agent-management
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent",…
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
$ npx -y skills add davila7/claude-code-templates --skill agent-memory-systems --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/agent-memory-systemsContext preview
The summary Claude sees to decide when to auto-load this skill.
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
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 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" source: vibeship-spawner-skills (Apache 2.0)
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`
Ready-to-use configurations for Anthropic's Claude Code. A comprehensive collection of AI agents, custom commands, settings, hooks, external integrations (MCPs), and project templates to enhance your development workflow.
Repo: davila7/claude-code-templates
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent",…
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an…
Search auto-generated codebase documentation for function signatures, API docs, class definitions, and code comments. Use when the user asks to "search docs",…
Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to "find callers", "check…
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we…