577 件 ・ 関連度順
概要と使いどころ
Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, Mean-Variance/SOCP variance caps, efficient frontiers, scenario generation, or NVIDIA cuOpt.
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。
Format CVs for academic positions with publications, grants, and teaching
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。
Use medical cv resume builder for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.
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。
Plan a Chinese campus recruitment season (校招: 秋招 and 春招), including internships that convert to offers (实习转正): the timeline, a target company list, a one-page campus CV, online application answers, written tests and interview rounds, and choosing between offers and signing the tripartite agreement (三方协议). Use when asked 秋招怎么准备, 帮我做校招计划, 校招简历, 网申问题怎么写, 群面, 三方协议, 怎么选 offer, or plan my campus recruitment in China. Produces a season timeline to confirm with each company, a tiered company list, a campus CV, application answer drafts, a test and interview preparation plan, and an offer comparison.
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。
Use when deciding whether a project is a genuine ACM MM (ACM Multimedia) contribution rather than single-modality work, choosing a thematic area, and routing between ACM MM, CVPR/ICCV, ACL/EMNLP, ICMR, MMSys, NeurIPS/ICLR, and the ACM TOMM journal by finding the cross-modal or media-systems core of the contribution.
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。
Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.
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。
Set up and run wireless network simulations using the Wireless Network Toolbox. Use this skill when creating system-level simulations with wirelessNetworkSimulator, adding traffic sources (networkTrafficOnOff, networkTrafficFTP, networkTrafficVoIP, networkTrafficVideoConference), configuring node mobility, logging events or IQ samples, visualizing traffic, scheduling actions during simulation, running parametric sweeps, writing PCAP files with pcapWriter or technology-specific PCAP writers, registering event callbacks on wireless nodes, accessing event data fields, or combining multiple capture/visualization tools in a simulation.
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。
Write a resume / CV or cover letter tailored to a specific role. Handles regional format differences (AU/NZ, US, UK), ATS-friendly formatting, achievement-focused bullets, and cover letter structure. Use whenever the user mentions a job application, resume, CV, cover letter, career document, applying for a role, or needs help framing their experience for a specific job.
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。
Use when a user asks what the EU Cyber Resilience Act (CRA) means for their product, whether and when they must report a vulnerability or incident, or what a specific CVE triggers legally. Maps a product with digital elements to CRA scope, product classification, Annex I vulnerability-handling duties, and Article 14 reporting obligations — every legal claim cited from official regulation text fetched live through the Ansvar Gateway MCP connector, joined with live CVE / CISA-KEV / EPSS vulnerability intelligence from the same connector.
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。
Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz
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。
Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and '
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。
全面的代码安全检查和服务器安全审计skill。适用于：(1) 代码漏洞扫描 - 检测SQL注入、XSS、SSRF等OWASP Top 10漏洞，(2) 依赖安全检查 - 识别过时或有漏洞的第三方库，结合实时搜索确认最新CVE，(3) 服务器配置审计 - 检查SSH、防火墙、权限等安全配置，(4) 敏感信息泄露检测 - API密钥、密码、令牌等硬编码检测，(5) 容器安全扫描 - Docker镜像和Kubernetes配置审计，(6) CI/CD安全检查。触发关键词："安全检查"、"漏洞扫描"、"代码审计"、"security audit"、"vulnerability scan"、"SAST"、"dependency check"、"CVE检测"等。不用于：修复单个已定位的bug、编写新的安全功能代码、对无授权的第三方系统做扫描或渗透测试。
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。
Provides multi-school trading Q&A, chart/OHLCV analysis, annotation, and fresh-market workflows covering ICT/SMC, ChanLun, Wyckoff, Price Action, Order Flow, VSA, and Elliott Wave. Use for trading concepts; capability-discovery questions about available analysis lenses, Schools, models, modes, or data sources; attached charts; pasted OHLCV; chart annotation; or asset-plus-timeframe requests across crypto, stocks, or forex. Defaults unselected market analysis to strict ICT/SMC, honors explicit School/source selectors, and fails closed when no native analyzer exists. Phase 1 comparison is Q&A-only; fetch fresh Mobius Quant API data only after the capability gate.
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。
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
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。
Audit project dependencies, frameworks, languages, and dev tools for known vulnerabilities, CVEs, and security anti-patterns. Use when the user mentions 'dependency audit,' 'npm audit,' 'CVE,' 'vulnerable packages,' 'supply chain security,' 'outdated dependencies,' 'known vulnerabilities,' 'security advisory,' 'package security,' 'framework vulnerability,' 'is this package safe,' or needs to check whether their stack has known security issues.
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。
Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging
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。
Use when attacking a Windows Active Directory domain — Kerberos roasting/delegation, coercion + NTLM/Kerberos relay (CVE-2025-33073), ADCS ESC1-16 (EKUwu), ticket forgery & DCSync, dMSA BadSuccessor (CVE-2025-53779), BloodHound attack-path enumeration, domain dominance
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。
Use when attacking an AI/ML system or model — prompt injection & jailbreaks (Crescendo, Skeleton Key, Best-of-N), RAG/vector poisoning, agentic/MCP exploitation (CVE-2025-54136), ML supply-chain RCE (pickle CVE-2025-32434), model extraction / membership inference / adversarial suffixes (GCG)
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。
Use when attacking a network or moving laterally — L2/L3 poisoning (LLMNR/mDNS, ARP/DHCP, mitm6), coercion + NTLM relay (CVE-2025-33073), TUN pivoting (Ligolo-ng/Chisel), MitM, network-service RCE (CVE-2024-38077), WPA2/WPA3 wireless
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。
Use when escalating privileges on a Linux host — SUID/SGID & GTFOBins, sudo LPE (CVE-2025-32462/32463), capabilities & LD_PRELOAD, kernel LPE (CVE-2024-1086, Dirty Pipe, GameOver(lay)), service misconfig (PwnKit, Looney Tunables), container/namespace escape
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。
Use when crossing a Windows security boundary or escaping a sandbox — kernel/user crossing (win32k/dxgkrnl UAF CVE-2025-24983), BYOVD kernel R/W, UAC/COM elevation, AppContainer/LPAC & Chromium-Mojo sandbox escape (CVE-2025-2783), PPL bypass, RPC/ALPC & named-pipe impersonation
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。
Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.
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。
Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.
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。
Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.
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。