Python Database Patterns
CheckedHelps with Python, database, patterns as part of developing backend services and APIs workflows.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
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terminal$ npx skills add neversight/learn-skills.dev --skill python-database-patterns -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
neversight
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Install target
Live repository
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Automated safety checks
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
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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
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Pinned to a commit: passed
Scanned neversight/skills_feed at 58220bbac4.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 58220bbac4
- Scanned
- 13 files, 180 lines
About this skill
What it does
This skill enables AI agents to interact with databases using established Python database patterns. It facilitates tasks like querying data, inserting new records, updating existing entries, and deleting information based on specific criteria. The agent can leverage these patterns for efficient and structured database operations within various applications.
When to use it
- Data Extraction: Retrieve specific data points from a relational database for reporting or analysis.
- Automated Data Entry: Populate a database with new information gathered from external sources, such as web scraping or API integrations.
- Database Updates: Modify existing records in a database based on predefined rules or user input.
- Data Cleanup: Delete obsolete or incorrect data entries to maintain the integrity of a database.
Key capabilities
- Python Database Interaction
- Established Database Patterns
- Querying Capabilities (SELECT)
- Insertion Capabilities (INSERT)
- Update Capabilities (UPDATE)
- Deletion Capabilities (DELETE)
Example prompts
- "Retrieve all customer records from the 'customers' table where the city is 'New York'."
- "Insert a new product with name 'Widget', price 19.99, and category 'Electronics' into the 'products' table."
- "Update the email address for customer ID 123 to '[email protected]'."
Tips & gotchas
- Requires familiarity with SQL syntax and database schema.
- Ensure the agent has appropriate database credentials and permissions before execution.