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Reviewing Performance Patterns

Checked
djankies·vlatest·other·MIT

Helps with performance optimization, patterns as part of agent workflows workflows.

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 djankies/claude-configs --skill reviewing-performance-patterns -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

djankies

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 djankies/claude-configs at a82763a6e7.

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

Commit
a82763a6e7
Manifest
react-19/skills/reviewing-performance-patterns/SKILL.md
Scanned
1 file, 44 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 text data to identify and describe recurring patterns in performance or behavior. It can summarize trends, highlight anomalies, and provide insights into underlying causes based on the provided input. The output is designed to be actionable for improving processes or understanding complex systems.

When to use it

  • Analyzing customer service transcripts to identify common complaints or areas of agent training need.
  • Reviewing code commit messages to understand development trends and potential bottlenecks.
  • Summarizing performance review feedback to highlight key strengths and weaknesses.
  • Identifying recurring themes in user survey responses for product improvement.

Key capabilities

  • Pattern identification
  • Trend summarization
  • Anomaly detection
  • Root cause analysis (implied)

Example prompts

  • "Analyze these customer service transcripts and identify the top 3 recurring issues."
  • "Summarize the performance review feedback for this employee, highlighting key areas for improvement."
  • “What patterns do you see in these code commit messages related to feature X?”

Tips & gotchas

The quality of the analysis depends heavily on the clarity and completeness of the input data. Providing context or specific questions will improve the accuracy and relevance of the results.