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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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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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Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.

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E-commerce growth strategy advisor. Diagnoses current business health using unit economics (CAC, LTV, AOV, contribution margin), identifies the highest-impact growth opportunities across 5 levers (traffic, conversion, AOV, retention, expansion), and builds a prioritized 90-day growth roadmap. Uses the Ansoff Matrix adapted for e-commerce to evaluate market penetration, channel expansion, product expansion, and new market entry. Includes multichannel readiness assessment (Amazon, Walmart, TikTok Shop, Etsy, DTC/Shopify/Shopify) and product line expansion analysis. No API key required. Use when: (1) planning next phase of e-commerce growth, (2) deciding whether to expand to new channels or products, (3) diagnosing why growth has stalled, (4) prioritizing what to fix or build next.

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Expert ITAR compliance advisor for US defense contractors, exporters, and manufacturers. Use this skill for any question about 22 CFR Parts 120-130, the United States Munitions List (USML), DDTC registration, export license applications (DSP-5/73/94), Technical Assistance Agreements (TAA), Manufacturing License Agreements (MLA), brokering regulations (Part 129), deemed export rules for foreign nationals, technology control plans, voluntary disclosures, violation mitigation, jurisdiction determination (ITAR vs EAR), or US Munitions List category scoping. Trigger even if the user doesn't say "skill" — any ITAR or US defense export control question should use this skill.

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Expert ITAR compliance advisor for US defense contractors, exporters, and manufacturers. Use this skill for any question about 22 CFR Parts 120-130, the United States Munitions List (USML), DDTC registration, export license applications (DSP-5/73/94), Technical Assistance Agreements (TAA), Manufacturing License Agreements (MLA), brokering regulations (Part 129), deemed export rules for foreign nationals, technology control plans, voluntary disclosures, violation mitigation, jurisdiction determination (ITAR vs EAR), or US Munitions List category scoping. Trigger even if the user doesn't say "skill" — any ITAR or US defense export control question should use this skill.

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Complete sanctions and export control analysis tool — individual designations, sectoral sanctions, dual-use goods, payment systems (SWIFT/USD), and extraterritorial regimes (EAR/ITAR/ FDPR US, ECL China). Triggers: "is X sanctioned?", "can I do business with X?", "can I sell Y to Z?", "pétrole Russie", "biens à double usage", "gel des avoirs", "sanctions internationales", "liste OFAC", "liste UE", "double usage", "embargo", "licence d'exportation", "SWIFT", "paiement en dollars", "US goods", "FDPR", "EAR", "ITAR", "BIS", "ECL chine", "no re-export clause", "sanctions sectorielles", "puis-je travailler avec X?", or any transaction involving a foreign person, product, sector, destination country, or payment in USD/EUR. Always adapt to user profile. Always run for any sanctions or export control query — even vague or exploratory ones.

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Structure a direct experience into a full learning cycle with concrete experience, reflection, and conceptual transfer. Use when planning field trips, simulations, or practical tasks.

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Generates value-moment-first onboarding flows for iOS/macOS apps — the default architecture races a new user to the first felt experience of the app's promised outcome, branching on whether they can experience it right now or need to plan for later. The classic paged welcome-carousel tour is an explicit fallback for genuinely explain-first apps. Use when user wants to add onboarding, welcome screens, first-launch experience, or improve activation/trial conversion.

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Expert knowledge for Azure Import Export development including troubleshooting, limits & quotas, and security. Use when setting CMK via Key Vault, checking drive limits, validating prerequisites, or debugging Import/Export job failures, and other Azure Import Export related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files).

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Expert knowledge for Azure ExpressRoute development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring ExpressRoute circuits/gateways, BGP routing (incl. IPv6), FastPath, Global Reach, or MACsec/IPsec, and other Azure ExpressRoute related development tasks. Not for Azure Virtual Network (use azure-virtual-network), Azure Virtual WAN (use azure-virtual-wan), Azure VPN Gateway (use azure-vpn-gateway), Azure Internet Peering (use azure-internet-peering).

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Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

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Systematic experimental results analysis workflow for ML/AI research papers. Connects experimental data to publication-ready Results sections with statistical validation, visualizations, and quality checks. Triggers on "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization".

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Use F# Interactive (`dotnet fsi`) for .NET exploration, scriptable experiments, package-backed .fsx workflows, quick data transforms, and reproducible command-line probes. USE FOR: .fsx scripts, F# REPL work, #r nuget references, #load composition, interactive type exploration, and small strongly typed experiments before moving code into a project. DO NOT USE FOR: production application code that needs compiled project structure; C# scripting; long-lived automation better expressed as a normal CLI, test, or build target. INVOKES: run dotnet fsi, edit .fsx scripts, load project or source files, and validate snippets against the target SDK.

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Use when the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation strategies. Triggers on phrases like "design ablation", "plan experiment", "what experiments should I run", "baseline comparison", or "experiment matrix".

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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

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Report delta-adjusted notional exposure across all IBKR accounts. Calculates option deltas using Black-Scholes and reports long/short exposure by account and underlying. Use when user asks about delta exposure, portfolio risk, or directional exposure.

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Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.

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Assemble 2-3 complementary experts to collaboratively analyze anything. Experts work together to explore topics from multiple expert angles.

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Analyze detected vulnerabilities to assess realistic exploitability by examining control flow, input sources, sanitization logic, and execution context. Use when users need to: (1) Determine if a vulnerability is actually exploitable in practice, (2) Assess severity and impact of security issues, (3) Prioritize vulnerability remediation, (4) Understand attack vectors and exploitation conditions, (5) Generate exploitability reports with proof-of-concept scenarios. Focuses on injection vulnerabilities (SQL, command, XSS, path traversal, LDAP) with detailed analysis of reachability, controllability, sanitization, and impact.

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Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.

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Internal publisher module for /codeck. Exports deck to PDF or PPTX with post-export QA. User-facing export requests should enter through /codeck; this skill defines the export behavior.

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Design and execute structured exploratory testing sessions. Covers Session-Based Test Management (SBTM), charter writing, heuristic-based exploration (HICCUPS, FEW HICCUPS), bug discovery patterns, note-taking templates, and conversion of findings to automated tests. Use when: "exploratory testing," "SBTM," "manual testing," "bug hunting," "test charter," "heuristic testing." Not for: an AI browser agent autonomously exploring the app from a natural-language goal — use agentic-browser-testing. Not for: testing your product's own AI/LLM features — use ai-system-testing. Related: test-planning, ai-bug-triage, risk-based-testing, agentic-browser-testing.

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Expert 3D modeling specialist with deep knowledge of topology, UV mapping, game-ready and film-ready pipelines, DCC tool workflows (Blender, Maya, ZBrush, 3ds Max, Houdini), retopology, LOD systems, and export pipelines. This skill represents years of production experience distilled into actionable guidance. Use when "3d model, 3d modeling, mesh topology, uv unwrap, uv mapping, retopology, retopo, low poly, high poly, subdivision, subdiv, edge flow, edge loops, polygon modeling, box modeling, hard surface, organic modeling, sculpting, zbrush, blender modeling, maya modeling, 3ds max, LOD, level of detail, game ready mesh, film ready, baking normals, high to low, fbx export, gltf export, texel density, 3d, modeling, topology, uv, game-dev, vfx, blender, maya, zbrush, retopology, lod, hard-surface, organic, sculpting" mentioned.

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