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概要と使いどころ
Configures Zscaler Private Access (ZPA) to replace traditional VPN with zero trust network access by deploying App Connectors, defining application segments, configuring identity- and device-posture-based access policies, and integrating with IdPs. Use when replacing VPN concentrators with ZTNA or providing remote users secure access to internal applications without network-level connectivity.
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 DefectDojo as a centralized vulnerability management dashboard that ingests findings from 200+ security scanners, deduplicates results, tracks remediation metrics, and integrates with CI/CD, Jira ticketing, and Slack notifications via its REST API. Use when consolidating scanner output into one dashboard or automating vulnerability ticketing and executive 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。
Audit MCP servers for tool poisoning, tool shadowing, rug pulls, SSRF, and unauthenticated exposure using Invariant Labs' mcp-scan for static/runtime scanning plus manual SSRF/auth checks and description pinning. Use before adding a new MCP server to an agent stack, when reviewing an internal MCP server, detecting rug pulls, or investigating an agent's unexpected tool-driven behavior.
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。
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。
Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Includes tech debt analyzer, team scaling calculator, engineering metrics frameworks, technology evaluation tools, and ADR templates. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy.
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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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。
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
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。
Generates unit tests, integration tests, and E2E tests for React/Next.js applications. Scans components to create Jest + React Testing Library test stubs, analyzes Istanbul/LCOV coverage reports to surface gaps, scaffolds Playwright test files from Next.js routes, mocks API calls with MSW, creates test fixtures, and configures test runners. Use when the user asks to "generate tests", "write unit tests", "analyze test coverage", "scaffold E2E tests", "set up Playwright", "configure Jest", "implement testing patterns", or "improve test quality".
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。
/cs:cto-review <plan> — Architecture and scaling interrogation. Tech debt, scaling cliffs, team scaling, build-vs-buy. Use when committing to an architecture, planning for 10x load, or weighing a rebuild against a vendor.
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。
Cascades strategy from boardroom to individual contributor. Detects and fixes misalignment between company goals and team execution. Covers strategy articulation, cascade mapping, orphan goal detection, silo identification, communication gap analysis, and realignment protocols. Use when teams are pulling in different directions, OKRs don't connect, departments optimize locally at company expense, or when user mentions alignment, strategy cascade, silo, conflicting OKRs, or strategy communication.
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。
Review a PR against the Pascal architectural rules — package boundaries (core/viewer/editor/nodes), the registry-driven composition model (def.geometry / def.renderer / def.system), legacy-dispatch regressions, the slots + world-scale-UV convention for new nodes/geometry, hook hygiene (useEditor/useScene/useViewer), and selector performance. Use when the user asks to review a PR, audit a branch, or check that changes respect the codebase's architecture.
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。
Assess whether furniture fits in a measured Pascal room or layout. Use this skill for sofa, table, bed, cabinet, appliance, staging, placement, collision, clearance, or rotated-footprint questions. Produce a tool-backed spatial report that distinguishes footprint fit from unsupported height, door-swing, assembly, and delivery-route claims, and return insufficient evidence when dimensions or scale are missing.
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。
The 15-minute escalation-routing runbook for the Plain support queue — detecting SLA breaches against {{sla_targets}}, VIP accounts in {{vip_accounts}}, and high severity, routing each qualifying ticket to the right team, opening a linked issue in {{linear_team}} when engineering is needed, and posting the alert to {{escalation_channel}}. Never closes a ticket or promises a resolution.
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 working with TradingView's lightweight-charts — creating charts, adding series (Line/Area/Bar/Candlestick/Histogram), configuring time and price scales, streaming realtime or historical data, panes, markers, per-bar/per-point colors, tooltips, coordinate conversion, custom plugins or primitives, non-financial horizontal scales, SSR/browser loading, or React/Vue/Web Components wrappers. Covers v5 API conventions and the common time, scale, marker, plugin, and wrapper foot-guns.
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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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。
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
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。
Use for a standard, single-pass security audit of an entire repository or a scoped path, package, folder, or submodule with no diff to review. This is the default repository scan. Do not use for PR, commit, branch, or working-tree diffs, or for deep, multi-pass scans.
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。
Develop evidence-backed structural and architectural security hardening proposals from vulnerability disclosures, supplied findings, incident or assessment documents, source code, or a completed Codex Security scan. Use when a user asks for systemic improvements, alternatives beyond per-finding patches, before-and-after security architecture views, engineering tradeoff analysis, or an implementation-ready plan for a selected hardening option. Also use automatically after a Codex Security scan with reportable findings when the top-level scan workflow requests final-report hardening guidance.
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。
IRAC-scaffolded case analysis memo with research gaps flagged — the scaffold, not the analysis. Rule blocks are RESEARCH NEEDED, Application is STUDENT ANALYSIS prompts, Conclusion is blank. Use when a student needs to scaffold a case analysis memo, write up their analysis, or build an IRAC memo for a case.
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。