Does this skill expose my environment variables or secrets?

No, the skill is hardcoded to never print raw secret values, even if the retrieved content contains them, ensuring your credentials remain secure.

What happens if no results are found?

The skill will clearly state that no matches were found and automatically suggest a refined follow-up query to help you find the information you need.

What is the HydraDB Knowledge Search skill?

It is a specialized skill for Claude Code that allows users to manually query the HydraDB vector store to see exactly what information has been indexed about their project.

When should I use manual search instead of letting Claude handle it?

Manual search is ideal when you want to audit the AI's knowledge base, confirm documentation ingestion, or explicitly pull up historical project context.

HydraDB Knowledge Search

Name: HydraDB Knowledge Search
Author: usecortex

byusecortex

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Database Management

Queries your HydraDB workspace memory to retrieve and summarize relevant context and stored implementation details.

HydraDB Knowledge Search enables manual interrogation of your workspace's vector store, allowing you to explicitly verify or retrieve specific information that Claude has indexed. By performing bounded retrieval across active backends, it provides a summarized view of the most relevant matches, making the AI's 'memory' transparent and accessible for debugging context or recalling specific project details. The skill is built with security in mind, ensuring that sensitive data and secrets are never printed in plain text during the retrieval process.

Key Features

01Intelligent follow-up suggestions for unsuccessful queries

02Built-in protection against printing raw secret values

03Automatic summarization of high-confidence search matches

04Manual query execution for explicit knowledge retrieval

05Bounded retrieval from multiple active HydraDB backends

060 GitHub stars

Use Cases

01Recalling specific architectural decisions stored during previous sessions

02Verifying which project documentation or code snippets are currently indexed

03Debugging context gaps by inspecting what the AI currently knows about the workspace

Key Features

01Intelligent follow-up suggestions for unsuccessful queries

02Built-in protection against printing raw secret values

03Automatic summarization of high-confidence search matches

04Manual query execution for explicit knowledge retrieval

05Bounded retrieval from multiple active HydraDB backends

060 GitHub stars

Use Cases

01Recalling specific architectural decisions stored during previous sessions

02Verifying which project documentation or code snippets are currently indexed

03Debugging context gaps by inspecting what the AI currently knows about the workspace