pieces-mcp-playbook
Connect to the Pieces MCP server (SSE) and reliably query or write to Pieces Long‑Term Memory (LTM) using query/write tool patterns (e.g., ask_pieces_ltm + create_pieces_memory), with practical troubleshooting and request-shaping examples.
helix-memory
Long-term memory system for Claude Code using HelixDB graph-vector database. Store and retrieve facts, preferences, context, and relationships across sessions using semantic search, reasoning chains, and time-window filtering.
agent-memory
Implement agent memory - short-term, long-term, semantic storage, and retrieval
curating-memories
Guidance for maintaining memory quality through curation. Covers updating outdated memories, marking obsolete content, and linking related knowledge. Use when memories need modification, when new information supersedes old, or when building knowledge graph connections.
memory-hygiene
Maintains memory cleanliness with deduplication, validation, and expiration
letta
Letta framework for building stateful AI agents with long-term memory. Use for AI agent development, memory management, tool integration, and multi-agent systems.
context-manager
Manages permanent memory storage for decisions, blockers, context, preferences, and procedures. Use when user says "remember", "save this decision", "what did we decide", "recall", "search memories", "any blockers", or when making important architectural decisions. Provides SDAM compensation through external memory.
memory
Long-term memory across sessions. Always use memory_search at the start of any user request (unless the user explicitly says not to), especially for questions about the user (profile/personal info/preferences), prior constraints or decisions, and resuming ongoing work; use memory_write only when the user explicitly asks to store memory.
iterating
Multi-conversation methodology for iterative stateful work with context accumulation. Use when users request work that spans multiple sessions (research, debugging, refactoring, feature development), need to build on past progress, explicitly mention iterative work, work logs, project knowledge, or cross-conversation learning.
session-memory
Manages cross-session learning and memory persistence. Use when user mentions 前回何をした, 履歴, 過去の作業, セッション記録, continue from before, session history. Do NOT load for: 実装作業, レビュー, 一時的な情報.
ensue-memory
Augmented cognition layer that makes users smarter by connecting conversations to their persistent knowledge tree. Use proactively when topics arise that might have prior knowledge, and when users ask to remember, recall, search, or organize. Triggers on technical discussions, decision-making, project work, "remember this", "recall", "what do I know about", or any knowledge request.
agent-memory
Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
