Elixir Performance Review logo

Elixir Performance Review

Organization
existential-birds
elixir-performance-review

Reviews Elixir code for performance issues including GenServer bottlenecks, memory usage, and concurrency patterns. Use when reviewing high-throughput code or investigating performance issues.

Overview

Publisherexistential-birds
Repositorybeagle
Skill nameelixir-performance-review
Stars
82
Forks
8
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by existential-birds on GitHub. Read the source before you install it.

Installation

Install the Elixir Performance Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/existential-birds/beagle.git /tmp/beagle
mkdir -p .claude/skills
cp -r /tmp/beagle/plugins/beagle-elixir/skills/elixir-performance-review .claude/skills/elixir-performance-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Elixir Performance Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Elixir Performance Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Elixir Performance Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Elixir Performance Review

Quick Reference

Issue TypeReference
Mailbox overflow, blocking callsreferences/genserver-bottlenecks.md
When to use ETS, read/write concurrencyreferences/ets-patterns.md
Binary handling, large messagesreferences/memory.md
Task patterns, flow controlreferences/concurrency.md

Review Checklist

GenServer

  • Not a single-process bottleneck for all requests
  • No blocking operations in handle_call/cast
  • Proper timeout configuration
  • Consider ETS for read-heavy state

Memory

  • Large binaries not copied between processes
  • Streams used for large data transformations
  • No unbounded data accumulation

Concurrency

  • Task.Supervisor for dynamic tasks (not raw Task.async)
  • No unbounded process spawning
  • Proper backpressure for message producers

Database

  • Preloading to avoid N+1 queries
  • Pagination for large result sets
  • Indexes for frequent queries

Valid Patterns (Do NOT Flag)

  • Single GenServer for low-throughput - Not all state needs horizontal scaling
  • Synchronous calls for critical paths - Consistency may require it
  • In-memory state without ETS - ETS has overhead for small state
  • Enum over Stream for small collections - Stream overhead not worth it

Context-Sensitive Rules

IssueFlag ONLY IF
GenServer bottleneckHandles > 1000 req/sec OR blocking I/O in callbacks
Use streamsProcessing > 10k items OR reading large files
Use ETSRead:write ratio > 10:1 AND concurrent access

Gates — before reporting

Do these in order for the performance review. Do not publish findings until each step passes.

  1. Protocol loaded — Read review-verification-protocol and apply its checks for each finding (hot paths, concurrency, resource use). Pass: For every substantive finding, you can name which protocol subsection you satisfied or state N/A with reason (e.g. pure reference to this skill’s Valid Patterns).
  2. Anchored evidencePass: Each finding includes a concrete locator: path:line (or line range), or Module.function/arity plus a short quoted snippet from the file.
  3. Performance claims — For anything under Context-Sensitive Rules, or any claim of bottleneck, N+1, unbounded growth, or heavy memory/binary cost, Pass: You state the observed or measured fact that meets “Flag ONLY IF” (e.g. rate, item count, ratio), or attach an artifact (profiler output, SQL/log excerpt, grep/search scope)—otherwise downgrade to question / suspected with what was not verified.

Before Submitting Findings

Complete Gates — before reporting (section above) first; the verification protocol is mandatory input to those gates.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Elixir Performance Review AI skill do?

Reviews Elixir code for performance issues including GenServer bottlenecks, memory usage, and concurrency patterns. Use when reviewing high-throughput code or investigating performance issues.

Why use Elixir Performance Review on TypingMind?

Because you install it once and use it with any model. Elixir Performance Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Elixir Performance Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/existential-birds/beagle/tree/main/plugins/beagle-elixir/skills/elixir-performance-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Elixir Performance Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Elixir Performance Review?

As many as you like. As long as a model supports skills, you can use Elixir Performance Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Elixir Performance Review AI skill free?

Yes. It is published on GitHub by existential-birds under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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