A strategic framework for optimizing context window usage through partitioning and automated checkpointing for long-running agent sessions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context-budgeting-v2
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-v2 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?
The Context Budgeting skill is a sophisticated resource management layer designed for Openclaw Skills. It addresses the inherent limitations of finite LLM context windows by implementing a strict information lifecycle and partitioning strategy. By organizing available RAM into dedicated sectors for goals, history, decisions, and knowledge, it ensures that critical task-related data remains persistent even during high-density operations.
This skill is essential for developers and users of Openclaw Skills who require high consistency in long-running sessions where memory loss or token-heavy latency becomes a bottleneck. It transforms how the agent perceives and retains information, moving from a first-in-first-out approach to a value-based retention model.
Context Budgeting Use Cases
- When session context usage exceeds 80% of the maximum allowed limit.
- Scenarios where the agent experiences performance degradation or memory loss after automatic compaction.
- Reducing token costs and latency during high-intensity, long-running autonomous tasks.
- Managing complex decision-making logs that require periodic summarization without losing the current execution state.
How Context Budgeting Works
- Monitor the system status to track context window utilization in real-time.
- Apply Information Partitioning to divide the window into specific segments: Objective (10%), Short-term History (40%), Decision Logs (20%), and Background Knowledge (20%).
- Generate a mandatory pre-compression checkpoint in the HOT_MEMORY.md file before any data cleanup.
- Trigger the physical garbage collection and compaction process using the provided automation scripts.
- Re-evaluate context levels every 30 minutes via the integrated Heartbeat mechanism.
Context Budgeting Setup
To integrate this skill into your Openclaw Skills environment, ensure the automation script is executable and the required directory structure exists:
# Navigate to the skill directory
cd skills/context-budgeting/scripts/
# Make the GC script executable
chmod +x gc_and_checkpoint.sh
# Initialize the hot memory file if it does not exist
mkdir -p ../../../memory/hot/
touch ../../../memory/hot/HOT_MEMORY.md
Context Budgeting Data Schema & Taxonomy
The skill organizes data through a tiered metadata taxonomy to ensure information integrity during compaction cycles:
| Data Component | Location | Responsibility |
|---|---|---|
| Hot Memory | memory/hot/HOT_MEMORY.md |
Stores current task status, significant choices, and immediate next steps. |
| GC Script | scripts/gc_and_checkpoint.sh |
Handles the physical cleanup of raw data and session synchronization. |
| Decision Logs | Session Context | Summarized outcomes of past steps used for logical continuity. |
| Background | MEMORY.md |
Long-term high-relevance snippets retrieved as needed. |
Context Budgeting Advanced Features
- Automated Heartbeat Integration for scheduled 30-minute garbage collection and health checks.
- Pre-compression Checkpointing to safeguard critical logic before LLM-based summarization occurs.
- Dynamic Partitioning Model that adjusts token allocation based on the specific phase of the task lifecycle.
- Automated Raw Data Purge which removes multi-megabyte JSON outputs once essential summaries are extracted to save space.
SKILL.md
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