1.2万 件 ・ 関連度順
概要と使いどころ
Advanced vulnerability analysis principles. OWASP Top 10 baseline + current threat landscape awareness, Supply Chain Security, attack surface mapping, risk prioritization. Use when performing security audits, threat modeling, or OWASP reviews. NOT for STRIDE/CSO-mode infrastructure audits (see mk:cso).
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。
Browser automation CLI for AI agents using agent-browser. Use for navigating websites, clicking/filling pages, screenshots, data extraction, web app testing, exploratory QA, dogfooding, Electron apps, Slack automation, Vercel Sandbox browser runs, AWS AgentCore cloud browsers, auth-heavy flows, and long autonomous browser sessions. Prefer over generic browser tools when a fresh/tool-managed Chrome session is fine. NOT for the user's real Chrome cookies/profile (see mk:chrome-profile); NOT for reusable Playwright specs (see mk:qa-manual).
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。
Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO review".
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。
Up-to-date Zig programming language patterns for version 0.17.0-dev (0.16.0 stable APIs plus the 0.17 deltas). Use when writing, reviewing, or debugging Zig code, working with build.zig and build.zig.zon files, or using comptime metaprogramming. Critical for avoiding outdated patterns from training data - especially std.net→std.Io.net (requires Io instance), std.time timestamps removed (use clock_gettime), std.Thread.Mutex/Condition/sleep removed (use pthreads), std.crypto.random removed, build system APIs (root_module, Compile methods→Module methods), I/O APIs (buffered writer pattern), container initialization (.empty/.init), allocator selection (DebugAllocator), ArrayList now unmanaged by default, @typeInfo lowercase fields (.@"struct" not .Struct), and removed language features (async/await, usingnamespace). Also covers 0.17.0-dev deltas: b.args→run_cmd.addPassthruArgs(), std.gpu→std.spirv, @bitCast logical-bit (endian-agnostic) semantics, build configurer/maker split, and the zig-pkg/ package directory. Also covers quality tooling Zig does not ship: coverage with kcov (and why in-file tests wreck the denominator), detecting unused private functions (neither the compiler nor zlint's unused-decls finds them), duplicate detection with jscpd, zlint (NOT the same-named X.509 linter in Homebrew), and the built-in fuzzer whose testOne now takes *std.testing.Smith rather than []const u8.
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。
Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or trimming low-quality bases, generating quality reports, or deciding which FASTQ quality encoding a file uses before parsing.
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 differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing, apeglm/ashr/normal LFC shrinkage, independent filtering, Cook's outlier handling, VST/rlog transforms, and design formulas including paired, batch, and interaction terms. Use when running bulk DE, choosing DESeq2 over edgeR or limma-voom, building a paired or interaction design, applying LFC shrinkage for ranking or GSEA, choosing Wald vs LRT, troubleshooting padj=NA, picking VST vs rlog, importing salmon/kallisto via tximport, or analyzing prokaryotic RNA-seq.
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。
Infers person-to-person transmission from pathogen genomes using outbreaker2, TransPhylo, phybreak, BadTrIP, SCOTTI, BEASTLIER, and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs; MRSA <=15; C. difficile <=2; Klebsiella <=21), models within-host diversity and transmission bottlenecks, integrates contact-tracing data, distinguishes generation from serial interval, and attributes source via Bayesian source attribution (islandR). Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after a dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB or chronic HIV.
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。
Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC. Use when handling 10X scATAC or 10X Multiome (paired RNA+ATAC) data, performing per-cell QC, choosing between ArchR/Signac/SnapATAC2 ecosystems, building per-cluster consensus peaksets, integrating with paired scRNA-seq, doublet detection (AMULET vs ArchR vs scDblFinder), or running pseudobulk differential accessibility per cluster.
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 differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.
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。
This skill guides evaluation of venous thromboembolism (VTE) risk factors in Cushing's syndrome patients to prevent thrombotic complications. Use when assessing a CS patient for thrombotic risk, especially perioperatively; triggers include preoperative assessment, postoperative period (within 4-6 weeks), or presence of CS with additional risk factors such as prior VTE, immobilization, malignancy, or estrogen use.
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。
When the user wants to create step-by-step technical tutorials, quickstarts, or code walkthroughs. Trigger phrases include "tutorial," "quickstart," "getting started guide," "walkthrough," "step by step," "how to guide," "hands-on guide," or "code tutorial."
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。
Three-tier memory layer for the orchestrator. Routes scratch (file-tier), WHERE (QMD), and WHY (MemPalace) through the existing code-memory-router slash command. Never edits planning files directly. Every WHY-tier promotion goes through the HITL 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。
Measure real frontend performance — Core Web Vitals (LCP, CLS, INP), page-load traces, CPU and network throttling, Lighthouse accessibility/SEO scores, and heap growth — using chrome-devtools-mcp against a live browser. Trigger when asked "is the page fast enough", "measure Core Web Vitals", "why is this route slow", "run Lighthouse", "check for a memory leak", before any performance-gated certification (Built for Shopify, Core Web Vitals thresholds, store listing review), and before any launch or traffic-multiplier event. Measurement only — this skill never drives functional journeys.
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。
Generate the ≤10-minute Loom walkthrough script for a completed Weekly Community Signal report — a plain spoken briefing that dives straight into what the team needs to know, walking the report top to bottom (CopilotKit page first, then AG-UI in the same order). Straightforward and factual, NOT a radio show or a performance; no hype opener, no blame framing. Runs after every report (invoked by weekly-report) and on "loom script", "walkthrough script", "record the loom", "narrate the report".
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。
Serve LLMs com alta throughput usando PagedAttention do vLLM e continuous batching. Use ao fazer deploy de APIs LLM em produção, otimizar latência/throughput de inferência, ou servir modelos com memória GPU limitada. Suporta endpoints compatíveis com OpenAI, quantização (GPTQ/AWQ/FP8) e tensor parallelism.
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。
国风 3D 网页渲染。当用户要"水墨风 3D""中国风网页特效""Three.js 水墨""国风 H5""青绿山水 3D""非真实感渲染 NPR""古风 WebGL"时使用。提供可运行的中式渲染 shader 与实现方法，不是通用 Three.js 教程。
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。
Solves complex, open-ended problems using a knockout tournament mechanism. Generates an initial population of diverse candidate answers, pairs them into head-to-head matches, produces a hybrid "child" solution from each pair, and advances the best of the three through a bracket until a winner emerges. Use this skill whenever the user wants to competitively select the best answer from a field of alternatives, asks for a "tournament" or "bracket" style search, wants a faster alternative to full evolutionary search, or uses phrases like: "run a tournament on this", "bracket-style search", "let solutions compete", "knockout search", "find the best answer by elimination", or "which solution wins?". Also trigger when the problem is open-ended and the user wants a clear winner quickly rather than a deep multi-generation refinement.
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。
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.
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。
This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.
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。
Collects and synthesizes open-source intelligence (OSINT) about threat actors, malicious infrastructure, and attack campaigns using publicly available data sources, passive reconnaissance tools, and dark web monitoring. Use when investigating external threat actor infrastructure, performing pre-engagement reconnaissance for authorized red team assessments, or enriching CTI reports with publicly available adversary context. Activates for requests involving Maltego, Shodan, OSINT framework, SpiderFoot, or infrastructure reconnaissance.
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 an automated system to track adversary infrastructure using passive DNS, certificate transparency, WHOIS data, and IP enrichment to map and monitor threat actor command-and-control networks.
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。
Extract and catalog attack patterns from cyber threat intelligence reports into a structured STIX-based library mapped to MITRE ATT&CK for detection engineering and threat-informed defense.
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 the Malpedia platform and API to research malware family relationships, track variant evolution, link families to threat actors, and integrate YARA rules for detection across malware lineages.
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。
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
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。