recall

name	recall
description	Retrieve relevant knowledge from Obsidian vault using 3-layer semantic search based on conversation context
argument-hint	<search query or topic>
allowed-tools	Read, Bash, Grep

Semantic Knowledge Retrieval

You are tasked with retrieving relevant knowledge from the Obsidian vault using multi-layer semantic search.

Local Brain Search

Use Local Brain Search for all semantic search operations. Spreading activation mode recommended for synthesis queries.

Scripts:

# Static search (fast, exact matches)
resources/local-brain-search/run_search.sh "query" --limit 10 --json

# Spreading activation search (follows graph connections)
resources/local-brain-search/run_search.sh "query" --mode spreading --limit 10 --json

# Find connections
resources/local-brain-search/run_connections.sh "Note Name" --json

# Find hubs
resources/local-brain-search/run_connections.sh --hubs --json

Search Query

$ARGUMENTS

Instructions

1. First Layer - Initial Search:

   • Use spreading activation for better context:

     resources/local-brain-search/run_search.sh "$ARGUMENTS" --mode spreading --limit 5 --json
     

   • Use Read tool to read the full content of the top 2 results

2. Second Layer - Direct Associations:

   • For the top result from layer 1, get connections:

     resources/local-brain-search/run_connections.sh "Top Result Note" --json
     

   • Use Read tool to read the full content of the top 2 connected notes

3. Third Layer - Extended Network:

   • For additional context, check hub notes and bridges:

     resources/local-brain-search/run_connections.sh --hubs --json
     

   • This reveals deeper conceptual connections

Output Format

Present the findings in this structured format:

# Knowledge Recall: [Query Topic]

## Layer 1: Direct Matches
[List notes found with similarity/activation scores and key excerpts]

## Layer 2: First-Degree Associations
[List connected notes with their relationships and excerpts]

## Layer 3: Extended Network
[Show hub notes and bridge connections]

## Key Insights
[Synthesize the main themes and connections discovered]

## Relevant Content
[Include the most pertinent excerpts from the retrieved notes]

Important Notes

• Use --mode spreading for synthesis and connection-finding queries
• Use static mode for exact factual lookups
• Focus on quality over quantity
• Highlight unexpected connections
• Provide enough context for the user to understand the relevance
• If search returns no results, try broader terms or related concepts
• Learning active: Searches are tracked and rankings improve over time based on usage

State Dependencies

Source	Location	Read	Write	Description
Brain notes	Brain/**/*.md	X		Search permanent notes, sources, MOCs
Local Brain Search index	resources/local-brain-search/	X		Vector index for semantic search
Memory config	resources/local-brain-search/memory_config.py	X		Tunable memory parameters

Completion Checklist

• Layer 1 search executed (spreading mode for synthesis queries)
• Layer 2 connections retrieved for top result
• Layer 3 hub notes checked for context
• Key insights synthesized from findings
• Relevant excerpts included in output