yf-memo

# 🗂️ 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 2. **Decide if task tracking is appropriate** 3. **Use the appropriate script functions** 4. **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 ``` ~/.openclaw/workspace/ ├── pending-items.md # Auto-numbered pending tasks ├── completed-items.md # Timestamped completed tasks └── skills/yf-memo/scripts/ ├── memo-helper.sh # Core management functions └── daily-summary.sh # Automatic daily summaries ``` ### 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)** ```bash # Find skill directory by name (yf-memo) SKILL_DIR=$(find ~/.openclaw/skills ~/.openclaw/workspace/skills -name "yf-memo" -type d 2>/dev/null | tail -1) MEMO_SCRIPT="$SKILL_DIR/scripts/memo-helper.sh" sh "$MEMO_SCRIPT" add "task description" ``` **Method 2: Consistent Relative Path Pattern** If the AI assistant is already in the OpenClaw workspace context: ```bash sh ./skills/yf-memo/scripts/memo-helper.sh add "task description" ``` **Method 3: Use Environment Variable Setup** First, set up these environment variables in shell profile: ```bash # Add to .zshrc or .bashrc export YFMEMO_SKILL_DIR="$HOME/.openclaw/skills/yf-memo" export YFMEMO_SCRIPT="$YFMEMO_SKILL_DIR/scripts/memo-helper.sh" ``` Then use: ```bash sh "$YFMEMO_SCRIPT" add "task description" ``` **Available Functions** (using dynamic location): - Add new task: `sh "$MEMO_SCRIPT" add "item description"` - Mark task X as complete: `sh "$MEMO_SCRIPT" complete-number X` - Mark matching task as complete: `sh "$MEMO_SCRIPT" complete-content "partial text"` - Display pending tasks: `sh "$MEMO_SCRIPT" show-todos` - Display completed tasks: `sh "$MEMO_SCRIPT" show-done` **daily-summary.sh** - For scheduled daily summaries (located in same scripts directory) ## AI Assistant Guidelines ### Memory Delegation Flow 1. Recognize user wants to delegate memory of a task 2. Extract the task description from natural language 3. Execute: `sh "$MEMO_SCRIPT" add "task description"` 4. Respond with confirmation including assigned number **Example interaction**: ``` User: "I should remember to call the dentist tomorrow" AI Reasoning: User wants me to remember this task for them AI Action: sh "$MEMO_SCRIPT" add "call the dentist tomorrow" AI Response: "✅ I've added that as item 3: call the dentist tomorrow" ``` ### Status Inquiry Flow 1. Recognize user wants to see pending tasks 2. Execute: `sh "$MEMO_SCRIPT" show-todos` 3. Present results conversationally **Example interaction**: ``` User: "What's still on my todo list?" AI Reasoning: User wants to see pending tasks AI Action: sh "$MEMO_SCRIPT" show-todos AI Response: Cool, I'll check... <shows results> ``` ### Progress Tracking Flow 1. Recognize user is reporting task completion 2. Determine if completion refers to item number or content 3. Execute appropriate completion command 4. Confirm the completion **Example interaction**: ``` User: "I finished that report" AI Reasoning: User saying a task is done AI Action: sh "$MEMO_SCRIPT" complete-content "report" AI Response: "✅ Marked that as complete!" ``` ## 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 ``` User: "Oh, I need to email Sarah about the project update" AI: "✅ I'll track that for you. Added as item 2: email Sarah about project update" ``` ### Conversation 2: Checking Status ``` User: "What's still outstanding?" AI: "Let me check... You have 2 pending items..." ``` ### Conversation 3: Mixed Language ``` User: "我刚刚完成了第三件事" AI: "✅ Marked item 3 as completed!" ``` ### Conversation 4: Implicit Request ``` User: "有哪几件事需要我今天处理？" AI: "Here's what's on your pending list..." ``` ## 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.*