memory▌

You remember. Not everything—but enough to feel continuous.

Acquire, analyze, and extract artifacts from memory dumps for incident response and malware analysis. \n \n Supports live memory acquisition across Windows (WinPmem, DumpIt), Linux (LiME, /dev/mem), and macOS (osxpmem), plus virtual machine memory from VMware, VirtualBox, QEMU, and Hyper-V \n Volatility 3 framework with 30+ plugins covering process analysis, network connections, DLL inspection, code injection detection, registry analysis, and file system artifacts \n Includes malware analysis an

Six-layer memory architecture combining WAL protocol, vector search, git-based knowledge graphs, and cloud backup. \n \n Hot RAM (SESSION-STATE.md) with Write-Ahead Log protocol ensures context survives crashes; warm store (LanceDB vectors) enables semantic search across memories with auto-recall \n Cold store uses git-notes for structured decisions and learnings; curated MEMORY.md archive plus daily logs provide human-readable long-term context \n Optional SuperMemory cloud sync and Mem0 auto-e

You are a memory retrieval sub-agent for OpenViking memory.

Write well-structured notes that Basic Memory can parse into a searchable knowledge graph. Every note is a markdown file with three key sections: frontmatter, observations, and relations.

Reorganize memory files for clarity, efficiency, and relevance. Like filesystem defragmentation but for knowledge.

$22

Unified read-side memory skill with subcommands for searching, loading, syncing, history, and visualization.

Pluggable memory and file backends for Deep Agents with ephemeral, persistent, and hybrid routing options. \n \n Four backend types: StateBackend (thread-scoped, ephemeral), StoreBackend (cross-session persistent), FilesystemBackend (real disk access for local dev), and CompositeBackend (route different paths to different backends) \n FilesystemMiddleware provides six file operation tools: ls , read_file , write_file , edit_file , glob , grep \n CompositeBackend uses longest-prefix matching to r

Two-layer memory system for SEO/GEO projects with hot cache and cold storage. \n \n Maintains a lean hot cache (CLAUDE.md, ~100 lines) for active context and structured cold storage directories for keywords, competitors, audits, and historical data \n Automatically promotes frequently referenced items to hot cache and demotes stale data to archives based on reference frequency and age \n Implements efficient context lookup flow across hot cache, glossary, and cold storage layers with intelligent