meeting-notes

What This Skill Does

The meeting-notes skill serves as an intelligent assistant designed to streamline your professional workflow by capturing, synthesizing, and organizing information from meetings. It eliminates the friction of manual note-taking by providing a structured framework to record key discussion points, core decisions, and individual contributions. Beyond mere transcription, this skill intelligently parses your meeting content to isolate and extract specific actionable tasks, ensuring that no requirement or follow-up item falls through the cracks. It also features a robust retrieval system, allowing users to query historical records and cross-reference outcomes with existing calendar events.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/huangjingzhi07/meeting-notes-oc Ensure that your OpenClaw instance has the necessary permissions for file storage, as the skill creates local JSON databases for persistence.

Use Cases

This skill is highly versatile for professionals working in dynamic environments. Common use cases include: tracking cross-functional project updates, documenting design decisions in engineering scrums, managing client feedback loops during discovery calls, and auditing historical meeting trends to prepare for quarterly business reviews.

Example Prompts

1. "记录今天下午的项目评审会议，重点关注API接口定义的调整。"
2. "查看上周技术沟通会中分配给我的所有待办事项。"
3. "搜索并总结所有关于数据库迁移策略的历史会议记录。"

Tips & Limitations

To maximize the utility of the meeting-notes skill, ensure your input descriptions are specific and provide sufficient context. The quality of extracted action items is directly proportional to the clarity of the input provided. Limitations to consider: the skill relies on locally stored JSON files, so verify that your environment supports file-write operations. It does not currently support real-time audio-to-text streaming, so it works best when provided with post-meeting summaries or transcribed text inputs. Always review extracted tasks for accuracy, as automatic parsing may occasionally misinterpret nuance in complex verbal exchanges.