1,317 件 ・ 関連度順
概要と使いどころ
Deobfuscates malicious PowerShell by decoding -EncodedCommand, reversing string and format obfuscation, resolving base64/gzip/IEX layers, and recovering the final payload and IOCs. Activates for requests to deobfuscate, decode, or analyze obfuscated PowerShell commands or scripts.
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。
Serena code intelligence — LSP-powered symbol navigation, diagnostics, and targeted code surgery. Activate before complex refactors, cross-file analysis, or when graph tools need symbol-level depth.
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。
Execute ENCODE DNase-seq pipeline from FASTQ to hotspots and footprints. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing DNase-seq data, calling DNase hypersensitive sites, performing footprinting analysis. Trigger on: DNase-seq pipeline, DNase hypersensitive, DHS, Hotspot2, footprinting, DNase I, chromatin accessibility DNase.
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。
Install bioinformatics tools for ENCODE data analysis. Covers CLI tools (BWA, STAR, samtools, MACS2), R/Bioconductor packages (DESeq2, Seurat, ChIPseeker), Python packages (Scanpy, deeptools), and Nextflow pipeline infrastructure. Generates conda environments, R install scripts, and Python requirements. Use when the user needs to set up a bioinformatics workstation, install tools for a specific assay, create reproducible environments, or troubleshoot dependency issues. Trigger on: install tools, set up environment, conda create, bioinformatics setup, install R packages, install Bioconductor, install pipeline 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。
Verify code-review or security-review findings for false positives using deep codebase tracing, framework-aware analysis, and web research. Produces a .verified.md report alongside the original. Use when a review report has been generated and needs independent verification, or when user runs /verify-findings, mentions "verify review", "check false positives", or "validate findings".
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。
Guideline for designing, implementing, and verifying secure TypeScript and JavaScript applications following OWASP Top 10 best practices. Use when the user wants to: (1) review TypeScript or JavaScript code for security vulnerabilities, (2) design a secure Node.js, Deno, or browser application architecture, (3) implement security features (authentication, authorization, cryptography, input validation), (4) audit npm/yarn/pnpm dependencies for known vulnerabilities, (5) create security checklists or verification plans, (6) fix security bugs or harden existing TypeScript or JavaScript code, (7) set up security testing and static analysis (ESLint security plugins, Semgrep, Snyk), or (8) handle any TypeScript/JavaScript security concern including injection prevention, prototype pollution, XSS protection, SSRF prevention, secrets management, and secure deployment.
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。
Guideline for designing, implementing, and verifying secure Python applications following OWASP Top 10 best practices. Use when the user wants to: (1) review Python code for security vulnerabilities, (2) design a secure Python application architecture, (3) implement security features (authentication, authorization, cryptography, input validation), (4) audit Python dependencies for known vulnerabilities, (5) create security checklists or verification plans, (6) fix security bugs or harden existing Python code, (7) set up security testing and static analysis (bandit, safety, semgrep), or (8) handle any Python security concern including injection prevention, secure deserialization, SSRF protection, secrets management, and secure deployment.
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。
Manages cloud infrastructure through natural language conversations with Pulumi Neo, an AI agent for platform engineers. Enables infrastructure analysis, resource provisioning, stack deployment, and configuration management via conversational AI. Use when creating Neo tasks, requesting infrastructure analysis, automating cloud deployments, managing infrastructure as code (IaC), provisioning AWS/Azure/GCP resources, managing infrastructure through natural language prompts, reviewing PRs with Neo, handling Neo approval workflows, or checking Neo task status and events. Also use when the user mentions "Pulumi Neo", "Neo task", "Neo agent", or wants AI-assisted infrastructure management.
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。
Conduct digital forensics investigations — evidence acquisition, timeline analysis, and court-ready documentation.
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。
Statistical analysis of extreme weather — EVT, return periods, thresholds, and detection/attribution of rare events.
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。
Design rigorous experiments — hypotheses, controls, randomization, power analysis, and preregistration. Use when planning any study where you need trustworthy causal conclusions.
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。
Build financial models — revenue forecasts, unit economics, scenario analysis, and investor-grade spreadsheets.
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。
Flow cytometry experimental design and analysis — panel design, compensation, gating, and QC.
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。
Evaluating diagnostic tests — sensitivity, specificity, ROC analysis, likelihood ratios, and study design.
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。
EEG preprocessing and analysis — filtering, artifact removal, time-frequency decomposition, and ERP 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。
Manage software composition analysis — SBOMs, vulnerability triage, and remediation of third-party dependencies.
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。
Analyze text data with NLP — preprocessing, embeddings, topic modeling, sentiment, classification, and evaluation. Use when turning unstructured text into structured insight.
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。
Copy-number variant analysis — detection from sequencing/arrays, segmentation, and clinical interpretation.
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。
Working with observational climate data — reanalyses, station records, anomalies, trends, and honest uncertainty.
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。
Using CMIP6 climate model output — finding data, multi-model means, scenario logic, and bias-aware 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。
Heterogeneous and homogeneous catalysis — mechanisms, kinetics, catalyst characterization, and deactivation 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。
Estimate causal effects from observational and experimental data — DAGs, confounding control, quasi-experimental designs, and sensitivity analysis. Use when you need "what causes what," not just correlation.
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。
Designing and analyzing cognitive tasks — Stroop, n-back, task-switching, and process-pure measurement.
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 structured career change with skill-gap analysis, bridge roles, and a de-risked transition timeline.
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。