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This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.

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SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.

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The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly fou

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mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.

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InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell comm

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Expert knowledge for Microsoft Foundry Tools (aka Azure AI services, Azure Cognitive Services) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when using Content Moderator, Content Safety, Content Understanding analyzers, REST/.NET APIs, or document extraction workloads, and other Microsoft Foundry Tools related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Local (use microsoft-foundry-local).

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Expert knowledge for Azure AI Video Indexer development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring live camera analysis, custom AI insights, OpenAI summaries, Video Indexer APIs, or face redaction, and other Azure AI Video Indexer related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Search (use azure-cognitive-search).

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Expert knowledge for Azure Artifacts development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, views, .npmrc auth, .artifactignore, or GitHub Actions package publishing, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Boards (use azure-boards).

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Expert knowledge for Azure Container Instances development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and deployment. Use when configuring ACI standby pools, static IP networking, GPU containers, secure ACR pulls, or GitHub Actions deploys, and other Azure Container Instances related development tasks. Not for Azure Container Apps (use azure-container-apps), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure App Service (use azure-app-service), Azure Virtual Machines (use azure-virtual-machines).

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Deep Amazon review analysis for competitive intelligence and product improvement. Extract sentiment patterns, recurring complaints, feature requests, and competitive insights from customer feedback. Turn reviews into actionable product development and marketing strategies. Use when the user asks about review analysis, customer feedback, product complaints, sentiment analysis, or what customers think about products.

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Amazon PPC campaign builder and optimizer for sellers. Two modes: (A) Build — design a complete campaign structure from scratch with keyword groupings, bid calculations, and negative keyword lists, (B) Optimize — audit existing campaigns using search term reports, identify keyword funnel opportunities, calculate bid adjustments, and generate a week-by-week action plan. Integrates with amazon-keyword-research for keyword input. No API key required. Use when: (1) setting up Amazon PPC campaigns for a new product, (2) auditing existing campaign performance and ACoS, (3) optimizing keyword bids and negative keywords, (4) building Auto/Manual/Exact campaign structures, (5) analyzing search term reports for opportunities, (6) calculating break-even ACoS and target ACoS, (7) scaling profitable campaigns to Sponsored Brands or Display.

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Account suspension prevention and appeal — policy violations, Plan of Action writing, reinstatement process

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Decode any Base transaction into a plain-English story - method, token movements, swaps/approvals, counterparties, and suspicious-approval flags. Keyless via Base RPC + Etherscan v2.

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Recap of everything shipped since the last run - cross-repo PRs, security fixes, star deltas, and X traction, synthesized into a digest article and a ready-to-post shiplog in your voice.

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Autonomous cloud-cost analyst across Neon, Vercel, Railway and GitHub Actions - pulls per-object usage, attributes it to the biggest drivers, root-causes each, and emits recommendations ranked by real signal (idle %, over-allowance, failure rate) with dollar figures only where the billing API returns real ones (and, armed, applies safe cost levers).

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One lifecycle poll over everything vuln-scanner produces - PR and advisory status, PVR triage transitions, and pending-disclosure aging, with a stars-secured impact headline and one action queue.

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Live-data research via the glim.sh MCP - web search, full page extraction, X/Twitter, Reddit, GitHub, Amazon, and YouTube transcripts - synthesized into a cited digest. Pay-per-call from the connected account balance; OAuth Connect via the dashboard MCP panel.

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Daily GitHub notification inbox triage - surfaces aging vuln PR replies, security advisories, review requests, and mentions that need action

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Access a Base Account via the Base MCP server (mcp.base.org) - wallet, portfolio, sending, swapping, signing, x402 payments, batched calls, and transaction history.

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Pre-send quality scorecard for any lead list. Grades duplicate rate, title diversity, bad-title patterns, catch-all domain density, ICP fit, email verification coverage. Outputs a letter grade + action items BEFORE you send. Catches bad lists before they burn inboxes. Run after email enrichment, before Smartlead upload.

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Diagnostic audit for a running cold email program. Checks domain authentication (SPF/DKIM/DMARC), inbox health/reputation from Smartlead, bounce rate by inbox type, and optionally runs a spam placement test via Smartlead's Smart Delivery API. Outputs markdown report + CSV with per-domain/per-inbox scores and concrete action items. Use when reply rates drop, when bounces spike, when onboarding someone else's account, or as a weekly/monthly health check.

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Use when you need to turn a product or service into a compelling, high-converting offer. Trigger on "offer", "why would someone buy", "improve conversions", "value proposition".

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Use when you need to define exactly who the buyer is, what they want, what they’ve tried, and what’s driving their decisions. Trigger on "target audience", "ideal client", "who is this for", "avatar", "customer profile".

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Read one conversation window and return every independently mutable claim it states, one record each, with who or what it is about, whether it happened or stands, and the words that carry its time. Used by the memory extraction model on every conversation window; the engine resolves dates, validates keys, and repairs format.

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