// Save content to SuperLocalMemory with intelligent indexing and knowledge graph integration. Use when the user wants to remember information, save context, store coding decisions, or persist knowledge for future sessions. Automatically indexes, graphs, and learns patterns.
| name | slm-remember |
| description | Save content to SuperLocalMemory with intelligent indexing and knowledge graph integration. Use when the user wants to remember information, save context, store coding decisions, or persist knowledge for future sessions. Automatically indexes, graphs, and learns patterns. |
| version | 3.4.23 |
| license | AGPL-3.0-or-later |
| compatibility | Requires SuperLocalMemory V2 installed at ~/.claude-memory/ |
| attribution | {"creator":"Varun Pratap Bhardwaj","role":"Solution Architect & Original Creator","project":"SuperLocalMemory V2"} |
Save content to your local memory system with automatic indexing, knowledge graph integration, and pattern learning.
slm remember "<content>" [--tags tag1,tag2] [--project name] [--importance 1-10]
slm remember "We use FastAPI for REST APIs"
What happens:
slm remember "JWT tokens expire after 24 hours" --tags security,auth,jwt
Tags help with:
slm remember "Database uses PostgreSQL 15 with UUID primary keys" --project myapp --tags database,postgresql
Project isolation:
slm switch-profileslm remember "CRITICAL: Production deploy requires approval from @lead" --importance 10 --tags deployment,production
Importance (1-10):
| Argument | Type | Required | Default | Description |
|---|---|---|---|---|
<content> | string | Yes | - | The text to remember |
--tags | string | No | None | Comma-separated tags |
--project | string | No | "default" | Project name |
--importance | integer | No | 5 | Priority level (1-10) |
Memory added with ID: 42
✅ Memory saved successfully
Next steps:
• Use `slm recall <query>` to search this memory
• Use `slm list` to see recent memories
~/.claude-memory/memory.db)Most AI assistants will automatically invoke this skill when you say:
Example in Cursor/Claude:
You: "Remember that we decided to use React hooks over class components"
AI: [Automatically invokes slm-remember skill]
✓ Memory saved
Save multiple memories from a file:
# From text file (one memory per line)
while IFS= read -r line; do
slm remember "$line" --project bulk-import
done < memories.txt
# From CSV (content,tags,project)
while IFS=',' read -r content tags project; do
slm remember "$content" --tags "$tags" --project "$project"
done < memories.csv
Pre-commit hook (save commit messages):
#!/bin/bash
# .git/hooks/post-commit
commit_msg=$(git log -1 --pretty=%B)
commit_hash=$(git log -1 --pretty=%H)
slm remember "Commit: $commit_msg (${commit_hash:0:7})" \
--tags git,commit \
--project "$(basename $(git rev-parse --show-toplevel))"
| Error | Cause | Solution |
|---|---|---|
| "Database locked" | Another process accessing DB | Wait or killall python3 |
| "Content cannot be empty" | Empty string passed | Provide content |
| "Invalid importance" | Value not 1-10 | Use number between 1-10 |
| "Database not found" | SuperLocalMemory not installed | Run ./install.sh |
slm switch-profile for profile isolationslm recall "<query>" - Search memories semanticallyslm list - List recent memoriesslm status - Check system healthslm build-graph - Rebuild knowledge graphslm switch-profile <name> - Switch memory profileDatabase Schema:
memoriesPerformance:
Limits:
Created by: Varun Pratap Bhardwaj (Solution Architect) Project: SuperLocalMemory V2 License: MIT (see LICENSE) Repository: https://github.com/varun369/SuperLocalMemoryV2
Open source doesn't mean removing credit. Attribution must be preserved per MIT License terms.
Build or rebuild the knowledge graph from existing memories using TF-IDF entity extraction and Leiden clustering. Use when search results seem poor, after bulk imports, or to optimize performance. Automatically discovers relationships between memories and creates topic clusters.
List most recent memories in chronological order. Use when the user wants to see what was recently saved, review recent conversations, check what they worked on today, or browse memory history. Shows memories sorted by creation time (newest first).
Search SuperLocalMemory for relevant facts, decisions, and past context. Use when the user asks to recall, search, find, or retrieve stored information. Invokes 5-channel retrieval with LightGBM reranking via MCP.
Check SuperLocalMemory system status, health, and statistics. Use when the user wants to know memory count, graph stats, patterns learned, database health, or system diagnostics. Shows comprehensive system health dashboard.
Switch between memory profiles for context isolation and management. Use when the user wants to change profile context, separate work/personal memories, or manage multiple independent memory spaces. Each profile has its own database, graph, and patterns.
Build or rebuild the knowledge graph from existing memories using TF-IDF entity extraction and Leiden clustering. Use when search results seem poor, after bulk imports, or to optimize performance. Automatically discovers relationships between memories and creates topic clusters.