🗂️ Personal Memo System Skill
A personal task tracking system integrated with OpenClaw workspace. The AI assistant uses this skill when it recognizes the user wants to manage tasks, reminders, or to-dos through natural conversation.
Core Principle: Intent-Based Activation
DO NOT implement fixed command patterns like specific phrase matching to specific actions.
Avoid binding exact user phrases to script calls.
INSTEAD the AI should:
- 1. Understand user intent through natural language
- Decide if task tracking is appropriate
- Use the appropriate script functions
- Respond conversationally
When to Consider Using This Skill
The AI assistant should consider using this skill when the user's request falls into these intent categories:
Intent Category: Memory Delegation
The user wants the assistant to remember or track something for them.
- - "I need to remember to submit the report tomorrow"
- "Can you note that I have a meeting at 3pm?"
- "Remind me to buy groceries after work"
AI Reasoning: User is asking me to serve as a memory aid for future actions.
Intent Category: Status Inquiry
The user wants to know what tasks are pending or need attention.
- - "What do I have on my plate right now?"
- "Show me what's left to do today"
- "Are there any outstanding tasks I should handle?"
AI Reasoning: User is seeking a summary of pending responsibilities.
Intent Category: Progress Tracking
The user indicates something has been completed or finished.
- - "I finished writing that document"
- "The meeting with the client is done"
- "Item number 2 on my list is complete"
AI Reasoning: User is providing status update that should be recorded.
Intent Category: Accomplishment Review
The user wants to review what has been accomplished.
- - "What have I completed so far this week?"
- "Show me a summary of finished tasks"
- "Let me see what I've gotten done today"
AI Reasoning: User wants retrospective view of completed work.
System Integration
File Structure
CODEBLOCK0
Script Functions
Finding the Script Location:
Since skill installation paths vary per user, use these methods to locate the scripts:
Method 1: Dynamic Path Discovery (Recommended)
CODEBLOCK1
Method 2: Consistent Relative Path Pattern
If the AI assistant is already in the OpenClaw workspace context:
CODEBLOCK2
Method 3: Use Environment Variable Setup
First, set up these environment variables in shell profile:
# Add to .zshrc or .bashrc
export YFMEMO_SKILL_DIR="$HOME/.openclaw/skills/yf-memo"
export YFMEMO_SCRIPT="$YFMEMO_SKILL_DIR/scripts/memo-helper.sh"
Then use:
CODEBLOCK4
Available Functions (using dynamic location):
- - Add new task: INLINECODE0
- Mark task X as complete: INLINECODE1
- Mark matching task as complete: INLINECODE2
- Display pending tasks: INLINECODE3
- Display completed tasks: INLINECODE4
daily-summary.sh - For scheduled daily summaries (located in same scripts directory)
AI Assistant Guidelines
Memory Delegation Flow
- 1. Recognize user wants to delegate memory of a task
- Extract the task description from natural language
- Execute: INLINECODE5
- Respond with confirmation including assigned number
Example interaction:
CODEBLOCK5
Status Inquiry Flow
- 1. Recognize user wants to see pending tasks
- Execute: INLINECODE6
- Present results conversationally
Example interaction:
CODEBLOCK6
Progress Tracking Flow
- 1. Recognize user is reporting task completion
- Determine if completion refers to item number or content
- Execute appropriate completion command
- Confirm the completion
Example interaction:
CODEBLOCK7
Important Notes for AI
Language Flexibility
- - Support both Chinese and English expressions
- Don't fixate on exact phrases
- Focus on intent over specific wording
User Experience
- - Always confirm actions clearly
- Include the item number in responses when applicable
- Keep responses natural, not robotic
Error Handling
- - If a task isn't found, explain clearly
- Offer to show current list if there's confusion
- Suggest alternative actions when appropriate
Sample Conversations
Conversation 1: Natural Task Addition
CODEBLOCK8
Conversation 2: Checking Status
CODEBLOCK9
Conversation 3: Mixed Language
CODEBLOCK10
Conversation 4: Implicit Request
CODEBLOCK11
Development Philosophy
This skill embodies the principle that AI assistants should:
- - Understand intent, not just parse commands
- Adapt to natural human conversation patterns
- Provide value through contextual understanding
- Maintain conversation flow organically
The system exists to support the assistant in helping the user manage tasks, not to enforce rigid interaction patterns.
This skill enables natural task management through conversational AI.
🗂️ 个人备忘录系统技能
一个与OpenClaw工作区集成的个人任务跟踪系统。当AI助手识别到用户希望通过自然对话管理任务、提醒或待办事项时,会使用此技能。
核心原则:基于意图的激活
不要实施固定的命令模式,例如将特定短语匹配到特定操作。
避免将用户的确切短语与脚本调用绑定。
相反,AI应该:
- 1. 通过自然语言理解用户意图
- 判断任务跟踪是否合适
- 使用适当的脚本函数
- 以对话方式回应
何时考虑使用此技能
当用户的请求属于以下意图类别时,AI助手应考虑使用此技能:
意图类别:记忆委托
用户希望助手为他们记住或跟踪某些事情。
- - 我需要记得明天提交报告
- 你能记下我下午3点有个会议吗?
