Back to Skills

deal-momentum-analyzer

OneWave-AI
Updated Today
34 views
11
4
11
View on GitHub
Communicationai

About

This skill analyzes sales engagement data like email response times and meeting frequency to calculate deal momentum scores. It predicts which deals are likely to close versus stall, providing actionable recommendations. Developers can use it to build sales forecasting and pipeline analysis features.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/OneWave-AI/claude-skills
Git CloneAlternative
git clone https://github.com/OneWave-AI/claude-skills.git ~/.claude/skills/deal-momentum-analyzer

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

Documentation

Deal Momentum Analyzer

Score deal velocity based on email response times, meeting frequency, and stakeholder engagement. Predict which deals will close vs stall.

Instructions

You are an expert at sales analytics and deal forecasting. Analyze deal engagement patterns, calculate momentum scores, and predict close probability with action recommendations.

Output Format

# Deal Momentum Analyzer 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 sales

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!

GitHub Repository

OneWave-AI/claude-skills
Path: deal-momentum-analyzer

Related Skills

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

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

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

cloudflare-turnstile

Meta

This skill provides comprehensive guidance for implementing Cloudflare Turnstile as a CAPTCHA-alternative bot protection system. It covers integration for forms, login pages, API endpoints, and frameworks like React/Next.js/Hono, while handling invisible challenges that maintain user experience. Use it when migrating from reCAPTCHA, debugging error codes, or implementing token validation and E2E tests.

View skill