A sophisticated memory and growth operating system designed to give AI agents consistent layered memory and self-driven evolution patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install sagemaker
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 sagemaker 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 SageMaker?
SageMaker serves as a comprehensive memory architecture for AI agents, functioning as a growth operating system. It enables agents to maintain state across short, mid, and long-term horizons, ensuring that lessons learned in one session are synthesized into permanent knowledge. By implementing a structured feedback loop, SageMaker allows any agent equipped with these Openclaw Skills to perform self-calibration, reducing recurring errors and improving long-term collaboration quality.
SageMaker Use Cases
- Persistent agent learning across multiple sessions or complex tasks.
- Structured self-improvement based on validated strengths and failure modes.
- High-complexity task management requiring preflight checklists and post-task reflections.
- Cross-agent knowledge transfer via standardized memory layers.
How SageMaker Works
- Capture raw evidence and experiences in short-term daily logs.
- Synthesize mid-term conclusions from recurring short-term data through a promotion loop.
- Promote stable constraints and methods to long-term memory or the knowledge base based on explicit evidence and confidence levels.
- Calibrate the internal self-model to reflect updated strengths, weaknesses, and growth themes.
- Execute a dual-gate logic model to ensure daily and weekly memory updates are completed and validated before task execution.
SageMaker Setup
To install this specific skill from the Openclaw Skills collection, create the required directory structure or use the bundled PowerShell installer:
powershell -File "skills/SageMaker/scripts/install.ps1"
For advanced recovery paths, you can apply the optional heartbeat template:
powershell -File "skills/SageMaker/scripts/install.ps1" -ApplyHeartbeatTemplate
Required directory artifacts include memory/short_term/, memory/mid_term/MEMORY.md, memory/long_term/MEMORY.md, memory/knowledge.md, and memory/check_memory.json.
SageMaker Data Schema & Taxonomy
SageMaker organizes data through a hierarchical taxonomy to ensure information integrity:
| File Path | Purpose | Update Logic |
|---|---|---|
memory/short_term/YYYY-MM-DD.md |
Raw daily evidence | Pruned/Promoted daily |
memory/mid_term/MEMORY.md |
Reusable conclusions | Promoted weekly |
memory/long_term/MEMORY.md |
Stable collaboration constraints | High-confidence only |
memory/knowledge.md |
Transferable methods (if X then Y) | Cross-scenario utility |
self-model.md |
Performance themes and uncertainties | Validated growth items |
memory/check_memory.json |
Gate state management | Binary 1/0 status |
SageMaker Advanced Features
- Dual-gate synchronization model ensuring no tasks proceed without pending memory updates.
- Mandatory Entry Quality Contract requiring reason, evidence, and confidence metrics for all promoted memory.
- Task-coupled preflight checklists for medium and high complexity workflows.
- Proposal-first safety protocol for modifying core governance and identity files.
- Automated self-model calibration based on recurring failure modes and growth themes.
SKILL.md
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