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Found 57 Skills
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.
Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.
Navigate unfamiliar code using GitNexus knowledge graph
Show the current health status of a GRACE project. Use to get an overview of project artifacts, codebase metrics, knowledge graph health, and suggested next actions — helps identify drift, missing contracts, or unpaired semantic blocks.
Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
Design and implement memory architectures for agent systems. Use when building agents that need to persist state across sessions, maintain entity consistency, or reason over structured knowledge.
Stores decisions and patterns in knowledge graph. Use when saving patterns, remembering outcomes, or recording decisions.
Trace bugs through call chains using knowledge graph
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.
Use when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
Interact with the SlipBox semantic knowledge engine and read notes from PrivateBox. Use when capturing ideas, searching notes, browsing your knowledge graph, or running semantic analysis passes (link, cluster, tension).