richfrem/vector-db-search
plugins/vector-db/skills/vector-db-search/SKILL.md
Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints.
$ install --global
skillsauthnpx skillsauth add richfrem/agent-plugins-skills vector-db-searchInstall 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 28, 2026, 8:51 AM155.1s19 files scanned
SKILL.md
- name:
- vector-db-search
- plugin:
- vector-db
- description:
- Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints.
- allowed-tools:
- Bash, Read
Dependencies
This skill requires the chromadb and langchain packages defined in the plugin root.
Vector DB Search
Semantic (meaning-based) search against the ChromaDB vector store using a high-precision Parent-Child architecture. Use for Phase 2 of the 3-phase search protocol (RLM -> Vector -> Grep).
Scripts
| Script | Role |
|:-------|:-----|
| scripts/query.py | Semantic search CLI -- recovers context-rich parent chunks. |
| scripts/operations.py | Core domain logic for retrieval. |
| scripts/vector_config.py | Unified profile-based configuration loader. |
Execution Mode
This skill defaults to In-Process mode for zero-latency direct disk access. No background server is required. This ensures maximum stability in isolated project environments.
When to Use
- Phase 1 (RLM Summary Ledger) returned no match or insufficient detail.
- User asks "how does X work?" / "find code that does Y".
- You need specific high-context snippets (Parent chunks) for reasoning.
Execution Protocol
1. Identify Search Profile
Verify available profiles in .agent/learning/vector_profiles.json. The default profile is usually wiki.
2. Run Query
Note: The --profile flag is mandatory to ensure the correct model and collection are loaded.
python ./scripts/query.py "your natural language question" --profile wiki --limit 5
Results include ranked parent chunks (2,000 chars) that provide broad context to the LLM for reasoning.
Rules
- Profile Sovereignty: Always pass
--profileto ensure the correct semantic space is searched. - API Integrity: NEVER attempt to read the database SQLite or parquet files directly. Always use
query.py. - Transparency: When search returns empty results, state which profile and scope were searched.
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