sales-ads-helper

What This Skill Does

The sales-ads-helper is a sophisticated sales enablement agent designed to bridge the gap between technical ads performance and commercial closing. It acts as a specialized layer that ingests raw prospect data—such as website URLs and requirements—and transforms them into high-converting sales proposals, detailed ROI projections, and strategic persuasion frameworks. By integrating CRM stage signals, the skill helps sales teams understand not just what to sell, but how to close, providing a clear forecast of deal probability and cash collection cycles.

Installation

To integrate this tool into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/danyangliu-sandwichlab/sales-ads-helper

Use Cases

• Proposal Drafting: Rapidly convert a prospect's URL and pain points into a tailored ad service proposal spanning Meta, Google, TikTok, and Amazon platforms.
• ROI Modeling: Generate channel-specific ROI estimates with auditable assumptions to manage client expectations and demonstrate value.
• Sales Strategy: Determine the optimal closing approach based on prospect urgency, budget constraints, and current CRM stage data.
• Reporting: Maintain healthy pipelines by automating daily follow-up summaries and retrospective performance reviews.

Example Prompts

1. "Analyze this URL: [Link] and create a Meta/Google Ads proposal for a client needing a 3x ROAS increase. Assume a $10k monthly ad spend."
2. "I'm stuck with a prospect who is hesitant about our pricing. Here is the CRM stage data: {stage: 'negotiation', urgency: 'low', sentiment: 'price-sensitive'}. How should I handle the next follow-up?"
3. "Draft a retrospective report for my Shopify client based on their last 30 days of performance: $50k spend, $120k revenue, $12k management fee."

Tips & Limitations

• Transparency: The tool explicitly separates sales narratives from delivery guarantees. Ensure your human team reviews all proposals before sending them to clients to prevent over-promising.
• Data Integrity: Always input accurate CRM stage data. The close probability score is only as reliable as the inputs provided.
• Assumption Driven: ROI models are built on user-defined assumptions. Use the tool as a strategic guide rather than a financial contract.