6,446 件 ・ 関連度順
概要と使いどころ
Detect AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations
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。
URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat
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。
Craft, send, sniff, and dissect network packets using Scapy for protocol analysis, network reconnaissance, and traffic anomaly detection in authorized security testing
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。
Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.
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。
Parse and analyze Linux auditd logs to detect intrusion indicators including unauthorized file access, privilege escalation, syscall anomalies, and suspicious process execution using ausearch and Python.
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。
Scan agent skills for security issues. Use when asked to "scan a skill", "audit a skill", "review skill security", "check skill for injection", "validate SKILL.md", or assess whether an agent skill is safe to install. Checks for prompt injection, malicious scripts, excessive permissions, secret exposure, and supply chain risks.
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。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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。
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
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 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
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。
Map the competitive landscape for a Stellar project idea. Use when a user says "who are my competitors on Stellar", "competitive analysis Stellar", "what already exists in this space on Stellar", "show me similar Stellar projects", "is anyone else building this on Stellar", or "map the Stellar landscape for X". Queries the 728-project LumenLoop ecosystem database and Electric Capital developer activity to rank competitors by SCF funding history and repo activity.
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 AI-to-human escalation systems. TRIGGERS - Use when user needs help with ai-escalation-system related tasks.
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 and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent readiness", "agent-ready score", "MCP server card", "agent skills index", "markdown for agents", "content signals", "web bot auth", "agent discovery", "RFC 9727", "RFC 8288", "RFC 9728", "SEP-1649", "WebMCP", "x402", "UCP", "ACP", or wants to make a website discoverable and usable by AI agents.
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。
Bootstrap a complete .github/workflows/ set for a new project: CI (build/test/lint), CD (workflow_dispatch with env choice + OIDC + ECR), security-scan (gitleaks + dependency scan + CodeQL), and optional database-migration. Use this skill when the user wants to add GitHub Actions CI/CD to a project that does not already have it, or when they want to add missing workflows alongside existing ones.
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 enables Claude to perform comprehensive accessibility audits. It uses the accessibility-test-scanner plugin to identify WCAG 2.1/2.2 compliance issues, validate ARIA attributes, check keyboard navigation, and assess screen reader compatibility. Use this skill when the user requests an accessibility scan, audit, or compliance check, or when terms like "WCAG", "ARIA", "screen reader", "accessibility testing", or "a11y" are mentioned. It provides actionable insights for improving web application accessibility.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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。
Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling 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。
Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
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。
Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.
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。
新しいフックの雛形（スクリプト + 配線）を本リポジトリの規約どおりに生成する。 イベント選定（PreToolUse / PostToolUse / Stop / SessionStart 等）とブロッキング可否を対話的に決め、 ai-agents/hooks/ か ai-agents/settings/<cli>/hooks/ への配置と、hooks.json / settings.json への配線まで行う。 新しいフックを追加したいときに `/hook-scaffold <フック名> [用途の一言]` で呼び出す。
新しいスキルの雛形（ディレクトリ + SKILL.md）を本リポジトリの規約どおりに生成する。 frontmatter（name / description / 任意の argument-hint・disable-model-invocation・metadata.version）と 本文（# /SKILL_NAME スキル → Goal / Workflow / Notes）を対話的に埋め、 `.claude/rules/skill-authoring.md` の判断軸で置き場所（ai-agents/skills / ai-agents/personal/skills / .claude/skills）を選んで配置する。 新しいスキルを新規作成・立ち上げたいときに `/skill-scaffold <スキル名> [用途の一言]` で呼び出す。
Cascade merge changes through a chain of dependent branches. Use when updating stacked/chained PRs, propagating changes from a parent branch to all downstream branches, or when the user mentions "cascade merge", "propagate changes", "update dependent branches", or "merge to downstream branches".
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。
ステージング環境にデプロイ済みのサーバーに対してランタイム検証を実行する。HTTPヘッダー・動的プローブ・認証テストに特化。手動呼び出し専用: /sec-runtime-scan [環境名]
リリース前の全ファイル静的解析スキル。プロジェクト構造を自動検出し、ソースモジュールごとのサブエージェントと依存関係スキャンを並列実行してコードベース全体の脆弱性を検出する。手動呼び出し専用: /sec-full-scan
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". (gstack) Voice triggers (speech-to-text aliases): "see-so", "see so", "security review", "security check", "vulnerability scan", "run security".
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。