Run a multi-agent debate around contested memories. Multiple agents argue for/against a memory claim, then vote to determine its final social status. This is the "game theory enhances collective memory" engine — consensus emerges through structured argumentation, not just majority counting.
Before generating an AI reply, recall relevant memories from DeepMemory and search related code from FlashMemory, then inject both as augmented context. This is the memory-enhanced RAG pipeline — combining symbolic recall with vector search for richer, history-aware responses.
Run a periodic memory evolution cycle: decay stale observations, deprecate low-confidence memories, and prune excess per object. Call every 10 turns or on a daily schedule to keep the memory store healthy and relevant.
Extract symbolic order from conversations: identify key concepts as Signs, anchor them to ObjectRefs, and record interpretant claims via DeepMemory MCP. This is the "semiotics deconstructs semantics" engine — it turns natural language into structured, contestable memory units instead of opaque vectors.
Process user feedback on AI memories: agreement promotes status, disagreement deletes, refinement revises claims. Translates natural user reactions into DeepMemory governance operations.
FlashMemory CLI for cross-language code analysis and semantic search. Use this skill when you need to understand project architecture, locate implementations, or index large codebases using natural language queries. Make sure to use this skill whenever the user asks about the codebase structure, finding code, or searching files.
Build and maintain semantic vector indexes for large codebases using FlashMemory. Use this skill whenever you need to index a new project, re-index after making code changes, perform incremental file updates, or switch between vector engine backends. Make sure to use this whenever you modify code or pull new changes so the semantic search index stays up-to-date.
Search the codebase using FlashMemory semantic queries to find implementations, understand architecture, and navigate unfamiliar code without knowing exact filenames. Use this skill whenever the user asks to find specific code, locate a function's implementation, understand how something works, or when you need to research a codebase that is too large to read entirely.