| name | memb-ingest |
| description | Deep scan and ingest project files (.md, .json, agent.md, .openwiki) and past conversation logs into the local memB vector memory engine. |
memB Deep Memory Ingestion Skill
When this skill is invoked via /memb-ingest or requested, you MUST orchestrate a deep scan of the user-specified project path or conversation logs to populate the memB local vector database (~/.MemBDB/memb.db).
Core Workflow & Instructions
1. Target Path & Filter Selection
- Ask the user: "Which directory or project path would you like to scan into memB memory?" (Default: Current Workspace or
/Users/timrennings/bdb-dev)
- Ask if they would also like to include past Antigravity chat transcripts (
--transcripts) or filter specific file patterns (e.g., *.md, agent.md, .openwiki/).
2. Execution
Run the embedded python ingestion script:
/Users/timrennings/.gemini/mcps/memb-mcp/.venv/bin/python /Users/timrennings/bdb-dev/bdb-dev-optimized-agent-skills/mcps/memb-mcp/memb_ingest.py "<TARGET_DIRECTORY>" --transcripts
3. Verification
Query memB using search_memory or inspect ~/.MemBDB/memb.db to confirm that the project architecture and key decisions are indexed.
Execution Rules
- Never skip path confirmation: Always verify the target path before running the scan.
- Filter Noise: Ignore
node_modules, .venv, .git, dist, and temporary cache directories.
- Report Summary: Present a clear count of indexed documents to the user upon completion.
