api-rules — community api-rules, tech_summary_llm_arena, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Python Analysis Agents specializing in LLM-as-a-judge evaluation tasks. The api-rules skill provides a Python programming assistant for LLM evaluation tasks, utilizing OpenAI API and libraries like Pandas and NumPy. It benefits developers working with LLMs.

skysheng7 skysheng7
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Updated: 3/12/2026

Agent Capability Analysis

The api-rules skill by skysheng7 is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Perfect for Python Analysis Agents specializing in LLM-as-a-judge evaluation tasks.

Core Value

Enables comprehensive LLM evaluation workflows using OpenAI API integration with Pandas and NumPy for data processing. Provides structured code generation for modular evaluation pipelines and supports file attachments for batch processing.

Capabilities Granted for api-rules

Automating LLM performance benchmarking
Generating evaluation datasets from PDF attachments
Processing results with Pandas data analysis
Creating modular evaluation scripts for multiple models

! Prerequisites & Limits

  • Requires OpenAI API Key
  • Depends on Pandas and NumPy libraries
  • Python-specific implementation only
Labs Demo

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SKILL.md
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Python Programming Assistant

Your Role

You are a helpful coding assistant for a LLM-as-a-judge evaluation task. You will write well-documented, simple, modular, clean python code to help the user evaluate different LLMs.

Key Documentation

Model Information

  • Current Model: GPT-5.2 (released December 11, 2025)
  • Model Name: gpt-5.2-2025-12-11

Code Simplicity

  • Use the simplest code possible
  • Minimize package dependencies, only use what's absolutely necessary
  • Avoid advanced Python features, no list comprehensions unless specifically taught, no complex lambda functions
  • Break down complex operations into simple, readable steps
  • Add clear comments explaining what code does
  • Use blank lines to separate logical sections
  • Keep lines under 88 characters when possible

Variable Naming

  • Use descriptive names: generated_images not img or x
  • Use snake_case: api_key, NOT apiKey NOT ApiKey
  • Make names meaningful: user_prompts, NOT data1

Function Design

  • Each function should do ONE thing
  • Write modular code, break complex tasks into small, reusable functions
  • Always include NumPy-style docstrings explaining purpose, parameters, and returns
  • Use type hints to make code clearer
  • Include default parameter values where appropriate

API Key Management

CRITICAL: Never hardcode API keys in code! Reminder users of this when necessary.

You will:

  1. Create a .env file in project root
  2. Add: OPENAI_API_KEY=your-actual-key-here
  3. Add .env to .gitignore
  4. Use python-dotenv to load it
  5. Prompt the user to create the API key and paste the key themselves to the .env file before proceeding.

Correct example of using API key:

python
1import os 2from dotenv import load_dotenv 3 4# Load environment variables from .env file 5load_dotenv() 6api_key = os.getenv('OPENAI_API_KEY') 7 8if not api_key: 9 raise ValueError("OPENAI_API_KEY not found in environment variables")

Error Handling

Always wrap API calls and file operations in try-except blocks:

python
1try: 2 response = client.chat.completions.create( 3 model="gpt-5.2-2025-12-11", 4 messages=[ 5 {"role": "system", "content": "You are a helpful assistant."}, 6 {"role": "user", "content": "Hello!"} 7 ] 8 ) 9 result = response.choices[0].message.content 10except Exception as e: 11 print(f"Error calling API: {e}") 12 result = None

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is api-rules?

Perfect for Python Analysis Agents specializing in LLM-as-a-judge evaluation tasks. The api-rules skill provides a Python programming assistant for LLM evaluation tasks, utilizing OpenAI API and libraries like Pandas and NumPy. It benefits developers working with LLMs.

How do I install api-rules?

Run the command: npx killer-skills add skysheng7/tech_summary_llm_arena/api-rules. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for api-rules?

Key use cases include: Automating LLM performance benchmarking, Generating evaluation datasets from PDF attachments, Processing results with Pandas data analysis, Creating modular evaluation scripts for multiple models.

Which IDEs are compatible with api-rules?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for api-rules?

Requires OpenAI API Key. Depends on Pandas and NumPy libraries. Python-specific implementation only.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add skysheng7/tech_summary_llm_arena/api-rules. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use api-rules immediately in the current project.

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