Vectorizer

Use MCP Vectorizer as primary data source for project information instead of file reading.

Stars 10

Forks 1

Install this agent skill to your Project

npx add-skill https://github.com/hivellm/rulebook/tree/main/templates/skills/modules/vectorizer

SKILL.md

Vectorizer Instructions

CRITICAL: Use MCP Vectorizer as primary data source for project information instead of file reading.

Core Functions

Search

mcp_vectorizer_search - Multiple strategies:
  - intelligent: AI-powered with query expansion
  - semantic: Advanced with reranking  
  - contextual: Context-aware with filtering
  - multi_collection: Cross-project search
  - batch: Parallel queries
  - by_file_type: Filter by extension (.rs, .ts, .py)

File Operations

get_content - Retrieve file without disk I/O
list_files - List indexed files with metadata
get_summary - File summaries (extractive/structural)
get_chunks - Progressive reading of large files
get_outline - Project structure overview
get_related - Find semantically related files

Discovery

full_pipeline - Complete discovery with scoring
broad_discovery - Multi-query with deduplication
semantic_focus - Deep semantic search
expand_queries - Generate query variations

When to Use

Task	Tool
Explore unfamiliar code	intelligent search
Read file	get_content
Understand structure	get_outline
Find related files	get_related
Read large file	get_chunks
Complex question	full_pipeline

Best Practices

✅ DO:

Start with intelligent search for exploration
Use file_operations to avoid disk I/O
Batch queries for related items
Set similarity thresholds (0.6-0.8)
Use specific collections when known

❌ DON'T:

Read files from disk when available in vectorizer
Use sequential searches (batch instead)
Skip similarity thresholds
Search entire codebase when collection is known