An essential package for Openclaw Skills designed to automate conversation context management and handle large-scale task processing through intelligent splitting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install optimize-context
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 optimize-context 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 Optimizer & Task Processor?
The Context Optimizer & Task Processing package is a high-performance suite designed to solve the common issue of context window exhaustion in AI interactions. By implementing these Openclaw Skills, developers can ensure their agents remain responsive and accurate during long-running sessions. The package works by summarizing key information and clearing stale history, while simultaneously offering a task processor that breaks down monolithic requests into manageable, sequential steps.
Integrating these Openclaw Skills allows for a seamless development experience where 'prompt too large' errors are a thing of the past. The system acts as an intelligent buffer, maintaining the most relevant facts and instructions while discarding the overhead that typically slows down large language models during complex workflows.
Context Optimizer & Task Processor Use Cases
- Managing extremely long development sessions where message history would otherwise exceed token limits.
- Executing massive code refactoring or documentation tasks that require multi-stage processing.
- Preventing performance degradation in persistent chat agents by maintaining a lean context window.
- Automating the extraction of key project facts and requirements from long-form conversations.
How Context Optimizer & Task Processor Works
- The background monitor continuously tracks the message count and token usage within the current session.
- When configured thresholds are reached, the Context Optimizer identifies and extracts essential facts and key points.
- The system clears redundant message history while injecting the summarized facts back into the prompt context.
- For new large requests, the Task Processor analyzes the input to determine if it exceeds the safe processing limit.
- Large tasks are automatically partitioned into smaller subtasks which are processed sequentially to maintain logical flow and accuracy.
Context Optimizer & Task Processor Setup
To install these Openclaw Skills, follow these steps in your terminal:
- Extract the package to your workspace:
cd ~/.openclaw/workspace
tar -xzf /path/to/context-optimizer-skill.tar.gz
- Install the necessary node dependencies:
cd ~/.openclaw/workspace/skills/context-optimizer
npm install
- Restart your agent environment to enable the
/optimize-contextand/process-taskcommands.
Context Optimizer & Task Processor Data Schema & Taxonomy
These Openclaw Skills utilize several configuration files to manage their internal logic and thresholds:
| File | Description |
|---|---|
task_processing_config.json |
Global settings for token limits and task splitting behavior. |
systems/context-monitor-config.json |
Configuration for background monitoring and auto-trigger thresholds. |
commands/optimize-context.json |
Metadata and parameter definitions for the manual optimization command. |
commands/process-task.json |
Metadata for the automated task splitting command logic. |
Context Optimizer & Task Processor Advanced Features
- Continuous background monitoring that requires zero manual intervention for context health.
- Intelligent key point extraction to preserve conversation memory across history resets.
- Sequential subtask execution with automated context passing between segments.
- Fully configurable thresholds for message counts, token limits, and optimization timing.
SKILL.md
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