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Found 85 Skills
Persistent local memory for AI agents. Use when starting a new session, when the user mentions remembering something, when you need project context, when making architecture decisions, or when working with other agents on the same project.
Advanced memory operations reference. Basic patterns (profile loading, simple recall/remember) are in project instructions. Consult this skill for background writes, memory versioning, complex queries, edge cases, session scoping, retention management, type-safe results, proactive memory hints, GitHub access detection, and ops priority ordering.
Comprehensive skill for Graphiti and Zep - temporal knowledge graph framework for AI agents with dynamic context engineering
File-based knowledge persistence patterns: when to store discoveries, when to recall past solutions, and how to organize project memory. Activate when starting tasks, encountering errors, making decisions, or when context may be lost between sessions.
ALWAYS ACTIVE — Persistent memory protocol. You MUST save decisions, conventions, bugs, and discoveries to engram proactively. Do NOT wait for the user to ask.
Fan-out search across all memory sources when context is unclear or vaguely referenced. Triggers on: 'from earlier', 'remember when', 'what we discussed', 'that thing with', 'the conversation about', 'did we ever', 'what happened with', 'you mentioned', 'we talked about', 'earlier today', 'last session', 'the other day', or any vague reference to past context that needs resolution before the agent can act.
Tiered memory system for cognitive continuity across agent sessions. Manages hot cache (session context loaded at boot) and deep storage (loaded on demand). Use when: (1) starting a session and loading context, (2) deciding what to remember vs forget, (3) promoting/demoting knowledge between tiers, (4) user says 'remember this' or asks about project history.
Use this skill when managing persistent user memory in ~/.memory/ - a structured, hierarchical second brain for AI agents. Triggers on conversation start (auto-load relevant memories by matching context against tags), "remember this", "what do you know about X", "update my memory", completing complex tasks (auto-propose saving learnings), onboarding a new user, searching past learnings, or maintaining the memory graph - splitting large files, pruning stale entries, and updating cross-references.
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory, or memory benchmarks (LoCoMo, LongMemEval). A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of durable agent knowledge and cross-session persistence.
Create narrative lore entries that transform technical work into mythological stories. Use when generating agent memory, documenting changes as narrative, or building persistent knowledge through storytelling.
Manage git-backed memory repos. Load this skill when working with git-backed agent memory, setting up remote memory repos, resolving sync conflicts, or managing memory via git workflows.
Stores decisions and patterns in knowledge graph. Use when saving patterns, remembering outcomes, or recording decisions.