「profiles」の検索結果

Source technical and professional talent by combining LinkedIn profiles, GitHub contributions, and company intelligence. Identifies candidates with specific skills, assesses expertise depth, and provides outreach context for recruiting teams.

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Research GitHub user profiles including repositories, contributions, languages, stars, and activity patterns. Useful for developer talent sourcing, open-source community research, and technical due diligence on engineering teams.

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Research any topic across the last 30 days using Reddit, Twitter, YouTube, Hacker News, web search, and news sources through SandBase. Use when asked for recent trends, launch reactions, competitor activity, person/company profiles, or current event analysis.

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Search content, analyze profiles, track hashtags, and research competitors on Instagram through SandBase. Use when asked for Instagram research, influencer analysis, hashtag tracking, content strategy research, or competitor social monitoring.

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Search companies, enrich profiles with firmographic data, find job postings, and track company news through Apollo. Covers company search, detailed enrichment (industry, size, funding, tech stack), hiring activity, and news monitoring for B2B sales intelligence.

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Find and prioritize ethical backlink and digital-PR opportunities using SandBase DataForSEO backlink data. Use when asked to analyze backlink profiles, find referring-domain gaps, compare link competitors, or prepare a link-outreach target list.

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Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin analysis.

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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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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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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 2025 Nat Genet; 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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Detect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data.

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SEC filings, company news, federal contracts, and company profiles. 4 tools for competitive intelligence.

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Use this skill when authoring reusable, idempotent MATLAB scripts that build System Composer architecture models via the architecture-modeling API — `systemcomposer.createModel`, `addComponent`, `addPort`, `setInterface`, `connect(srcPort, dstPort)`, interface dictionaries (.sldd) with `addInterface`/`addElement`, profiles/stereotypes with `Profile.createProfile` and `addStereotype`, or `systemcomposer.allocation.createAllocationSet`. Also trigger when debugging these APIs (connections that don't appear, interfaces that don't resolve, profile save errors, `createAllocationSet` signature-mismatch errors). Do NOT trigger for ad-hoc structural edits to an already-built model (adding one SubSystem, rewiring a port) — use `building-simulink-models` with `model_edit` for that.

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Use this skill for the architecture phases of an MBSE workflow in MATLAB, when writing idempotent buildXxx.m scripts that produce a three-layer RFLPV architecture (Functional, Logical, Physical) with interface dictionaries, stereotype profiles, allocation sets, and requirements Implement links. Trigger for defining stereotype properties, functional-to-logical / logical-to-physical allocation, mapping requirements to components via slreq Implement links, or running quantitative roll-up analysis on the architecture. Do NOT trigger for ad-hoc structural edits to an already-built System Composer model (adding one component, rewiring a port) — use `building-simulink-models` with `model_edit` for that. Works alongside the `system-composer` skill for detailed SC API patterns.

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Test application performance with k6 load/stress/soak/spike scripts and k6 scenarios, Lighthouse CI for Web Vitals, and performance budgets as CI gates. Covers load profiles, custom metrics, bottleneck identification, and Core Web Vitals (LCP, INP, CLS). Use when: "performance test," "load test," "stress test," "soak test," "spike test," "k6," "k6 scenarios," "Lighthouse," "Web Vitals," "Core Web Vitals," "performance budget." Not for: scheduled production probes — use synthetic-monitoring; pixel-diff regressions — use visual-testing; designing tests from prod telemetry — use observability-driven-testing. Related: ci-cd-integration, qa-metrics, release-readiness.

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Cucumber runs behavior-driven (BDD) tests written as plain-language Gherkin feature files and matches each Given/When/Then step to code. Use when the user wants to write feature files, implement step definitions in JavaScript or TypeScript with cucumber-js, share state through a World, add hooks, tags and profiles, or run BDD scenarios in CI. Also use when the user mentions "cucumber," "BDD," "Gherkin," "feature files," "given-when-then," "step definitions," or "behavior-driven." For contract testing, see pact.

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You are an expert in Clay, the data enrichment and outbound sales platform that pulls from 75+ data providers to build rich prospect profiles. You help teams automate lead discovery, enrich contacts with firmographic and technographic data, score leads against ICP criteria, and trigger personalized outreach sequences — replacing manual research with automated, data-driven prospecting.

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Build detailed ideal customer profiles with pain points, objections, buying triggers, and messaging angles. Includes community research to find where ICPs gather online and extract their exact language. Use when researching audiences, creating buyer personas, or developing targeted messaging.

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Use when an OpenClaw maintainer or owner is reviewing a ClawHub malicious-skill profile proposal PR: checking `proposals/<GHSA-ID>/clawscan.yml`, reading the private vulnerability context without leaking it, running the SkillTrustBench Profile Gate or equivalent local benchmark, updating the accepted baseline, and promoting an accepted candidate into `internal/profiles/clawhub/clawscan.yml`.

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Create refined user personas from research data with demographics, goals, frustrations, and behavioral patterns. Use when synthesizing user research into actionable persona profiles for design decisions.

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Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".

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When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.

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Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".

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。