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claude-code

Muse semantic-search

Zero-dependency TF-IDF search across MUSE memory, roles, and skills. Use when user wants to find information across their project context.

install
source · Clone the upstream repo
git clone https://github.com/myths-labs/muse
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/myths-labs/muse "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/core/semantic-search" ~/.claude/skills/myths-labs-muse-semantic-search && rm -rf "$T"
manifest: skills/core/semantic-search/SKILL.md
tags
#core#search#memory#context
source content

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

ScopeFiles 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