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

# Search everything
./scripts/search.sh "auth jwt oauth"

# Search only memory files
./scripts/search.sh "database migration" --scope memory

# Search only role files
./scripts/search.sh "dashboard" --scope roles --top 3

# Search only skills
./scripts/search.sh "testing" --scope skills --top 10

How It Works

1. Tokenization: Query is split into lowercase terms
2. TF (Term Frequency): For each file, count occurrences of each query term, normalized by file length
3. IDF (Inverse Document Frequency): Terms that appear in fewer files get higher weight
4. Score: TF × IDF summed across all query terms
5. 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:

# Before /resume — find what was done last week
./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