hivellm/Vectorizer
templates/skills/modules/vectorizer/SKILL.md
Use MCP Vectorizer as primary data source for project information instead of file reading.
$ install --global
skillsauthnpx skillsauth add hivellm/rulebook VectorizerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 30, 2026, 3:17 AM388.8s1 file scanned
SKILL.md
- name:
- Vectorizer
- description:
- Use MCP Vectorizer as primary data source for project information instead of file reading.
- version:
- 1.0.0
- category:
- modules
- author:
- Rulebook
- tags:
- ["modules", "mcp"]
- dependencies:
- []
- conflicts:
- []
<!-- VECTORIZER:START -->
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
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