204건 · 관련도순
Complete career acceleration system — from self-assessment to offer negotiation. Covers career strategy, job search operations, resume/CV optimization, interview preparation, salary negotiation, career transitions, and long-term growth planning. Works for any role, any level, any industry.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Turn clients into long-term revenue engines. This isn't advice — it's a complete operating system with scoring models, templates, playbooks, and automation patterns that work for any B2B or B2C sub...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, and memory hygiene. No APIs, no databases, no external tools. Works with any agent framework.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture, gate, and query long-term project knowledge via the gated wiki subsystem: propose candidates, recall, or search; agents propose, humans approve. NOT for short-lived memory (mk:memory).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Call this skill when faced with "wicked problems", complex challenges, or when a user asks for strategic innovation, root-cause analysis, or long-term problem solving. Do not use for simple, linear tasks. This skill forces the agent to bypass superficial symptoms, map the entire ecosystem, and anticipate second-order consequences before proposing an intervention.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill recommends educating patients and families about the clinical features of remission in Cushing's syndrome when a patient achieves biochemical remission after treatment. Use when a CS patient has postoperative normal cortisol levels, reports symptom improvement, and requires guidance on long-term expectations and monitoring for recurrence.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Decides how far ahead to plan, what to discount future payoffs by, and which backlog items can be dropped without analysis - using receding-horizon planning, an explicit discount factor, and branch-and-bound pruning against the incumbent. Use when building a roadmap, prioritising a backlog, arguing about short-term versus long-term, setting quarterly or annual plans, or when planning has become an end in itself.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing or auditing long-term memory for persistent AI agents. Stores immutable source evidence before canonical facts, links beliefs to provenance, applies novelty-aware ADD/UPDATE/NOOP write gating, keeps uncertain merges explicit, separates retrieval from answer generation, supports deletion, and evaluates memory failures layer by layer.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory or memory benchmarks (LoCoMo, LongMemEval).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when scheduling and executing review sessions at scientifically-calibrated expanding intervals for long-term retention.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Executes containment strategies to stop active adversary operations and prevent lateral movement during a confirmed security breach. Implements short-term and long-term containment using network segmentation, endpoint isolation, credential revocation, and access control modifications. Activates for requests involving breach containment, lateral movement prevention, network isolation, active threat containment, or live incident response.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create a new Hindsight-powered subagent with long-term memory. Use when the user wants a specialized agent that learns and remembers across sessions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector s...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert en systèmes de mémoire pour agents IA (court terme, long terme, épisodique, sémantique, vector retrieval, RAG)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query or store long-term memory with memB. Generated from plugin-commands.json; invoke as $bdb-aos:memb.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
BDB local-first long-term memory engine (memB). Use when querying, remembering, or adapting preferences, code architectures, and developer patterns across tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
MANDATORY deploy after every code change. Typecheck → deploy → purge CDN → E2E on production → visual verify → fix-forward loop. Workers Builds native CI/CD, D1 Time Travel PIT recovery, D1→R2 long-term backups, wrangler rollback, wrangler secrets management, structured observability, cross-browser smoke tests, rollback procedures, and GitHub auto-configuration.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.