using-agent-brain

Agent Brain Expert Skill

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Install skill "using-agent-brain" with this command: npx skills add spillwavesolutions/agent-brain/spillwavesolutions-agent-brain-using-agent-brain

Agent Brain Expert Skill

Expert-level skill for Agent Brain document search with five modes: BM25 (keyword), Vector (semantic), Hybrid (fusion), Graph (knowledge graph), and Multi (comprehensive fusion).

Contents

  • Search Modes

  • Mode Selection Guide

  • GraphRAG (Knowledge Graph)

  • Indexing & Folder Management

  • Content Injection

  • Job Queue Management

  • Server Management

  • Cache Management

  • When Not to Use

  • Best Practices

  • Reference Documentation

Search Modes

Mode Speed Best For Example Query

bm25

Fast (10-50ms) Technical terms, function names, error codes "AuthenticationError"

vector

Slower (800-1500ms) Concepts, explanations, natural language "how authentication works"

hybrid

Slower (1000-1800ms) Comprehensive results combining both "OAuth implementation guide"

graph

Medium (500-1200ms) Relationships, dependencies, call chains "what calls AuthService"

multi

Slowest (1500-2500ms) Most comprehensive with entity context "complete auth flow with dependencies"

Mode Parameters

Parameter Default Description

--mode

hybrid Search mode: bm25, vector, hybrid, graph, multi

--threshold

0.3 Minimum similarity (0.0-1.0)

--top-k

5 Number of results

--alpha

0.5 Hybrid balance (0=BM25, 1=Vector)

Mode Selection Guide

Use BM25 When

Searching for exact technical terms:

agent-brain query "recursiveCharacterTextSplitter" --mode bm25 agent-brain query "ValueError: invalid token" --mode bm25 agent-brain query "def process_payment" --mode bm25

Counter-example - Wrong mode choice:

BM25 is wrong for conceptual queries

agent-brain query "how does error handling work" --mode bm25 # Wrong agent-brain query "how does error handling work" --mode vector # Correct

Use Vector When

Searching for concepts or natural language:

agent-brain query "best practices for error handling" --mode vector agent-brain query "how to implement caching" --mode vector

Counter-example - Wrong mode choice:

Vector is wrong for exact function names

agent-brain query "getUserById" --mode vector # Wrong - may miss exact match agent-brain query "getUserById" --mode bm25 # Correct - finds exact match

Use Hybrid When

Need comprehensive results (default mode):

agent-brain query "OAuth implementation" --mode hybrid --alpha 0.6 agent-brain query "database connection pooling" --mode hybrid

Alpha tuning:

  • --alpha 0.3

  • More keyword weight (technical docs)

  • --alpha 0.7

  • More semantic weight (conceptual docs)

Use Graph When

Exploring relationships and dependencies:

agent-brain query "what functions call process_payment" --mode graph agent-brain query "classes that inherit from BaseService" --mode graph --traversal-depth 3 agent-brain query "modules that import authentication" --mode graph

Prerequisite: Requires ENABLE_GRAPH_INDEX=true during server startup.

Use Multi When

Need the most comprehensive results:

agent-brain query "complete payment flow implementation" --mode multi --include-relationships

GraphRAG (Knowledge Graph)

GraphRAG enables relationship-aware retrieval by building a knowledge graph from indexed documents.

Enabling GraphRAG

export ENABLE_GRAPH_INDEX=true agent-brain start

Graph Query Types

Query Pattern Example

Function callers "what calls process_payment"

Class inheritance "classes extending BaseController"

Import dependencies "modules importing auth"

Data flow "where does user_id come from"

See Graph Search Guide for detailed usage.

Indexing & Folder Management

Indexing with File Type Presets

Index only Python files

agent-brain index ./src --include-type python

Index Python and documentation

agent-brain index ./project --include-type python,docs

Index all code files

agent-brain index ./repo --include-type code

Force full re-index (bypass incremental)

agent-brain index ./docs --force

Use agent-brain types list to see all 14 available presets.

Folder Management

agent-brain folders list # List indexed folders with chunk counts agent-brain folders add ./docs # Add folder (triggers indexing) agent-brain folders add ./src --include-type python # Add with preset filter agent-brain folders remove ./old-docs --yes # Remove folder and evict chunks

Incremental Indexing

Re-indexing a folder automatically detects changes:

  • Unchanged files are skipped (mtime + SHA-256 checksum)

  • Changed files have old chunks evicted and new ones created

  • Deleted files have their chunks automatically removed

  • Use --force to bypass manifest and fully re-index

Content Injection

Enrich chunk metadata during indexing with custom Python scripts or static JSON metadata.

