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日常小助手 MCP Server

该 Skill 是个人日常任务管理 MCP Server,基于本地 Markdown 任务文件,提供下一任务推荐、今日任务读取、未完成任务继承、逾期检测、日终复盘和任务拆分提示;确定性的解析、排序与统计由 Python 完成,AI 仅在需要创造性建议时介入。

asuranale
asuranale
热度 48.71
最近更新 2026-05-11 17:38:25
分类:效率依赖:MCP 服务 / 读写文件 / 代码执行应用场景:任务管理 / 日程安排 / 工作流 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add daily-assistant
Metadata
name
daily-assistant
description
AI-powered daily task management MCP Server — recommend next task, inherit uncompleted todos, detect overdue, generate reviews. Deterministic ops in code (zero tokens), AI only when needed.
version
2.1.0
metadata
{"openclaw":{"requires":{"bins":["python3"]},"emoji":"📅","homepage":"https://github.com/AsuraNale/daily-assistant-mcp"}}

Daily Assistant MCP Server

A personal task management MCP Server. Deterministic operations (parsing, sorting, statistics) run in Python with zero token cost — AI only steps in when creativity is needed.

Setup

git clone https://github.com/AsuraNale/daily-assistant-mcp.git
cd daily-assistant-mcp
python3 src/setup.py --auto   # Windows: py src/setup.py --auto

The setup wizard creates a .venv, installs dependencies, sets up your data directory, and auto-configures your AI editor. No manual pip install or config editing needed.

Tools

Tool What it does
recommend_next Recommends the most important task to work on next
get_today Reads today's daily task file
inherit_tasks Carries over uncompleted tasks from yesterday
check_overdue Detects overdue task files
generate_review Generates end-of-day review with completion stats
scan_split Flags tasks >80min or missing time estimates

Resources

Resource Content
daily://today Today's task file
daily://dashboard Dashboard overview
daily://history 7-day completion statistics

Task Format

- [ ] Task description ⏱️45min 📅 2026-03-30 ⏫

Markers: ⏱️ = time estimate, 📅 = deadline, ⏫ = highest priority, 🔼 = high, 🔽 = low.

Design Philosophy

  • Zero-token deterministic ops: Parsing, sorting, stats run in Python code
  • AI only when needed: Task splitting, creative advice, context-aware suggestions
  • Platform-independent: Windows, macOS, Linux. No Obsidian dependency
  • Simple data: Plain Markdown files, edit anywhere