context-budgeting
Manage and optimize OpenClaw context window usage via partitioning, pre-compression checkpointing, and information lifecycle management. Use when the session context is near its limit (>80%), when the agent experiences "memory loss" after compaction, or when aiming to reduce token costs and latency for long-running tasks.
Why use this skill?
Optimize OpenClaw agent memory and reduce token costs with the context-budgeting skill. Implement automated partitioning and checkpointing for long-term task persistence.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/sarielwang93/context-budgetingWhat This Skill Does
The context-budgeting skill provides a robust architectural framework for managing the finite RAM (context window) of an OpenClaw agent. As agents work on long-running tasks, token accumulation can lead to degradation in reasoning or performance. This skill implements a systematic lifecycle management strategy, utilizing information partitioning, pre-compression checkpointing, and automatic garbage collection. By segmenting context into specific zones—Objectives, Short-term History, Decision Logs, and Background Knowledge—the agent can effectively prioritize information. The mandatory pre-compression checkpointing ensures that no task state is lost during memory consolidation, allowing the agent to maintain high-fidelity task persistence even as older dialogue is purged.
Installation
To integrate this skill, ensure you have the OpenClaw CLI configured, then execute the following command in your terminal:
clawhub install openclaw/skills/skills/sarielwang93/context-budgeting
This will provision the necessary scripts in your local skills/ directory and hook the gc_and_checkpoint.sh utility into your agent's automation layer.
Use Cases
This skill is essential for:
- Long-form research projects where documentation spans thousands of tokens.
- Complex software development tasks requiring memory of past trial-and-error iterations.
- Maintaining persistent agent personality and instructions over multiple days of interaction.
- Reducing infrastructure costs by aggressively managing the token-to-utility ratio in high-frequency sessions.
Example Prompts
- "Our current context usage is at 85%. Please initiate the pre-compression checkpointing process and clean up the old logs."
- "I'm noticing memory loss regarding our previous technical decisions. Can you run the gc_and_checkpoint script and reorganize the context partition?"
- "Summarize the current task status into HOT_MEMORY.md and trigger a garbage collection cycle to keep the session responsive."
Tips & Limitations
- Manual Intervention: Always verify the content of
HOT_MEMORY.mdbefore triggering the script, as the summary is the primary source of truth for the next session. - Automation: Leverage the Heartbeat integration; if your agent handles significant data flows, adjust the heartbeat interval to perform GC more frequently.
- Storage: Ensure you have write permissions for the
memory/hot/andscripts/directories, as the skill requires direct file access to perform effective maintenance.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-sarielwang93-context-budgeting": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-write, file-read, code-execution
Related Skills
memory-tiering
Automated multi-tiered memory management (HOT, WARM, COLD). Use this skill to organize, prune, and archive context during memory operations or compactions.
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Model Guard
Skill by sarielwang93