWM), magnetic field (WMM, IGRF), geoid height, geocentric radius, space weather data, planetary ephemeris, Earth orientation (polar motion, nutation, delta-UT1, CIP). Use when computing atmospheric density, temperature, pressure, gravity vectors, wind profiles, magnetic field components, geoid undulation, solar flux indices, planet positions, or Earth orientation parameters for aerospace vehicle analysis, spacecraft environment modeling, or navigation corrections.

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。