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Found 88 Skills
Use when saving or retrieving important patterns, decisions, and learnings across sessions. Triggers on keywords like "remember", "save pattern", "recall", "memory", "persist", "knowledge base", "learnings".
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
Use when implementing agent memory, persisting state across sessions, building knowledge graphs, tracking entities, or asking about "agent memory", "knowledge graph", "entity memory", "vector stores", "temporal knowledge", "cross-session persistence"
Read-side memory operations: search, load, sync, history, visualize. Use when searching past decisions, loading session context, or viewing the knowledge graph.
Complete GRACE methodology reference. Use when explaining GRACE to users, onboarding new projects, or when you need to understand the GRACE framework — its principles, semantic markup, knowledge graphs, contracts, and unique tag conventions.
Use when you need a deep-dive explanation of a specific file, function, or module in the codebase
Maintains persistent codebase knowledge across sessions through a structured knowledge graph stored in a local Obsidian vault (.doctrack/). Use this skill whenever you have just made meaningful code changes (new features, modified components, refactoring, bug fixes) to update the project's documentation. Also use it when the user asks to document code, update docs, sync documentation, initialize documentation for an existing project, or when you want to understand the existing codebase structure at the start of a session. This skill should be used proactively after any significant code modification — don't wait for the user to ask. If you changed code, update the docs. Think of it as your long-term memory system: read before working, write after changing. Also use this when a user says "doctrack init", "doctrack refresh", "refresh docs", "update docs", "sync docs", "initialize docs", "document this project", or wants to bootstrap documentation for a codebase that has no .doctrack/ vault yet.
Persistent memory architecture for AI agents across sessions. Episodic memory (past events), procedural memory (learned skills), semantic memory (knowledge graph), short-term memory (active context). Use when implementing cross-session persistence, skill learning, context preservation, personalization, or building truly adaptive AI systems with long-term memory.
Deep code analysis for pplx-sdk — parse Python AST, build dependency graphs, extract knowledge graphs, detect patterns, and generate actionable insights about code structure, complexity, and relationships. Use when analyzing code quality, mapping dependencies, or building understanding of the codebase.
Neo4j graph database with Cypher query language. Use for graph-based data.
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.
Hybrid memory strategy combining OpenClaw's built-in QMD vector memory with Graphiti temporal knowledge graph. Use for all memory recall requests.