memb-skill

name	memb-skill
description	BDB local-first long-term memory engine (memB). Query, remember, and adapt preferences, code architectures, and developer patterns across tasks.
risk	low
source	bdb
date_added	2026-07-11

memB: Local Long-Term Memory Skill

This skill allows agents to access and maintain a persistent, offline-first long-term memory bank using the memb-mcp server. It manages user preferences, project structures, and complex developer workarounds.

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🔒 Memory Safety & Secret Filtration

[!IMPORTANT] Secret Ingestion Rule: Under no circumstances should raw credentials, passwords, API keys (e.g. GEMINI_API_KEY, POSTGRES_PRISMA_URL), or raw environment configurations be written to memory. Ensure all inputs are scrubbed of high-entropy strings before calling memory tools.

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🛠️ Memory Categorization (Dynamic Flower-Like Graph Layout)

memB implements a dynamic, project-agnostic hierarchical layout that structures memories into clusters automatically without hardcoded categories:

1. "God Mode" / General Knowledge Hub (Center):
   • Mapped using category="godmode" (with project_id=None).
   • Holds universal developer preferences, coding philosophies, global style sheets, and general core commands.
2. Dynamic Project Leaves (Petals):
   • Mapped using a specific project_id (e.g. project_id="VisualSelect_By_BDB" or project_id="litha-gathering").
   • Isolates facts, custom configurations, and files routing patterns to the specific workspace project, preventing context pollution.
   • System Integration: The agent dynamically resolves the basename of the active workspace directory to scan and bind project memories automatically.

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🔌 Using Memory Tools

When working on tasks, query memory at the start of your turn to retrieve relevant developer context, and add critical decisions at the end.

1. Ingestion (add_memory)

Use add_memory to commit a new fact, style preference, or design decision.

• Args: text (string), user_id (string, default: "bdb_developer"), category (string, default: "godmode"), project_id (string, optional)
• Usage Guidelines:
  • General Preferences: "Alice prefers to use absolute import paths globally." -> add_memory({ text: "Prefers absolute import paths globally", category: "godmode" })
  • Project Learned Facts: "In project VisualSelect, we must bypass the Firebase Auth login on localhost." -> add_memory({ text: "Bypass Firebase Auth login on localhost", category: "project_node", project_id: "VisualSelect_By_BDB" })

2. Retrieval (search_memory)

Use search_memory at the beginning of a task to load context.

• Args: query (string), user_id (string), limit (integer), project_id (string, optional)
• Behavior: The search queries both global godmode memory and the active project_id memory in parallel, merging and ranking results by similarity.

3. Cleanup & Auditing (list_memories / delete_memory)

• Use list_memories to audit active records.
• Use delete_memory with a UUID to remove outdated or erroneous facts.

🚨 CRITICAL DIRECTIVE: AI-FIRST VAULT NAVIGATION 🚨

[!CAUTION] DO NOT use arbitrary find, ls -R, or arbitrary file searches to discover project architecture! The memB ecosystem natively maintains a physical AI-First Vault at ~/.MemBDB/memB_Vault/.

1. Always read ~/.MemBDB/memB_Vault/God_Mode.md FIRST to understand the ecosystem topology.
2. Navigate the tree via the _Hub.md files.
3. Only search the actual workspace filesystem for raw code editing once you know the exact file path.

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🧠 Agentic Ingestion (Intelligent Vault Building)

You are responsible for intelligently categorized ingestion. Do NOT rely on static rules. When asked to ingest a project into memB:

1. Analyze the Target: Briefly scan the target directory to understand the project's purpose (e.g. 3D WebGL site, Python API, React Native app).
2. Design Semantic Categories: Dynamically invent highly precise categories tailored to the project (e.g., 3D_Engine, Routing_Logic, Database_Schemas, Styling_System).
3. Targeted Ingestion: Execute the Python ingestion tool explicitly for specific files and categories, rather than doing a blind root scan.

Execution: Use the run_command tool to execute memb_ingest.py on specific files or folders, explicitly passing the --project and --category flags.

/Users/timrennings/.gemini/mcps/memb-mcp/.venv/bin/python /Users/timrennings/bdb-dev/bdb-dev-optimized-agent-skills/mcps/memb-mcp/memb_ingest.py path/to/specific_file.md --project "MyProject" --category "3D_Engine"

(By injecting files one-by-one or in smart batches with precise categories, you build a flawless physical AI Vault that other agents can navigate intuitively).

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🚀 Micro-Targeted RAG (Task Execution)

1. Task Start (Context Loading): If God_Mode.md shows the project exists, read its Hubs. If you need highly specific snippets, use the search_memory MCP tool to query the vector DB.
2. Execution: Proceed with coding, applying the retrieved styles and preferences.
3. Task End (Knowledge Capture): If you resolved a complex setup bug or the user specified a new preference, run add_memory to persist it.