A sophisticated framework for reducing token usage while preserving technical integrity and artifact tracking in long-running AI agent sessions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context-engineering
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-engineering 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 Compression?
Context compression is a critical technical skill designed to manage agent sessions that generate millions of tokens, often exceeding standard context window limits. Instead of naive truncation, this skill focuses on the optimization of tokens-per-task—the total tokens consumed to complete a specific objective—by using structured summarization to ensure the agent never loses sight of critical file paths or architectural decisions.
By leveraging Openclaw Skills for context engineering, developers can implement robust strategies like anchored iterative summarization. This approach uses dedicated sections for intent, file modifications, and next steps to act as a checklist for the summarizer, preventing the silent information drift that typically occurs in opaque compression methods.
Context Compression Use Cases
- Managing agent sessions that exceed context window limits (5M+ token systems).
- Maintaining artifact trail integrity to ensure agents remember which files were modified.
- Designing high-fidelity conversation summarization strategies for long-running debug sessions.
- Optimizing token costs by targeting tokens-per-task efficiency.
- Evaluating the functional quality of compression using probe-based testing.
How Context Compression Works
- Monitor context utilization and activate the compression workflow when reaching a 70-80% threshold.
- Identify the optimal strategy, such as anchored iterative summarization for coding tasks or opaque compression for maximum savings.
- Extract structured data from the truncated history, focusing on session intent, files modified, and decisions made.
- Merge the new summary into a persistent, structured document rather than performing a full regeneration to prevent detail loss.
- Execute probe-based evaluation to verify that technical details like error messages and file paths remain retrievable.
Context Compression Setup
To integrate context compression into your workflow using Openclaw Skills, you should configure your agent's scaffolding to trigger summarization logic based on token usage. Use the following structure to initialize the compression module:
# Define compression thresholds and strategy within your agent environment
openclaw config set compression_threshold 0.8
openclaw config set summary_strategy anchored_iterative
Ensure your agent has access to a structured summary template that includes sections for Session Intent, Files Modified, Decisions Made, and Next Steps.
Context Compression Data Schema & Taxonomy
The skill organizes context into a structured summary format to ensure high accuracy and recall. The following metadata schema is utilized:
| Section | Purpose | Retention Priority |
|---|---|---|
| Session Intent | Defines the user's primary goal | High |
| Files Modified | Tracks file paths and specific code changes | Critical |
| Decisions Made | Records reasoning for architectural choices | High |
| Current State | Lists test results and active errors | Medium |
| Next Steps | Outlines the remaining task list | Medium |
Context Compression Advanced Features
- Anchored Iterative Summarization: Maintains persistent sections to prevent information loss over multiple compression cycles.
- Probe-Based Evaluation: Uses recall and decision probes to measure the functional quality of the compressed context.
- Three-Phase Workflow Integration: Transitions context from Research to Planning to Implementation phases for large codebase management.
- Artifact Trail Indexing: Separately tracks file-state changes to mitigate the inherent weakness of LLMs in tracking file modifications over time.
SKILL.md
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