AI_friend

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.

Build conversational AI voice agents with ElevenLabs Platform. Configure agents, tools, RAG knowledge bases, agent versioning with A/B testing, and MCP security. React, React Native, or Swift SDKs. Prevents 34 documented errors. Use when: building voice agents, AI phone systems, agent versioning/branching, MCP security, or troubleshooting @11labs deprecated, webhook errors, CSP violations, localhost allowlist, tool parsing errors.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

A high-fidelity development protocol and architectural framework for building and evolving the CVS-1.0 Sovereign Mesh. Enforces contract-first development, parallel codegen, local-first memory integrity, and physically-grounded embodied intelligence.

Produces codebase-grounded implementation plans before coding. Use when scoping non-trivial features, refactors, reliability fixes, cross-module changes, or risky behavior updates.

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new service or component from scratch and choosing how to layer responsibilities, when refactoring a God class or monolithic function that has grown too large, when deciding whether to add a new abstraction or live with duplication, when evaluating a pull request for structural issues like tight coupling or leaking internal types, when choosing between inheritance and composition for a new class hierarchy, or when a codebase is becoming hard to test because of entangled I/O and business logic.

Neo4j graph data modeling patterns — node/relationship design, fraud detection schemas, and modeling best practices.