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Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.

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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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End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.

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TOP PRIORITY skill — find and immediately fix or remove every piece of wrong, outdated, or redundant information in ToolUniverse docs. Wrong code, broken links, incorrect counts, and overlapping instructions must be fixed or removed — never left in place. Runs five phases: (D) static method scan, (C) live code execution, (A) automated validation, (B) ToolUniverse audit, (E) less-is-more simplification. Core philosophy: each concept appears exactly once; remove don't add; no emojis; single setup entry point. Use when reviewing docs, before releases, after API changes, or when asked to audit, fix, or simplify documentation.

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When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ad copy,' 'ad creative,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' or 'audience targeting.' This skill covers campaign strategy, ad creation, audience targeting, and optimization.

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Video and audio processing capabilities for Flutter applications

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Write high-converting ad copy for Facebook, Google, LinkedIn, and TikTok ads. TRIGGERS - Use when user wants ad copy, paid media creative, or advertising text for any platform.

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Generate a vertical story image for TikTok or Instagram book promotion with cover art, hook text, and author branding.

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Generate a professional certificate image with recipient name, course title, and completion date for digital sharing, social media, or email delivery.

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Generate a formatted PDF compliance audit document with findings, risk ratings, remediation recommendations, and sign-off sections.

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Extract property listing details from a real estate document, then generate a branded PDF brochure and a social media image for the listing.

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You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.

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Plan accessibility compliance - WCAG 2.2, Section 508, EN 301 549, inclusive design principles, audit planning, and remediation strategies.

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Evaluate learning materials and technology for WCAG, Section 508, and accessibility compliance with remediation recommendations

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Validate digital outputs for compliance with accessibility standards (e.g., WCAG, Section 508). Use when reviewing reports, dashboards, documents, or interfaces from pilot projects to identify and remediate accessibility barriers.

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Scan an AEM Edge Delivery Services page for WCAG 2.1 AA accessibility violations and generate specific fixes. Identifies missing alt text, heading hierarchy issues, link text problems, color contrast concerns, and EDS-specific accessibility patterns. Use when fixing accessibility issues, preparing for compliance audits, or remediating WCAG violations.

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Production-grade accessibility skill for WCAG 2.2 AA compliance. Covers auditing, remediation, component authoring, and validation workflows. Auto-invoked for UI implementation, a11y fixes, and accessibility testing.

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Austrian Impressum (legal notice) requirements for all company types. Covers ECG, UGB, GewO, MedienG, and Offenlegungspflicht for public media. Use when working with impressum, legal notice, imprint, austria, österreich, ECG, mediengesetz, offenlegung.

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Use when you need to design, review, or improve REST APIs with Spring Boot — including HTTP methods, resource URIs, status codes, DTOs, versioning, deprecation and sunset headers, content negotiation (JSON and vendor media types), ISO-8601 instants in DTOs, pagination/sorting/filtering, Bean Validation at the boundary, idempotency, ETag concurrency, HTTP caching, error handling, security, contract-first OpenAPI (OpenAPI Generator), controller advice, and problem details for errors. This should trigger for requests such as Review Java code for Spring Boot REST API; Apply best practices for Spring Boot REST API in Java code. Part of cursor-rules-java project

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Generates RESTful API Controllers with proper routing, versioning, authorization, and MediatR integration. Follows REST conventions and Clean Architecture patterns.

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Perform a diff-scoped, behavior-neutral cleanup of AI-generated slop on the current branch before code review. Use for removing trivial comment, defensive-check, type-laundering, redundant-intermediate, compatibility-shim, or local style drift from the branch diff. Do not use for full correctness or safety review, documentation work handled by technical-documentation or docs-refactor, or repo-wide refactors.

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AVFoundation / AVKit / PhotoKit リファレンス。 AVPlayer, AVPlayerViewController, VideoPlayer, AVPlayerItem, AVMutableComposition, AVCaptureSession, AVCaptureDevice, AVCapturePhotoOutput, AVAudioEngine, AVAudioSession, AVAudioPlayer, AVSpeechSynthesizer, PHAsset, PHImageManager, PhotosPicker, PHPhotoLibrary。

Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.

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