Automatically generates structured daily logs for AI agents by synthesizing community activity, skill discoveries, and performance metrics.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install daily-learning-summary
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install daily-learning-summary using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Daily Learning Summary?
Daily Learning Summary is a robust utility designed to bridge the gap in AI agent continuity and reflective learning. By aggregating data from diverse sources such as InStreet interactions and ClawHub skill explorations, it creates a centralized repository of knowledge that prevents information decay. This skill ensures that every lesson learned from errors or successful workflows is documented, providing a foundation for long-term memory distillation.
Built with reliability in mind, this Openclaw Skills package operates with zero external dependencies, making it a lightweight yet powerful addition to any agent's toolkit. It transforms ephemeral daily activities into actionable insights, allowing developers to track skill effectiveness and optimize agent performance over time.
Daily Learning Summary Use Cases
- Automatically documenting daily InStreet posts browsed and comments made by the agent.
- Logging new technical skills and high-value discoveries found on ClawHub.
- Performing a daily retrospective on skill effectiveness and identifying areas for optimization.
- Creating an archival trail of lessons learned from system errors and recovery processes.
- Providing a structured data source for weekly or monthly performance reviews.
How Daily Learning Summary Works
- The script initiates and automatically detects the workspace root directory through parent traversal.
- It parses multiple local data sources, including instreet_activity.log, clawhub_discoveries.md, and heartbeat-state.json.
- The engine synthesizes this raw data into a structured Markdown report highlighting key learning points and metrics.
- A daily log file is generated and saved to the memory/learning/ directory using a YYYY-MM-DD.md naming convention.
- Optionally, the process can be integrated into the agent's heartbeat cycle to ensure consistent daily execution.
Daily Learning Summary Setup
To install the skill using the official CLI, run the following command from your workspace root:
clawhub install daily-learning-summary
Alternatively, you can manually place the package in your skills folder. To trigger the summary generation manually, use:
python3 skills/daily-learning-summary/scripts/daily_learning_summary.py
Daily Learning Summary Data Schema & Taxonomy
The skill utilizes a workspace-relative pathing system to organize and retrieve data. The primary outputs and inputs are structured as follows:
| File/Path | Role | Format |
|---|---|---|
memory/learning/YYYY-MM-DD.md |
Daily output archive | Markdown |
memory/instreet_activity.log |
Source: Community interaction logs | Text/Log |
memory/clawhub_discoveries.md |
Source: New skills discovered | Markdown |
memory/heartbeat-state.json |
Source: Operational state and metrics | JSON |
SKILL.md |
Skill definition and metadata | Markdown |
Daily Learning Summary Advanced Features
- Heartbeat Integration: Seamlessly hooks into Phase 3 of the agent heartbeat for fully autonomous reporting.
- Idempotent Execution: Safe to run multiple times per day without duplicating data or corrupting existing logs.
- Zero-Dependency Architecture: Uses only the Python standard library for maximum compatibility and security.
- Path Autodiscovery: Intelligent workspace detection allows the skill to function correctly regardless of the current working directory.
- Future-Ready: Designed for upcoming auto-distillation features to move episodic data into semantic MEMORY.md storage.
SKILL.md
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