- 提醒我下班后买杂货
AI推理:用户要求我作为未来行动的辅助记忆工具。
意图类别:状态查询
用户想知道有哪些任务待处理或需要注意。
- - 我现在手头有什么任务?
- 告诉我今天还有什么要做
- 有没有什么未完成的任务需要我处理?
AI推理:用户正在寻求待办责任的摘要。
意图类别:进度跟踪
用户表示某件事已经完成或结束。
- - 我写完了那份文档
- 与客户的会议结束了
- 我清单上的第二项完成了
AI推理:用户提供应被记录的状态更新。
意图类别:成果回顾
用户想要回顾已完成的内容。
- - 这周我完成了什么?
- 给我看看已完成任务的摘要
- 让我看看今天完成了什么
AI推理:用户希望回顾已完成工作的回顾视图。
系统集成
文件结构
~/.openclaw/workspace/
├── pending-items.md # 自动编号的待办任务
├── completed-items.md # 带时间戳的已完成任务
└── skills/yf-memo/scripts/
├── memo-helper.sh # 核心管理功能
└── daily-summary.sh # 自动每日摘要
脚本函数
查找脚本位置:
由于技能安装路径因用户而异,请使用以下方法定位脚本:
方法1:动态路径发现(推荐)
bash
按名称查找技能目录(yf-memo)
SKILL_DIR=$(find ~/.openclaw/skills ~/.openclaw/workspace/skills -name yf-memo -type d 2>/dev/null | tail -1)
MEMO
SCRIPT=$SKILLDIR/scripts/memo-helper.sh
sh $MEMO_SCRIPT add 任务描述
方法2:一致的相对路径模式
如果AI助手已经在OpenClaw工作区上下文中:
bash
sh ./skills/yf-memo/scripts/memo-helper.sh add 任务描述
方法3:使用环境变量设置
首先,在shell配置文件中设置这些环境变量:
bash
添加到.zshrc或.bashrc
export YFMEMO
SKILLDIR=$HOME/.openclaw/skills/yf-memo
export YFMEMO
SCRIPT=$YFMEMOSKILL_DIR/scripts/memo-helper.sh
然后使用:
bash
sh $YFMEMO_SCRIPT add 任务描述
可用函数(使用动态位置):
- - 添加新任务:sh $MEMOSCRIPT add 项目描述
- 将任务X标记为完成:sh $MEMOSCRIPT complete-number X
- 将匹配的任务标记为完成:sh $MEMOSCRIPT complete-content 部分文本
- 显示待办任务:sh $MEMOSCRIPT show-todos
- 显示已完成任务:sh $MEMO_SCRIPT show-done
daily-summary.sh - 用于定时每日摘要(位于同一脚本目录)
AI助手指南
记忆委托流程
- 1. 识别用户想要委托记忆某个任务
- 从自然语言中提取任务描述
- 执行:sh $MEMO_SCRIPT add 任务描述
- 回复确认,包括分配的编号
示例交互:
用户:我应该记得明天给牙医打电话
AI推理:用户希望我为他们记住这个任务
AI操作:sh $MEMO_SCRIPT add 明天给牙医打电话
AI回复:✅ 我已将其添加为第3项:明天给牙医打电话
状态查询流程
- 1. 识别用户想要查看待办任务
- 执行:sh $MEMO_SCRIPT show-todos
- 以对话方式呈现结果
示例交互:
用户:我的待办清单上还有什么?
AI推理:用户想要查看待办任务
AI操作:sh $MEMO_SCRIPT show-todos
AI回复:好的,我来查一下... <显示结果>
进度跟踪流程
- 1. 识别用户正在报告任务完成
- 判断完成是指项目编号还是内容
- 执行相应的完成命令
- 确认完成
示例交互:
用户:我完成了那份报告
AI推理:用户表示任务已完成
AI操作:sh $MEMO_SCRIPT complete-content 报告
AI回复:✅ 已标记为完成!
AI重要注意事项
语言灵活性
- - 支持中英文表达
- 不要拘泥于确切的短语
- 关注意图而非特定措辞
用户体验
- - 始终清晰地确认操作
- 在回复中尽可能包含项目编号
- 保持回复自然,不要机械
错误处理
- - 如果找不到任务,清晰解释
- 如有混淆,提供显示当前列表的选项
- 在适当时建议替代操作
示例对话
对话1:自然任务添加
用户:哦,我需要给Sarah发邮件讨论项目更新
AI:✅ 我会为你跟踪这个。已添加为第2项:给Sarah发邮件讨论项目更新
对话2:检查状态
用户:还有什么未完成的?
AI:让我查一下...你有2个待办项目...
对话3:混合语言
用户:我刚刚完成了第三件事
AI:✅ 已将第3项标记为完成!
对话4:隐含请求
用户:有哪几件事需要我今天处理?
AI:以下是你的待办清单...
开发理念
此技能体现了AI助手应遵循的原则:
- - 理解意图,而不仅仅是解析命令
- 适应自然的人类对话模式
- 通过上下文理解提供价值
- 有机地维持对话流程
该系统的存在是为了支持助手帮助用户管理任务,而不是强制执行僵化的交互模式。
此技能通过对话式AI实现自然的任务管理。