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Install in OpenClaw
/install caring-memory
Description
AI task reminder using priority, Ebbinghaus intervals, gamification, and active time tracking for effective task management.
README (SKILL.md)
Caring Memory Skill
🧠 AI-powered task reminder system with Ebbinghaus forgetting curve + gamification + active time learning
Overview
A smart task management system that:
- Priority-based reminders: urgent/high/medium/low with auto-upgrade near deadlines
- Ebbinghaus curve: reminds at 1h, 24h, 4d, 7d, 15d intervals
- Gamification: XP, levels, streaks, achievements
- Active time learning: tracks when you're most responsive
Usage
Add a task
python3 caring_memory.py add "Task title" [priority] [deadline]
# priority: urgent/high/medium/low
# deadline: ISO format e.g. "2026-04-10T18:00:00"
Complete a task
python3 caring_memory.py complete \x3Cid>
Cancel a task
python3 caring_memory.py cancel \x3Cid>
List pending tasks
python3 caring_memory.py list
Generate reminder summary (for cron)
python3 caring_memory.py remind
View stats
python3 caring_memory.py stats
Record chat activity
python3 caring_memory.py chat
OpenClaw Integration
Recommended Cron Setup
Morning reminder (08:00):
Task: python3 skills/caring-memory-skill/caring_memory.py remind
Schedule: 0 8 * * *
Midday check (12:00):
Task: python3 skills/caring-memory-skill/caring_memory.py remind
Schedule: 0 12 * * *
Evening review (18:00+21:00):
Task: python3 skills/caring-memory-skill/caring_memory.py remind
Schedule: 0 18,21 * * *
Agent Integration
- Session start: Call
chatto record active time - User mentions tasks: Auto-call
add - User says "done": Call
complete - Heartbeat check: Call
remindfor summary
Priority Auto-Upgrade
| Time to deadline | Upgrade |
|---|---|
| \x3C 24h | high → urgent |
| \x3C 48h | medium → high |
| \x3C 96h | low → medium |
Gamification
| Action | XP |
|---|---|
| Complete task | 10 × priority_multiplier |
| Daily streak | +5 bonus |
| Level up | Every 100 XP |
Trigger Words
- "这很重要" → Add as high priority
- "记住这个" → Add task
- "完成了" / "搞定" → Complete task
- "待办" / "任务" → List tasks
Usage Guidance
This skill is internally consistent and implements a local, file-based reminder system. Before installing: 1) Review the full caring_memory.py file (the provided snippet was truncated) to confirm there are no network calls or hidden logging/exfiltration in the remainder of the code. 2) Be aware that task data and activity logs are stored as plain JSON files inside the skill folder (not encrypted); do not store sensitive secrets or personally-identifying data there. 3) If you enable the agent integration (auto-calling add/complete/remind), understand the agent may invoke the skill automatically when the configured trigger words appear. If you want tighter control, run reminders only via cron or require manual invocation. 4) Run the script in a limited environment or sandbox if you want to audit behavior first.
Capability Analysis
Type: OpenClaw Skill
Name: caring-memory
Version: 2.0.0
The skill is a legitimate task management system that implements the Ebbinghaus forgetting curve and gamification. The Python script (caring_memory.py) operates entirely on local JSON files for data persistence and lacks any high-risk behaviors such as network requests, shell execution, or access to sensitive system directories. The instructions in SKILL.md are strictly aligned with the stated purpose of automating task reminders and updates for the user.
Capability Assessment
Purpose & Capability
Name/description (task reminder with spaced repetition, gamification, active-time learning) match the files and code: the Python script implements task CRUD, reminder generation, activity logging, priority upgrades and XP rules. No unrelated credentials, binaries, or external services are requested.
Instruction Scope
SKILL.md instructs the agent and cron to run local commands (python3 caring_memory.py ...). It also suggests agent integration patterns (call chat/add/complete/remind on session start or user mentions) — this is within the skill's purpose but grants the agent discretion to invoke the skill automatically when those triggers occur. The instructions and code operate on files in the skill directory only.
Install Mechanism
No install spec (instruction-only / script-based) and no downloads. The skill runs as a local Python script and ships package.json and config.json; nothing is fetched from external URLs during install.
Credentials
The skill declares no required environment variables, credentials, or system config paths. The code reads/writes local JSON files (tasks.json, activity_log.json, stats.json, config.json) in the skill directory — this is proportional to a local task manager.
Persistence & Privilege
always is false and disable-model-invocation is false (normal). The skill stores state in local JSON files in its directory; it does not request elevated platform privileges or modify other skills' configuration.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install caring-memory - After installation, invoke the skill by name or use
/caring-memory - Provide required inputs per the skill's parameter spec and get structured output
Version History
v2.0.0
v2.0: Ebbinghaus curve + gamification + active time learning
Metadata
Frequently Asked Questions
What is Caring Memory?
AI task reminder using priority, Ebbinghaus intervals, gamification, and active time tracking for effective task management. It is an AI Agent Skill for Claude Code / OpenClaw, with 99 downloads so far.
How do I install Caring Memory?
Run "/install caring-memory" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Caring Memory free?
Yes, Caring Memory is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Caring Memory support?
Caring Memory is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Caring Memory?
It is built and maintained by wukai8289 (@wukai8289); the current version is v2.0.0.
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