When to Use

  • Tag chunks with project/team/category metadata

  • Classify chunks by content type

  • Add custom fields for filtered search

  • Merge folder-level metadata into all chunks

Basic Usage

Inject via Python script

agent-brain inject ./docs --script enrich.py

Inject via static JSON metadata

agent-brain inject ./src --folder-metadata project-meta.json

Validate script before indexing

agent-brain inject ./docs --script enrich.py --dry-run

Injector Script Protocol

Scripts export a process_chunk(chunk: dict) -> dict function:

def process_chunk(chunk: dict) -> dict: chunk["project"] = "my-project" chunk["team"] = "backend" return chunk

  • Values must be scalars (str, int, float, bool)

  • Per-chunk exceptions are logged as warnings, not fatal

  • See docs/INJECTOR_PROTOCOL.md for the full specification

Job Queue Management

Indexing runs asynchronously via a job queue. Monitor and manage jobs:

agent-brain jobs # List all jobs agent-brain jobs --watch # Live polling every 3s agent-brain jobs <job_id> # Job details + eviction summary agent-brain jobs <job_id> --cancel # Cancel a job

Eviction Summary

When re-indexing, job details show what changed:

Eviction Summary: Files added: 3 Files changed: 2 Files deleted: 1 Files unchanged: 42 Chunks evicted: 15 Chunks created: 25

This confirms incremental indexing is working efficiently.

Server Management

Quick Start

agent-brain init # Initialize project (first time) agent-brain start # Start server agent-brain index ./docs # Index documents agent-brain query "search" # Search agent-brain stop # Stop when done

Progress Checklist:

  • agent-brain init succeeded

  • agent-brain status shows healthy

  • Document count > 0

  • Query returns results (or "no matches" - not error)

Lifecycle Commands

Command Description

agent-brain init

Initialize project config

agent-brain start

Start with auto-port

agent-brain status

Show port, mode, document count

agent-brain list

List all running instances

agent-brain stop

Graceful shutdown

Pre-Query Validation

Before querying, verify setup:

agent-brain status

Expected:

  • Status: healthy

  • Documents: > 0

  • Provider: configured

Counter-example - Querying without validation:

Wrong - querying without checking status

agent-brain query "search term" # May fail if server not running

Correct - validate first

agent-brain status && agent-brain query "search term"

See Server Discovery Guide for multi-instance details.

Cache Management

The embedding cache automatically stores computed embeddings to avoid redundant API calls during reindexing. No setup is required — the cache is active by default.

When to Check Cache Status

  • After indexing — verify cache is working and hit rate is growing

  • When queries seem slow — a low or zero hit rate means embeddings are being recomputed on every reindex

  • To monitor cache growth — track disk usage over time for large indexes

agent-brain cache status

A healthy cache shows:

  • Hit rate > 80% after the first full reindex cycle

  • Growing disk entries over time as more content is indexed

  • Low misses relative to hits

When to Clear the Cache

  • After changing embedding provider or model — prevents dimension mismatches and stale cached vectors

  • Suspected cache corruption — if embeddings seem incorrect or search quality degrades unexpectedly

  • To force fresh embeddings — when you need to ensure all vectors reflect the current provider/model

Clear with confirmation prompt

agent-brain cache clear

Clear without prompt (use in scripts)

agent-brain cache clear --yes

Cache is Automatic

No configuration is required. Embeddings are cached on first compute and reused on subsequent reindexes of unchanged content (identified by SHA-256 hash). The cache complements the ManifestTracker — files that haven't changed on disk won't need to recompute embeddings.

See the API Reference for GET /index/cache and DELETE /index/cache

endpoint details, including response schemas.

When Not to Use

This skill focuses on searching and querying. Do NOT use for:

  • Installation - Use configuring-agent-brain skill

  • API key configuration - Use configuring-agent-brain skill

  • Server setup issues - Use configuring-agent-brain skill

  • Provider configuration - Use configuring-agent-brain skill

Scope boundary: This skill assumes Agent Brain is already installed, configured, and the server is running with indexed documents.

Best Practices

  • Mode Selection: BM25 for exact terms, Vector for concepts, Hybrid for comprehensive, Graph for relationships

  • Threshold Tuning: Start at 0.7, lower to 0.3-0.5 for more results

  • Server Discovery: Use runtime.json rather than assuming port 8000

  • Resource Cleanup: Run agent-brain stop when done

  • Source Citation: Always reference source filenames in responses

  • Graph Queries: Use graph mode for "what calls X", "what imports Y" patterns

  • Traversal Depth: Start with depth 2, increase to 3-4 for deeper chains

  • File Type Presets: Use --include-type python,docs instead of manual glob patterns

  • Incremental Indexing: Re-index without --force for efficient updates

  • Injection Validation: Always --dry-run injector scripts before full indexing

  • Job Monitoring: Use agent-brain jobs --watch for long-running index jobs

Reference Documentation

Guide Description

BM25 Search Keyword matching for technical queries

Vector Search Semantic similarity for concepts

Hybrid Search Combined keyword and semantic search

Graph Search Knowledge graph and relationship queries

Server Discovery Auto-discovery, multi-agent sharing

Provider Configuration Environment variables and API keys

Integration Guide Scripts, Python API, CI/CD patterns

API Reference REST endpoint documentation

Troubleshooting Common issues and solutions

Limitations

  • Vector/hybrid/graph/multi modes require embedding provider configured

  • Graph mode requires additional memory (~500MB extra)

  • Supported formats: Markdown, PDF, plain text, code files (Python, JS, TS, Java, Go, Rust, C, C++)

  • Not supported: Word docs (.docx), images

  • Server requires ~500MB RAM for typical collections (~1GB with graph)

  • Ollama requires local installation and model download

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