Cs

What This Skill Does

The Smart Summarizer (Cs) is a high-efficiency intelligence tool designed for OpenClaw users who struggle with information overload. It acts as an autonomous reading assistant, leveraging Large Language Models (LLMs) to ingest long-form content and distill it into actionable, structured bullet points. By utilizing both keyword detection and heuristic length-based triggers, the skill ensures that critical information is surfaced without manual intervention. Whether it is a lengthy email thread, a technical documentation dump, or a chaotic group discussion, the Cs skill extracts core arguments, decisions, and essential data points while discarding conversational noise.

Installation

To integrate the Smart Summarizer into your OpenClaw environment, execute the following command in your terminal. This will fetch the latest version directly from the OpenClaw repository:

openclaw skills install smart-summarizer

Alternatively, for direct source integration:

clawhub install openclaw/skills/skills/1466561686/cs

Ensure you have the latest version of the OpenClaw CLI to support automatic trigger parsing.

Use Cases

• Project Management: Quickly summarize lengthy meeting minutes into actionable checklists, ensuring team members never miss assigned tasks.
• Research & Development: Digest academic papers, technical documentation, or industry news into brief summaries, highlighting key methodologies and findings.
• Communication Efficiency: Process long email threads or Slack conversations to extract final decisions and pending items, saving time on context switching.

Example Prompts

1. "总结一下这份关于AI伦理的报告，我只需要核心观点和结论。"
2. [Pastes 5,000 words of internal memo text] "Could you summarize this for me? I need to understand the main action points for the engineering team."
3. "请把刚才的群聊内容概括为简短的摘要，特别是关于发布时间的讨论。"

Tips & Limitations

• Triggering: If you want to bypass the 100-character length check, use explicit keywords like 'summary' or '总结'.
• Model Versatility: Since the skill supports any connected LLM, output quality is dependent on your primary model choice. For complex logical extraction, we recommend models with strong reasoning capabilities.
• Data Privacy: As an agent skill, it processes content sent to it; always review sensitive data before passing it to any LLM-powered summary service.
• Handling Limitations: Extremely long texts (exceeding context window limits) may require breaking the input into smaller segments to maintain high summary fidelity.