Automated summary
Smart search across notes using BM25 relevance ranking, querying the graph index first with ranked results then falling back to grep for content search.
Strengths
- BM25 relevance ranking for accurate results.
- Fast initial search via graph index (~50ms).
- Automatic fallback to grep for full-text search.
- Supports filters by type, status, priority, and backlinks.
Limitations
- Requires a pre-built graph index.
- Grep search can be slow on large vaults (2-5s).
- Does not handle complex multi-filter advanced queries.
Use this command when you need to quickly find a specific note among many, benefiting from relevance-ranked results.
Avoid this command for very complex searches requiring advanced boolean logic or unsupported regex patterns.
Security analysis
CautionThe skill uses bash commands with unescaped user input, posing a medium risk of command injection in the agent's execution environment. No network exfiltration or obfuscated payloads, but the potential for destructive commands exists.
- •Command injection vulnerability: User-supplied query is interpolated directly into shell commands (graph-query.js --search and grep -r) without sanitization, allowing arbitrary command execution if special characters are included.
Examples
/search kafka/search type:Adr status:proposed/search backlinks:Project - MyProjectcontext: fork
/search
Smart search using BM25 relevance ranking. Queries the graph index first with ranked results, then falls back to grep for content search.
Usage
/search kafka
/search "API gateway"
/search type:Adr status:proposed
/search backlinks:Project - MyProject
Instructions
When the user invokes /search <query>:
Phase 1: Parse Query Type
Determine what kind of search this is:
| Pattern | Search Type | Action |
|---------|-------------|--------|
| type:<Type> | Type filter | Use graph --type |
| status:<status> | Status filter | Use graph --status |
| priority:<priority> | Priority filter | Use graph --priority |
| backlinks:<note> | Backlink search | Use graph --backlinks |
| Simple keyword | Keyword search | Use graph --search, then grep |
| Regex pattern | Content search | Skip to grep |
Phase 2: Query Graph Index First
Always start with the graph index (unless it's a regex):
# Check index exists
if [ ! -f ".graph/index.json" ]; then
npm run graph:build
fi
# Run graph query
node scripts/graph-query.js --search "<query>"
Phase 3: Evaluate Results
After running the graph query:
- If results found: Present them formatted as a table
- If no results OR user needs content search: Fall back to grep
# Grep fallback for content search
grep -r "<query>" --include="*.md" --exclude-dir=".git" --exclude-dir=".obsidian" --exclude-dir="node_modules" --exclude-dir=".smart-env"
Phase 4: Present Combined Results
Format output with BM25 relevance scores:
## Search Results for "<query>"
### Graph Index Results (BM25 ranked)
Found **X** notes matching in graph index:
| Score | Type | Title |
|-------|------|-------|
| 14.65 | Page | Kafka to SAP Integration |
| 11.32 | Adr | ADR - Kafka Integration |
| 9.45 | Meeting | Data Platform Discussion |
*Results ranked by relevance (higher score = better match)*
### Content Search Results (full-text)
Found **Y** additional matches in note content:
- `Page - Kafka Architecture.md:45` - "...kafka cluster configuration..."
- `Meeting - 2025-01-10 Data Team.md:12` - "...discussed kafka..."
Query Shortcuts
| Shortcut | Expands To |
|----------|------------|
| /search t:Adr | --type Adr |
| /search s:active | --status active |
| /search p:high | --priority high |
| /search b:Note | --backlinks "Note" |
| /search orphans | --orphans |
| /search broken | --broken-links |
| /search stale | --stale |
Examples
/search kafka # Keyword in graph + content
/search type:Meeting kafka # Meetings mentioning kafka
/search backlinks:Project - MyProject # Notes linking to project
/search status:proposed # All proposed items
/search orphans # Orphaned notes
/search "event.*driven" # Regex - grep only
Performance Notes
- Graph search (BM25): ~50ms for 1500 notes (pre-indexed with ranking)
- Grep search: ~2-5s depending on vault size
- Combined: Graph first catches 80%+ of searches instantly
- Relevance ranking: Results are sorted by BM25 score, so top results are most relevant
- IDF weighting: Rare terms like "kafka" score higher than common terms like "the"
Related
/graph-query- Direct graph queries with more options/orphans- Dedicated orphan analysis/broken-links- Dedicated broken link analysis
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