| name | semantic-search |
| description | Zero-dependency TF-IDF search across MUSE memory, roles, and skills. Use when user wants to find information across their project context. |
| version | 1.0.0 |
| author | muse |
| tags | ["core","search","memory","context"] |
Semantic Search
Search across your entire MUSE project context using TF-IDF ranking.
When to Use
- User asks "where did we discuss X?"
- User wants to find a past decision or lesson
- User needs to locate a specific skill
- Context recovery when resuming work
Usage
./scripts/search.sh "auth jwt oauth"
./scripts/search.sh "database migration" --scope memory
./scripts/search.sh "dashboard" --scope roles --top 3
./scripts/search.sh "testing" --scope skills --top 10
How It Works
- Tokenization: Query is split into lowercase terms
- TF (Term Frequency): For each file, count occurrences of each query term, normalized by file length
- IDF (Inverse Document Frequency): Terms that appear in fewer files get higher weight
- Score: TF × IDF summed across all query terms
- Ranking: Files sorted by score, top N shown with best-matching line as context snippet
Scopes
| Scope | Files Indexed |
|---|
all (default) | memory/ + .muse/ + MEMORIES.md + skills/ |
memory | memory/*.md + MEMORIES.md |
roles | .muse/*.md |
skills | skills//SKILL.md + .agent/skills//SKILL.md |
Integration
When resuming a conversation, you can use search to quickly find relevant context:
./scripts/search.sh "migration deploy" --scope memory --top 3
Limitations
- Pure TF-IDF, no semantic understanding (no embeddings/vectors)
- Exact term matching only (no synonyms)
- Best for keyword-based queries with specific terms
- For semantic search, consider integrating with mem0 or memsearch
