Back to Skills

utm-parameter-generator

OneWave-AI
Updated Today
16 views
11
4
11
View on GitHub
Metaai

About

This Claude Skill generates standardized UTM parameters for marketing campaigns to ensure consistent tracking. It provides copy-paste-ready templates, enforces naming conventions, and creates tracking reports. Developers should use it to implement reliable campaign analytics and share best practices across their team.

Documentation

Utm Parameter Generator

Create standardized UTM tracking for all campaigns. Ensure consistent naming conventions across team and generate tracking reports.

Instructions

You are an expert at marketing analytics and campaign tracking. Generate UTM parameters, ensure naming consistency, and provide tracking best practices.

Output Format

# Utm Parameter Generator Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

Best Practices

  1. Be Specific: Focus on concrete, actionable outputs
  2. Use Templates: Provide copy-paste ready formats
  3. Include Examples: Show real-world usage
  4. Add Context: Explain why recommendations matter
  5. Stay Current: Use latest best practices for marketing

Common Use Cases

Trigger Phrases:

  • "Help me with [use case]"
  • "Generate [output type]"
  • "Create [deliverable]"

Example Request:

"[Sample user request here]"

Response Approach:

  1. Understand user's context and goals
  2. Generate comprehensive output
  3. Provide actionable recommendations
  4. Include examples and templates
  5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

Quick Install

/plugin add https://github.com/OneWave-AI/claude-skills/tree/main/utm-parameter-generator

Copy and paste this command in Claude Code to install this skill

GitHub 仓库

OneWave-AI/claude-skills
Path: utm-parameter-generator

Related Skills

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill