Skip to content
TrustedSkillsbeta
Back to all skills

Django Perf Review

Official
getsentry·vlatest·backend·Apache-2.0

Analyzes Django application performance metrics from Sentry to identify bottlenecks and suggest optimization opportunities.

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 getsentry/skills --skill django-perf-review -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

getsentry

Matched to the vendor's own GitHub organisation.

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 getsentry/skills at c2f99a5b04.

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

Commit
c2f99a5b04
Manifest
skills/django-perf-review/SKILL.md
Scanned
2 files, 357 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 analyzes Django application performance metrics sourced from Sentry, focusing on identifying and validating potential bottlenecks within the codebase. It aims to pinpoint specific areas for optimization by tracing data flow and verifying issues before reporting them. The tool prioritizes finding provable problems and avoids generating false positives.

When to use it

  • When investigating slow request times or timeouts in a Django application.
  • To proactively identify performance bottlenecks before they impact users at scale.
  • During code reviews to ensure efficient database queries and data access patterns.
  • After deploying new features or making significant changes to the codebase.
  • When Sentry reports indicate potential performance issues requiring deeper investigation.

Key capabilities

  • N+1 Query Detection: Identifies inefficient query patterns where a loop iterates through results, triggering an additional database query for each item.
  • Unbounded Queryset Analysis: Detects potentially memory-exhausting querysets that could lead to "Out of Memory" (OOM) errors.
  • Missing Index Identification: Highlights opportunities to improve query performance by adding missing indexes on large tables.
  • Write Loop Detection: Identifies loops causing lock contention and slowing down request processing.
  • Prioritization based on Impact: Focuses on critical and high-impact issues (e.g., N+1 queries, unbounded querysets) that significantly affect application performance at scale.

Example prompts

  • "Analyze the Django code for potential N+1 query patterns."
  • "Review recent Sentry reports for performance bottlenecks in the user list view."
  • "Identify any missing indexes contributing to slow database queries."

Tips & gotchas

  • The skill relies on data from Sentry; ensure proper instrumentation and reporting are configured.
  • Validation is crucial – the tool flags potential issues, but manual verification of the reported problems is required.
  • Focus on critical and high-impact findings first to maximize efficiency.