A systematic framework for managing and optimizing the finite context window of an AI agent to prevent memory loss and reduce operational costs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context-budgeting
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 context-budgeting 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 Context Budgeting?
This skill provides a robust framework for managing the finite context window (RAM) of an OpenClaw agent. By implementing strategic information partitioning and automated checkpointing, it ensures that critical project goals and decision logs remain preserved even during complex, long-running sessions. Utilizing Openclaw Skills for context budgeting allows developers to maintain high performance and lower latency by proactively managing the information lifecycle.
The primary purpose of this skill is to prevent the common "memory loss" issue encountered during context compaction. It enforces a strict pre-compression workflow where the agent summarizes its state before clearing raw data, ensuring that the most relevant insights are never lost to the truncation process.
Context Budgeting Use Cases
- Managing sessions when context window usage exceeds the 80% threshold.
- Preventing agent memory loss during automatic or manual compaction cycles.
- Reducing token costs and latency for complex, multi-step engineering tasks.
- Preserving core task instructions while clearing redundant short-term history logs.
How Context Budgeting Works
- The system monitors context usage through a heartbeat mechanism every 30 minutes.
- Information is partitioned into four specific categories: Objective, History, Decision Logs, and Background.
- A mandatory pre-compression checkpoint is generated, updating the persistent HOT_MEMORY.md file with status and next steps.
- The agent executes the gc_and_checkpoint.sh script to physically clean up the context window without restarting the session.
- Large raw data outputs are cleared once high-level summaries are extracted to keep the agent responsive.
Context Budgeting Setup
To implement this within your Openclaw Skills environment, ensure the script is correctly placed and executable.
# Navigate to your skill directory
cd skills/context-budgeting/scripts/
# Provide execution permissions to the GC script
chmod +x gc_and_checkpoint.sh
Configure your agent's heartbeat to monitor /status. When context usage exceeds 80%, the agent must be instructed to trigger the checkpointing procedure before running the script.
Context Budgeting Data Schema & Taxonomy
The skill organizes data using a prioritized partitioning model and specific markdown files for information persistence.
| Component | Allocation | Description |
|---|---|---|
| Objective | 10% | Core task instructions and active constraints. |
| Short-term History | 40% | Recent 5-10 turns of raw dialogue. |
| Decision Logs | 20% | Summarized outcomes and failed attempts. |
| Background | 20% | High-relevance snippets from MEMORY.md. |
| Checkpoint File | Metadata | memory/hot/HOT_MEMORY.md tracks current progress and next steps. |
Context Budgeting Advanced Features
- Automated Garbage Collection (GC) integration with system heartbeats.
- Strategic information partitioning to prevent the loss of critical mission constraints.
- Pre-compression checkpointing that ensures task continuity across compaction events.
- Enhanced resource management for long-running Openclaw Skills sessions.
SKILL.md
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