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Llm Api Benchmark

Checked
ridewind·vlatest·ai-ml·MIT

Helps with LLMs, API as part of building AI and machine learning applications 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 ridewind/my-skills --skill llm-api-benchmark -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

ridewind

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

    Only contacts known or declared hosts: api.anthropic.com, api.openai.com, generativelanguage.googleapis.com.

    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.

    Hosts contacted

    api.anthropic.commodel providerapi.openai.commodel providergenerativelanguage.googleapis.commodel provider

  • 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 ridewind/my-skills at c0ca309f44.

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

Commit
c0ca309f44
Manifest
skills/llm-api-benchmark/SKILL.md
Scanned
8 files, 2,171 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 benchmarks Large Language Model (LLM) APIs. It provides a standardized way to evaluate and compare different LLMs based on performance metrics like latency, cost, and output quality across various tasks. The benchmark results can be used for model selection or optimization purposes.

When to use it

  • Model Selection: When choosing between multiple LLM providers (e.g., OpenAI, Cohere, Anthropic) for a specific application.
  • Cost Optimization: To identify the most cost-effective LLM for a given workload by comparing pricing and performance.
  • Performance Monitoring: Track changes in LLM API performance over time to ensure consistent quality and efficiency.
  • A/B Testing: Compare different prompt strategies or model configurations within an application using benchmark data.

Key capabilities

  • LLM API benchmarking
  • Performance metric evaluation (latency, cost, output quality)
  • Standardized task execution across models
  • Model comparison and selection assistance

Example prompts

  • "Benchmark the GPT-3.5 and Claude 2 APIs for summarization tasks."
  • "Compare the cost per token of each available LLM API."
  • "Run a benchmark suite on the Llama 2 model and report latency results."

Tips & gotchas

This skill requires access to multiple LLM APIs, so ensure you have appropriate API keys configured for each. The accuracy of the benchmarks depends heavily on the quality and diversity of the tasks used in the evaluation.