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概要と使いどころ
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), pr...
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Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring, and behavioral heuristics. Identifies mass file modification patterns, abnormal entropy spikes in written data, and suspicious process behavior characteristic of ransomware encryption routines. Activates for requests involving ransomware behavioral detection, entropy-based file monitoring, I/O anomaly detection, or real-time encryption activity alerting.
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Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
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。
Detect unauthorized modifications to running containers by monitoring for binary execution drift, file system changes, and configuration deviations from the original container image.
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。
Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.
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。
Builds SOC performance metrics and KPI tracking dashboards measuring Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), alert quality ratios, analyst productivity, and detection coverage using SIEM data. Use when SOC leadership needs operational visibility, continuous improvement tracking, or executive-level reporting on security operations effectiveness.
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。
Builds real-time incident response dashboards in Splunk, Elastic, or Grafana to provide SOC analysts and leadership with situational awareness during active incidents, tracking affected systems, containment status, IOC spread, and response timeline. Use when IR teams need unified visibility during incident coordination and post-incident reporting.
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 Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.
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。
Runtime iOS app security testing with Objection (Frida): inspect keychain and filesystem data, explore app internals at runtime, and validate/bypass client-side protections during authorized mobile assessments.
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。
Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.
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。
Jest best practices, patterns, and API guidance for JavaScript/TypeScript testing. Covers mock design, async testing, matchers, timer mocks, snapshots, module mocking, configuration, and CI optimization. Baseline: jest ^30.0.0. Triggers on: jest imports, describe, it, test, expect, jest.fn, jest.mock, jest.spyOn, mentions of "jest", "unit test", "test suite", or "mock".
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Complete SolanaOS agent skill — install, configure, and operate the autonomous Solana trading runtime with Honcho v3 epistemological memory, multi-venue perp trading (Hyperliquid + Aster), on-chain intelligence with USD pricing, Telegram bot, gateway API, Tailscale mesh, hardware integration, and cross-session recall. Use when asked to install SolanaOS, query Solana blockchain data, manage wallets, run OODA trading loops, configure strategies, control BitAxe mining fleets, pair Seeker devices, or operate any SolanaOS runtime surface.
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Full SolanaOS operator skill — install the runtime, query Solana blockchain data via SolanaTracker RPC, manage wallets, run OODA trading loops, configure multi-venue strategies (spot + Hyperliquid + Aster perps), control BitAxe mining fleets, pair Seeker devices, and interact with the SolanaOS Hub. Use when asked about SolanaOS installation, Solana wallet balances, token research, trading strategy, OODA loops, gateway setup, Telegram bot configuration, mining fleet management, or any SolanaOS runtime operation.
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 network device logs to identify connectivity issues, latency problems, and outages. Correlate timestamps across timezones, detect anomaly patterns, and generate incident reports with root cause hypotheses. Use when troubleshooting network incidents from log files.
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本番と同じ失敗を本番前に検出するためのテスト階層設計スキル。 DB方言差、実依存関係、永続化確認、timezone差、packaging整合性、 adversarial regressionをunit/integration/e2e/smoke/packagingに 適切に割り振り、PR CIで最低限の本番同等性を保証する。 Use when designing production-parity tests, closing test blind spots, building smoke suites for real dependencies, verifying persistence beyond UI success, or creating adversarial regression backlogs.
Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical 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。
4人の異なる専門家ペルソナ（20年ベテランエンジニア、TDDエキスパート、Clean Codeエキスパート、バグハンター）で ソースコードを批判的にレビューするスキル。設計判断、テスト容易性、可読性、バグ検出の観点から コードの問題を検出し、改善提案を行う。Python/JavaScriptに対しては追加のチェックポイントを適用。 バグハンターは状態遷移、例外パス、依存関係、非同期競合に特化し、本番環境での問題を事前に検出する。 Use when reviewing source code from multiple expert perspectives to find design flaws, testability issues, code quality problems, and runtime bugs. Triggers: "critical code review", "multi-persona code review", "expert code review", "code quality assessment", "find bugs".
Relentless one-question-at-a-time interview (tappable options with a recommended answer) that sharpens a requirement, business plan, project plan, proposal, or raw idea through conversation, then writes it up as a brief with a glossary and decision log. Use whenever the user wants their thinking stress-tested or pinned down before acting — trigger phrases include "grill me", "grill this", "詰めて", "詰めたい", "壁打ち", "要件を固めたい", "企画を練りたい", "計画を整理したい", "アイデアをぶつけたい", "突っ込んで", "clarify", "質問して", "何が決まってないか教えて", or when the user describes a plan/idea vaguely and asks what to do next. Do NOT use for reviewing a finished document (use a document-review skill) or when the user just wants an answer, not questions.
Use when designing, implementing, reviewing, debugging, testing, profiling, or porting concurrent, parallel, distributed, high-performance, network dataplane, kernel-bypass, or real-time software, including locks, semaphores, monitors, MPI, CSP rendezvous, DPDK, VPP, eBPF, XDP, AF_XDP, SR-IOV, RSS, RoCEv2/RDMA, RMS/EDF, priority inversion, and environment selection from silicon to edge.
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。
End-of-life detection for runtimes (Node, Python, Java, Go, .NET) and key packages — surfaces past-EOL items, items reaching EOL within 90 days, and EOL base images for containers. Use when planning a runtime upgrade, auditing for unsupported versions, gating a deploy against EOL deps, or producing a remediation roadmap.
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Signpost (not a knowledge pack) for maritime safety and class rules: SOLAS Consolidated Edition 2024 (IH110E) as the paywalled statutory core, ISM Code & Guidelines 2018 (ID117E), ISPS Code 2003 (K116E) and the 2021 guide (IB116E), and COLREGS 2026 (IC904E), with designation, edition, owner, status, and official URL only. Contains no source content. Free-path layers are US flag implementation (eCFR Title 46 and USCG circulars), the USCG Navigation Rules PDF, UK MCA notices under Open Government Licence v3.0, and class portals (DNV, ABS, Bureau Veritas). None of those layers is the IMO consolidated text. No in-repo pack covers these instruments.
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。
Obtenir et gérer un access token pour l'API Merci Facteur (envoi de courrier papier, recommandé, recommandé électronique). Utiliser dès qu'un appel à l'API Merci Facteur doit être authentifié, ou quand apparaît une erreur de signature, de timestamp, de restriction d'IP ou un token expiré. Couvre le calcul HMAC-SHA256 de la secret key, getToken, la durée de vie du token jusqu'à 365 jours (timeLimit, force) et le stockage des identifiants.
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。
Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental 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。