188건 · 관련도순
Plan and configure command and control (C2) systems for red team exercises. Covers major frameworks, communication methods, relay servers, certificate management, and the network or endpoint signals defenders use to detect these systems.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Compare batching, request scheduling, and memory management for a single model serving instance. Assess concurrent request capacity, response times, generation speed, and GPU time per result that meets the required quality and deadline.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Automate checks, code formatting, agent coordination, and session records around Claude Code operations. Uses hooks, actions triggered before or after work, to preserve context and connect development tasks with shared memory.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Security best practices and vulnerability prevention for Golang — injection (SQL, command, XSS), cryptography, path traversal, SSRF and HTTP security headers, cookies, secrets management, memory safety, PII in logs, STRIDE/DREAD threat modeling, plus `gosec` SAST, race detection, and fuzz testing. Apply when writing, reviewing, or auditing Go code for security, or when touching crypto, file or network I/O, secrets, user input, or authentication. Not for non-exploitable defensive bugs such as nil panics or slice aliasing (→ See `samber/cc-skills-golang@golang-safety` skill), dependency vulnerability scanning with govulncheck (→ See `samber/cc-skills-golang@golang-dependency-management` skill), or wiring security scanners into CI pipelines (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Conversation summarization for memory compression and context management
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Write efficient C code with proper memory management, pointer arithmetic, and system calls. Handles embedded systems, kernel modules, and performance-critical code. Use PROACTIVELY for C optimization, memory issues, or system programming.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Profile and optimize iOS apps with Instruments, memory management, and rendering techniques. Use when profiling iOS apps with Instruments or optimizing memory and rendering.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。 不适用于：定义工具接口（tool-specification）、定义安全规则（safety-guardrails）、定义人格风格（personality-system）。 关键 trigger 信号：AI 需要跨会话记住用户信息、记忆内容可能敏感、需要在回复中隐式应用记忆而非显式引用、用户要求"记住这个"。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rotUse when "context window, token limit, context management, context engineering, long context, context overflow, llm, context, tokens, memory, summarization, optimization" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Keeping codebases healthy, performant, and maintainable - refactoring, performance optimization, and technical debt managementUse when "refactor, optimize, performance, technical debt, cleanup, architecture, speed up, bundle size, memory leak, slow query, code smell, complexity, dead code, performance, refactoring, optimization, technical-debt, architecture, cleanup, bundle, memory" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Neo4j command-line tools — neo4j-cli (modern unified CLI — Cypher via Bolt, schema inspection, Aura management, Docker containers, credential management, agent skill catalog install; install with curl -sSfL https://neo4j.sh/install.sh | bash), neo4j-admin (backup, restore, import, memory sizing), cypher-shell (ad-hoc queries, scripting, CI/CD), or neo4j-mcp (quick MCP server install). Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades — use neo4j-migration-skill. Does NOT cover full MCP editor configuration — use neo4j-mcp-skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Power management and performance optimization for Zephyr RTOS. Covers system power states (Idle, Suspend, Off), device-level power management, residency hooks, and code/data relocation for speed efficiency. Trigger when optimizing battery life, reducing latency, or managing memory constraints.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when using kmalloc, vmalloc, or the page allocator, sizing kmalloc allocations, working with SLUB or the buddy allocator, or debugging memory zones and kernel OOM.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Memory persistence operations using claude-mem
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.