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Unwrap layers of obfuscated PowerShell malware to recover hidden commands, additional malicious code, and server addresses. Combines script structure analysis with execution tracing and decoding tools to support incident investigation and detection work.
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Create a Power Apps Code App starter project with React and TypeScript. Set up development tools, authentication, connector examples, and deployment scripts, with documentation covering local development and Power Platform configuration.
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Connect an AI agent to Power Automate through FlowStudio MCP. This skill covers authentication, discovering available tools, reusable Python or Node.js helpers, and extracting useful data from responses too large to display directly.
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读取 superpowers 会话记录，调查重复工作、偏离计划、耗时及令牌消耗。以文件路径和行号为依据整理经过，生成有证据支持的报告。
Investigate suspicious PowerShell activity using endpoint monitoring data and security logs. Examine encoded commands, downloads, and attempts to evade inspection, then document evidence, affected systems, risk, and recommended response actions.
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Build presentation slides in HTML, preview them in a browser, and export PDF or PPTX files. Also create fully editable PowerPoint decks or modify existing templates, with checks for content, layout, and rendering issues.
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将演讲笔记或PowerPoint文件制作成可在浏览器中播放的HTML幻灯片，通过预览选择设计，并调整布局和动画，使每页内容完整显示在屏幕内。
Turn business requirements or meeting transcripts into a Power Platform solution design. The skill recommends components, explains how people and data move through the process, and can provide a Mermaid.js diagram to illustrate the proposed architecture.
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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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Best practices for building and maintaining applications with PowerSync: Cloud and self-hosted setup, sync configuration, client SDK usage, backend integration (Supabase, custom Postgres, MongoDB, Azure DocumentDB, MySQL, MSSQL), schema changes, watch and reactive queries (useQuery), attachments, the upload queue, and debugging. Use this skill whenever the user mentions PowerSync, a @powersync/* package (@powersync/web, @powersync/react-native, @powersync/node), the powersync Flutter/Dart package, the powersync Rust crate, wa-sqlite, offline-first sync, local-first architecture, sync rules, sync streams, uploadData, fetchCredentials, disconnectAndClear, logout or user switching in a synced app, real-time data replication, Electric Cloud migration, Electric Shapes, an app stuck on Syncing, data not syncing, or wants to add offline-capable sync to a mobile or web app, even if they do not explicitly name PowerSync.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Create and edit PowerPoint presentations with PptxGenJS through an agent workflow. Use it to build a deck from scratch or request presentation edits, with the full workflow provided by the upstream bundle.
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Create, edit, and extract content from PowerPoint presentations and templates. Combine or split decks, work with speaker notes, and build slides with consistent themes, reusable layouts, and charts that remain editable in PowerPoint.
中文简介正在准备中。当前显示英语简介。
Rebuild slide images, screenshots, scanned PDFs, or image-only presentations as editable PowerPoint files. Reconstruct text and simple shapes as separate objects, retain complex visuals as image assets where needed, and check rendered slides against their sources.
中文简介正在准备中。当前显示英语简介。
Organize Power Automate flows by business impact and responsible team using FlowStudio’s cached records. Find resources without active owners, audit connections to other services, review compliance metadata, and configure notifications for failures or missing runs.
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Build detections for fileless attacks on Windows using process activity, script logs, and endpoint monitoring. Investigate suspicious PowerShell, code injected into running processes, WMI persistence, and registry-based execution with logging guidance and detection query examples.
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Analyze Windows PowerShell event logs to reconstruct scripts split across records and identify encoded commands, suspicious downloads, and security scanning bypass attempts. Produce a report with recovered scripts, risk scores, and mappings to documented attack techniques.
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Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs (DiD/event-study, IV, panel). Use when user says "power analysis", "power calculation", "MDE", "minimum detectable effect", "how big a sample do I need", "is my study powered", "power for an RCT", or when /preregister needs a power section for an experiment. Produces a power/MDE table, power curves, and a methods paragraph to paste into a preregistration.
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Convert an HTML slide deck into a PowerPoint file with editable text, shapes, and images. The skill handles font substitutions, imports speaker notes, and checks export warnings to help identify missing images or incorrectly captured slides.
中文简介正在准备中。当前显示英语简介。
Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.
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Create an editable, single-slide PowerPoint research poster from author-approved local text and images. Check physical dimensions, image resolution, contrast, reading order, and file security, with reports to support manual review and printing.
中文简介正在准备中。当前显示英语简介。