memory▌

Daily briefing of active focus areas, priorities, and recent knowledge changes for cross-session continuity. \n \n Load at the beginning of each session to understand current context and recent work across tools \n Surfaces active focus areas ranked by recent activity, flagged priorities, and unresolved contradictions or stale information \n Works via nmem wm read CLI command for both local and remote knowledge bases, with fallback to local file access \n Includes deep links to specific memories

Consolidates mature learnings from domain memory files into instruction files with quality-assured merging. \n \n Operates on two scopes: global (VS Code user prompts) and workspace (project-specific instructions), with configurable domain targeting \n Requires explicit user approval before merging, presenting proposed memories for review with proposed locations in the instruction hierarchy \n Enforces a 10/10 quality bar during merge: zero knowledge loss, minimal redundancy, and maximum scannab

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Uses Rekall memory forensics framework to analyze memory dumps for process hollowing, injected code via VAD anomalies, hidden processes, and rootkit detection. Applies plugins like pslist, psscan, vadinfo, malfind, and dlllist to extract forensic artifacts from Windows memory images. Use during incident response memory analysis.

Saves decisions, git state, and pending work so future sessions can resume without reconstructing context from scratch.

Restores saved context, including across branch changes and workspace handoffs.

Quality dashboard skill that wraps project checks and reports a weighted health score and trend line.

Keep vector memory lean. Prevent token waste from junk memories.

Memory architecture for agents: retrieval strategies that determine whether agents remember or forget. \n \n Covers five memory types: short-term (context window), long-term (vector stores), working memory, episodic memory, and semantic memory, each suited to different information patterns \n Emphasizes retrieval as the core challenge; provides chunking strategies, embedding quality guidance, and metadata filtering to surface the right memories at decision time \n Includes anti-patterns like sto

Persistent, searchable memory bank for AI agents with automatic project documentation sync. \n \n Provides four core MCP tools: memory_search for querying by text/type/tags, memory_write for recording knowledge and decisions, memory_read for retrieving specific entries, and memory_stats for usage analytics \n Organizes memories by type (architecture, patterns, decisions) and supports custom tagging for flexible retrieval and organization \n Runs as an MCP server that syncs with your project work