About memfs-search
memfs-search is a skill for searching a memory filesystem with keyword, semantic, and hybrid retrieval. It helps agents find conceptually related material across Markdown memory files and retrieve matching documents.
Key features
- 01Keyword, semantic, and hybrid search
- 02Retrieve documents by path or in batches
- 03Output JSON, file lists, or full documents
- 04Build and refresh the search index
Use cases
- 01Find topic-related material in memory
- 02Search using a vague description
- 03Check for existing material before adding a memory file
- 04Reindex after bulk memory changes
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ letta-ai/ skills --skill memfs-searchAfter installing, type/memfs-searchin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
LICENSEreferences/qmd-setup.mdscripts/memfs-search.shDetails
From the project READMEMemFS Search
Semantic search over your memory filesystem. Useful when Grep isn't enough — finding conceptually related blocks, discovering forgotten reference files, or answering "what do I know about X" across all memory.
Setup
First time only. Run the setup script to create the index and generate embeddings:
bash <SKILL_DIR>/scripts/memfs-search.sh setup
This creates a QMD collection over $MEMORY_DIR, adds context annotations, and embeds all .md files. First run downloads ~2GB of local GGUF models to ~/.cache/qmd/models/.
For installation, embedding model options, and troubleshooting: references/qmd-setup.md.
Searching
Three tiers. Pick based on what you know about your query:
| You have... | Use | Command | Speed |
|---|---|---|---|
| An exact term or phrase | keyword | search | ~0.3s |
| A vague concept ("what do I know about X") | semantic | vsearch | ~2s cold, <1s warm |
| No idea, need the best results | hybrid | query | ~3s cold, <1s warm |
S="bash <SKILL_DIR>/scripts/memfs-search.sh"
# Keyword — fast, use first
$S search "lettabot architecture"
# Semantic — conceptual, use when keyword misses
$S vsearch "how does the user feel about code reviews"
# Hybrid — best quality, uses keyword + vectors + reranking
$S query "projects cameron is working on"
Always start with keyword search. Only escalate when it misses. Hybrid is 10x slower than keyword.
Output Formats
All commands accept output flags forwarded to QMD:
$S search "topic" --json # structured (for processing)
$S search "topic" --files # file paths only (pipe into Read)
$S search "topic" --full # full document, not snippet
$S search "topic" -n 15 # more results (default: 5)
--json returns an array of objects with file, score, snippet, and context fields.
Retrieval
Fetch a specific file or batch of files without searching:
# Single file
qmd get "system/human/identity.md" -c memory --full
# Batch by glob
qmd multi-get "reference/projects/*" -c memory
When to Search Proactively
Don't wait to be asked. Search memory when:
- Before creating a new memory file — check if the topic already exists.
$S search "topic" --filestells you instantly. - User asks "do you know about X" — search before saying no. Reference files you haven't loaded recently might have it.
- During
/initor memory reorg — verify coverage. Search for key concepts and confirm they're stored somewhere. - Debugging "I told you about this" — the user thinks you should know something. Search memory before falling back to message history.
Maintenance
After bulk memory changes (e.g. after /init, reorganization, creating many files):
bash <SKILL_DIR>/scripts/memfs-search.sh reindex
Check index health:
bash <SKILL_DIR>/scripts/memfs-search.sh status
When NOT to Use
- Exact string matching — use Grep.
- Finding files by name/pattern — use Glob.
- Reading a file you already know the path to — use Read.
- Searching message history — use the
searching-messagesskill. - The query is a single word that would match literally — keyword Grep is faster.