context-optimizer

What This Skill Does

The Context Optimizer by atlaspa is a powerful utility designed for OpenClaw agents to drastically improve token efficiency. By utilizing advanced heuristics, this skill identifies and removes redundant, irrelevant, or non-critical information from a conversation or task context before it is sent to the LLM. It functions through a multi-layered approach: deduplication to remove repeated data, pruning to strip out whitespace or boilerplate, and intelligent summarization to maintain meaning while shrinking the word count. The primary value proposition is a consistent 40-60% reduction in token consumption, which directly translates to cost savings when operating agents at scale. Furthermore, the skill features a machine learning component that observes which specific types of context are prioritized in your unique workflow, ensuring the optimization becomes more effective over time.

Installation

To install this skill, use the primary OpenClaw CLI tool provided with your distribution. Ensure your environment is initialized before running the command:

clawhub install openclaw/skills/skills/atlaspa/openclaw-context-optimizer

Once installed, you can verify it by running claw optimize stats to see if the engine is active and ready to process your upcoming requests.

Use Cases

• Long-form Document Analysis: When feeding large research papers or massive codebases into an agent, use the optimizer to strip out repetitive header/footer data or standard license agreements.
• Extended Conversational Threads: If you have an active project chat that has grown to hundreds of messages, the optimizer keeps the context window lean, preventing the LLM from losing track of the core objective.
• Agentic Workflow Chains: When chaining multiple agent tasks, the optimizer acts as a bridge, cleaning up output from the previous agent before passing it to the next to ensure maximum token space for high-value reasoning.

Example Prompts

1. "OpenClaw, please run the context optimizer on our current documentation thread to reduce the token load before we begin the next architectural review session."
2. "Show me the statistics from the last 24 hours; I want to see how much money I saved on token costs using the hybrid compression strategy."
3. "Enable the Pro tier features and set a custom rule to prioritize code comments over verbose logging when optimizing context for debugging tasks."

Tips & Limitations

To get the best results, use the --strategy=hybrid mode, which balances speed and accuracy. Be aware that extreme summarization can occasionally remove subtle context nuances if your prompt is highly technical; monitor your outputs via claw optimize dashboard to ensure the summary quality matches your expectations. For best results, use the Memory System integration to allow the optimizer to reference historical patterns effectively.