168 件 ・ 関連度順
概要と使いどころ
This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences across sessions using MCP memory server integration
日本語の概要は準備中です。原文の説明を表示しています。
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
日本語の概要は準備中です。原文の説明を表示しています。
INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.
日本語の概要は準備中です。原文の説明を表示しています。
Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries
日本語の概要は準備中です。原文の説明を表示しています。
Debug iOS apps and profile performance using LLDB, the interactive Memory Graph Debugger, and Instruments. Use for crashes, retain-cycle inspection, hangs, build failures, and generic CPU, memory, energy, or network profiling. Use ios-memgraph-analysis for .memgraph capture, leaks CLI ownership paths, or persistent heap growth; use ios-ettrace-performance for ETTrace capture and JSON.
日本語の概要は準備中です。原文の説明を表示しています。
Give an AI agent persistent, cross-session long-term memory through MindMemOS. Covers installing and authenticating the mindmemos CLI, the full command interface (add / search / get / update / delete / feedback / dreaming) with parameters and examples, guidance on which capability to use when, plus a Python SDK example. To wire memory into a specific agent host (OpenClaw, DeepSeek Harness, Codex, Claude, etc.), see references/.
日本語の概要は準備中です。原文の説明を表示しています。
Sync session learnings to persistent memory incrementally, with a report. "save what we learned this session"
日本語の概要は準備中です。原文の説明を表示しています。
Claude Mind - Search and manage Claude's persistent memory stored in a single portable .mv2 file
日本語の概要は準備中です。原文の説明を表示しています。
Manages persistent memory. Invoke to read active context or archive old tasks. Structure: Long-term Principles (Rules) + Short-term Workbench (Tasks).
日本語の概要は準備中です。原文の説明を表示しています。
Persistent project documentation system that maintains context across sessions. Creates structured Memory Bank files to preserve project knowledge, decisions, and progress.
日本語の概要は準備中です。原文の説明を表示しています。
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
日本語の概要は準備中です。原文の説明を表示しています。
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
日本語の概要は準備中です。原文の説明を表示しています。
Guidelines and workflows for the agent to maintain persistent memory using native file tools (read_file, write_file, edit_file) and standard OS commands for searching.
日本語の概要は準備中です。原文の説明を表示しています。
Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.
日本語の概要は準備中です。原文の説明を表示しています。
Persistent memory across sessions. Save project state and user preferences. Never save secrets.
日本語の概要は準備中です。原文の説明を表示しています。
> Persistent belief revision log wiring in-memory AGM BeliefSet to DuckDB time-travel storage. Triggers: belief revision, AGM postulates, time-travel query, belief history, AS-OF queries on propositions, persistent belief state, append-only belief log. >>>>>>> origin/main
日本語の概要は準備中です。原文の説明を表示しています。
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
日本語の概要は準備中です。原文の説明を表示しています。
Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.
日本語の概要は準備中です。原文の説明を表示しています。
Process unhandled Slack messages as Claudicle with persistent memory. Loads user models and soul state, posts terminal-styled thinking messages, generates cognitive-step responses, updates memory based on mentalQuery decisions. Requires Session Bridge listener running.
日本語の概要は準備中です。原文の説明を表示しています。
Cross-session shared memory for agents. Use at session start to load the persistent bias-log via `mcp__memex__recent_observations`, mid-session to search prior context with `mcp__memex__search`, and immediately when a bias surfaces to log a new `observation` via `mcp__memex__add`. The six-layer reconstruction trail (raw → embedded → indexed → graphed → curated → reflexive) means memex stores facts AND the agent's own reasoning patterns across sessions. Prefer querying memex before re-deriving prior architectural decisions; re-derivation is itself evidence that the trail is incomplete.
日本語の概要は準備中です。原文の説明を表示しています。
Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.
日本語の概要は準備中です。原文の説明を表示しています。
Persistent per-project memory for Claude Code. Auto-loads project context on session start, tracks sessions with git activity, and writes to native memory. Commands run deterministic Node.js scripts — behavior is consistent across model versions.
日本語の概要は準備中です。原文の説明を表示しています。
Architect persistent, searchable memory for agents — episodic, semantic, and procedural stores with retrieval, consolidation, and forgetting policies. Use when designing how an agent remembers across sessions.
日本語の概要は準備中です。原文の説明を表示しています。
Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.
日本語の概要は準備中です。原文の説明を表示しています。