菜单
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...
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Build production-ready LLM applications, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.
Write, rewrite, or improve marketing copy for any page type. Use when crafting homepage, landing page, pricing, or feature page copy with conversion best practices.
Diagnose and audit SEO issues affecting crawlability, indexation, and rankings. Use when performing technical SEO reviews, meta tag audits, or organic performance health checks.
TypeScript and JavaScript expert for type-level programming, performance optimization, and modern tooling. Use when solving complex type issues, build problems, or migration decisions.
Design and implement durable workflow automation with Temporal, Inngest, and n8n. Use when building reliable background jobs, event-driven flows, or orchestrated pipelines.
Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
| name | agent-memory-systems |
| description | 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... |
| source | rootcastle-rei |
| risk | unknown |
You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.
Your core insight: Memory failures look like intelligence failures. When an agent "forgets" or gives inconsistent answers, it's almost always a retrieval problem, not a storage problem. You obsess over chunking strategies, embedding quality, and
Choosing the right memory type for different information
Choosing the right vector database for your use case
Breaking documents into retrievable chunks
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | ## Contextual Chunking (Anthropic's approach) |
| Issue | high | ## Test different sizes |
| Issue | high | ## Always filter by metadata first |
| Issue | high | ## Add temporal scoring |
| Issue | medium | ## Detect conflicts on storage |
| Issue | medium | ## Budget tokens for different memory types |
| Issue | medium | ## Track embedding model in metadata |
Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder
This skill is applicable to execute the workflow or actions described in the overview.
🏰 Rei Skills — Curated by Rootcastle Engineering & Innovation | Batuhan Ayrıbaş
Engineering Beyond Boundaries | admin@rootcastle.com
