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Found 85 Skills
Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.
Migrate memory blocks from an existing agent to the current agent. Use when the user wants to copy or share memory from another agent, or during /init when setting up a new agent that should inherit memory from an existing one.
Enables continuous self-improvement through learning from failures, user corrections, and capability gaps. Integrates with QAVR for learned memory ranking.
Semantic search over global agent memory. Use to retrieve previously learned patterns, decisions, gotchas, and workarounds. Prevents stale-context errors across long sessions and multi-agent pipelines.
Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.
Obsidian vault management combining qmd (search) and notesmd-cli (CRUD). No Obsidian app needed. Use for: (1) searching notes with keyword, semantic, or hybrid search, (2) creating/editing/moving/deleting notes, (3) daily journaling, (4) frontmatter management, (5) backlink discovery, (6) AI agent memory workflows, (7) vault automation and scripting. Triggers: obsidian vault, obsidian notes, vault search, note management, daily notes, agent memory, knowledge base, markdown vault.
Defragment and reorganize agent memory files: split bloated files, merge duplicates, remove stale information, and restructure the memory hierarchy. Use when memory files have grown unwieldy, contain redundancies, or need reorganization. Run periodically (weekly) or on demand.
Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Use task_summary when you need full task context, skill_get for experience guides, skill_search to discover public skills, memory_write_public for shared knowledge, and memory_timeline to expand around a memory hit.
File-based memory system using Tiago Forte's PARA method. Use this skill whenever you need to store, retrieve, update, or organize knowledge across sessions. Covers three memory layers: (1) Knowledge graph in PARA folders with atomic YAML facts, (2) Daily notes as raw timeline, (3) Tacit knowledge about user patterns. Also handles planning files, memory decay, weekly synthesis, and recall via qmd. Trigger on any memory operation: saving facts, writing daily notes, creating entities, running weekly synthesis, recalling past context, or managing plans.
Trigger Scenarios: (1) Explicit memory requests – remember, record, don't forget, pay attention next time, form rules, generate summaries/record documents; (2) Correction and modification – note, incorrect, wrong, it should be, change to, replace with, don't, also need, missing; (3) Preference expression – I prefer, in the future, it's better, suggest, my habit, I usually; (4) Global specifications – unified, all, every, any, each, every time, all, uniformly; (5) Conversation end settlement – when the conversation ends naturally or the topic switches. Convert users' corrections, preferences and rules into structured memory files to improve the output quality of subsequent conversations.
Store and retrieve agent memories across jobs. Enables long-term context, learning from past interactions, and building agent knowledge bases. Based on OpenClaw's memory-core architecture.
Persistent memory layer for AI agents using Postgres/pgvector with MCP server support