git:20260827.e9312a3 to git:20260827.ebd5ea3

---

name: memb-skill

category: bdb-core

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.

---

## 🔒 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.

---

## 🛠️ 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.

---

## 🔌 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/`.

> Treat `.openwiki` files as the highest-priority source of truth for architectural context.

> 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.

---

## 🧠 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.

Because `memB` is supported across many AI platforms, the installation path depends on your current environment. The base path for MCPs is typically one of the following in the user's home directory (`~`):

- `~/.gemini/mcps/` (Antigravity)

- `~/.codex/mcps/` (Codex CLI / ChatGPT)

- `~/.cursor/mcps/` (Cursor)

- `~/.claude/mcps/` (Claude Desktop / Claude Code)

- `~/.windsurf/mcps/` (Windsurf)

- `~/.cline/mcps/` (Cline)

- `~/.roo/mcps/` (Roo Code)

- `~/.aider/mcps/` (Aider)

**You must resolve the correct path first**, then run the ingestion command using the python binary from the `.venv` inside that `memb-mcp` directory.

Example command structure (replace `<PLATFORM_DIR>` with the correct path discovered):

```bash

~/<PLATFORM_DIR>/mcps/memb-mcp/.venv/bin/python ~/<PLATFORM_DIR>/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).*

---

## 🚀 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.