1,142 件 ・ 関連度順
概要と使いどころ
Chief Security Officer mode: infrastructure-first audit of secrets, dependency supply chain, CI/CD, LLM/AI security, OWASP Top 10, STRIDE, active verification. Daily/comprehensive, trend tracking.
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。
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。
Use when creating a new project from scratch — orchestrates the full journey from idea to running code. Research → design → plan → scaffold → implement → docs. Explicit invocation only — never auto-activates. CLI = project infrastructure (.claude/), bootstrap = application code + full pipeline. NOT for autonomous multi-hour builds of specified products (see mk:autobuild); NOT for single-task feature work on existing projects (see mk:cook).
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。
DraftForge test infrastructure for writing backend unit tests, Playwright E2E tests, and managing test data population. This skill should be used when writing new tests, adding test fixtures, creating populate functions for new features, adding Playwright E2E specs, using login/auth fixtures, creating test users/orgs/leagues/tournaments, or understanding the test data architecture. Covers backend Django tests via Docker, Playwright config (projects, workers, sharding), test auth endpoints that expose backend login to frontend, and feature-isolated test data 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。
Guide setup for and verify a reviewed, read-only Grafana Observability profile and its activated capability packs. Use when a user asks to set up Grafana access, verify profile readiness, discover available infrastructure, APM, log, IoT or business KPI packs, or troubleshoot why a Grafana pack cannot run.
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。
Designs and orchestrates a realistic interview simulation platform with voice AI, whiteboard evaluation, gaze-tracking proctoring, and mobile spaced repetition. Use for building mock interview infrastructure, configuring sessions, and adaptive difficulty. Activate on "interview simulator", "mock interview", "practice session", "voice mock". NOT for individual round-type coaching, resume writing, or prep timeline coordination.
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。
Comprehensive reference for inter-process communication mechanisms -- every way processes can talk on a computer. Covers sockets (TCP, UDP, Unix domain), WebSockets, SSE, pipes (named/anonymous), shared memory (mmap, shm_open), message queues, signals, D-Bus, XPC (macOS), gRPC, REST, stdin/stdout, file-based coordination, and clipboard. Performance benchmarks, platform availability, and code examples for each. Special focus on which IPC works best for AI agent coordination. Activate on: "IPC", "inter-process communication", "process communication", "how do I talk between processes", "Unix socket vs TCP", "shared memory", "named pipe", "WebSocket vs SSE", "gRPC vs REST", "agent coordination IPC", "XPC service", "message passing", "stdout pipe", "D-Bus", "mmap". NOT for: distributed systems design (use distributed-systems), network protocol design (use networking), message queue infrastructure like Kafka (use data-pipeline-engineer).
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。
Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).
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 error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.
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。
Warehouse cost reduction, auto-scaling, query optimization, and lifecycle policies for data infrastructure. Activate on: data cost, warehouse credits, cost reduction, auto-scaling, lifecycle policy, cold storage, cost monitoring, resource optimization. NOT for: query performance tuning (use data-warehouse-optimizer), batch job optimization (use batch-processing-optimizer).
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。
Expert DevOps engineer for CI/CD, IaC, Kubernetes, and deployment automation. Activate on: CI/CD, GitHub Actions, Terraform, Docker, Kubernetes, Helm, ArgoCD, GitOps, deployment pipeline, infrastructure as code, container orchestration. NOT for: application code (use language skills), database schema (use data-pipeline-engineer), API design (use api-architect).
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。
Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.
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。
Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-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。
Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).
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。
Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).
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。
Collection of AWS development skills covering CDK infrastructure as code, cost optimization, and serverless/event-driven architectures with integrated MCP servers.
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。
Expert guide for Backend.AI distributed computing platform. Automatically activates when users ask about: - Backend.AI architecture, components (Manager, Agent, Storage Proxy, Webserver, App Proxy) - Features (session scheduling, Sokovan orchestrator, multi-tenancy, resource allocation) - APIs (REST, GraphQL), authentication, RBAC authorization - Container runtime (kernels, jail sandbox, hook library, virtual folders) - Accelerator support (CUDA, ROCm, TPU, NPU, Graphcore IPU) - Client SDKs (Python, Java, JavaScript, PHP) - Setup, requirements (Python 3.13+, Docker, PostgreSQL, Redis, etcd) - How WebUI connects to/interacts with Backend.AI backend - Plugin interfaces, development setup, infrastructure Use when user mentions "Backend.AI", "backend.ai", "Sokovan", component names, or asks about the backend platform this WebUI connects to. allowed-tools: WebFetch, Read
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 Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up Azure DevOps pipelines, or migrate to Azure. Covers AKS, App Service, Azure Functions, Cosmos DB, and cost optimization.
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。
AWS development with infrastructure automation and cloud architecture 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。
Deploy containerized frontend + backend applications to Azure Container Apps with remote builds, managed identity, and idempotent 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。
Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.
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。
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
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 or modify frontend interfaces using repository-native components while preserving accessibility, responsive behavior, state handling, and visual verification. Use for UI features, design implementation, forms, client-side defects, and frontend refactors; do not use for backend-only or infrastructure-only work.
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。