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Tech Drizzle

Checked
ravnhq·vlatest·database

Generates concise SQL queries and explanations from natural language requests for efficient database interaction.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add ravnhq/ai-toolkit --skill tech-drizzle -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

ravnhq

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned ravnhq/ai-toolkit at e6237ae66e.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
e6237ae66e
Manifest
skills/database/tech-drizzle/SKILL.md
Scanned
26 files, 2,112 lines
Copies in repo
2 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The tech-drizzle skill allows AI agents to query and retrieve data from various database systems. It supports SQL queries, enabling the agent to extract specific information based on defined criteria. This skill facilitates interaction with databases like PostgreSQL, MySQL, and SQLite, simplifying data access for AI applications.

When to use it

  • Data Extraction: Retrieve customer order history from a relational database for analysis or reporting.
  • Inventory Management: Query product stock levels in an inventory database to optimize supply chain decisions.
  • Content Retrieval: Fetch articles and metadata from a content management system (CMS) database based on keywords or categories.
  • Report Generation: Build dynamic reports by querying data from multiple tables within a database.

Key capabilities

  • Supports SQL queries.
  • Compatible with PostgreSQL, MySQL, and SQLite databases.
  • Data retrieval from relational databases.

Example prompts

  • "Get me the names of all customers who placed an order in the last month."
  • "What is the current stock level for product ID 12345?"
  • "Retrieve articles tagged with 'technology' and published after January 1, 2023."

Tips & gotchas

  • Ensure the AI agent has appropriate database credentials (username, password) to access the target database.
  • Be mindful of SQL injection vulnerabilities when constructing queries; sanitize user inputs if applicable.