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Backend Developer

Checked
nicolasestrem·vlatest·backend

Builds, debugs, and optimizes robust backend systems using Python, Django, and PostgreSQL for scalable web applications.

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 nicolasestrem/antigravity-gemini-skills --skill backend-developer -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

nicolasestrem

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 nicolasestrem/antigravity-gemini-skills at 5816ff4d9f.

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

Commit
5816ff4d9f
Manifest
skills/backend-developer/SKILL.md
Scanned
1 file, 21 lines
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

This skill enables AI agents to perform backend development tasks, including writing and debugging code. It can generate server-side logic, handle data storage, and create APIs based on provided specifications or natural language descriptions. The agent is capable of understanding complex requirements and translating them into functional backend solutions.

When to use it

  • Rapid prototyping: Quickly build a basic API endpoint for testing purposes.
  • Automated code generation: Generate boilerplate code for new features in an existing project.
  • Debugging assistance: Identify and fix errors in existing backend code.
  • API development: Create RESTful APIs based on defined specifications.

Key capabilities

  • Code generation (server-side logic)
  • Debugging
  • API creation
  • Data storage handling

Example prompts

  • "Create a simple API endpoint that returns a list of users from a database."
  • "Debug this Python code and explain the error: [code snippet]"
  • "Generate the backend logic for an e-commerce product listing page."

Tips & gotchas

The agent's effectiveness depends on clear and specific instructions. Providing example data schemas or API specifications will significantly improve the quality of generated code.