Advanced context management for DeepSeek models featuring auto-compaction and dynamic relevance-based optimization.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context-optimizer
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-optimizer 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 Pruner?
Context Pruner is a sophisticated context management engine designed specifically to handle the demands of Large Language Models, with specialized optimization for the DeepSeek 64k token window. By implementing intelligent compaction strategies and dynamic relevance scoring, it ensures that AI agents maintain high performance and logical consistency without hitting hard context limits. This tool is essential for developers building complex Openclaw Skills that require long-running conversations, extensive data processing, or high-fidelity memory management.
The skill provides a robust framework for adaptive pruning, utilizing semantic deduplication to remove redundant information while preserving high-priority system messages. Whether you are building an autonomous research agent or a coding assistant, integrating this into your Openclaw Skills workflow ensures that token overhead is minimized and context health is monitored in real-time.
Context Pruner Use Cases
- Managing extremely long-form conversations in Openclaw Skills without losing critical historical data.
- Reducing operational token costs by intelligently pruning redundant or low-relevance message blocks.
- Implementing hierarchical memory systems for historical data retrieval during complex technical tasks.
- Preventing context overflow errors in DeepSeek-based agents during intensive multi-file coding sessions.
How Context Pruner Works
- The skill initializes a monitor with a specific context limit, typically tuned to 64,000 tokens for DeepSeek models.
- It continuously tracks token usage and triggers auto-compaction once usage crosses a defined threshold (e.g., 75%).
- The engine applies various strategies—including semantic, temporal, and extractive compaction—to condense historical data.
- Using query-aware relevance scoring, the skill filters and prioritizes messages that are most similar to the user's current intent.
- Excess data is offloaded to a hierarchical archive system, allowing the agent to retrieve snippets later if the current context lacks specific information.
Context Pruner Setup
To integrate this functionality into your Openclaw Skills environment, navigate to the skill directory and install the necessary peer dependencies:
cd ~/.clawdbot/skills/context-pruner && npm install tiktoken @xenova/transformers
Add the skill to your configuration file:
skills:
context-pruner:
enabled: true
config:
contextLimit: 64000
autoPrune: true
Context Pruner Data Schema & Taxonomy
The skill organizes its internal logic and archival system using the following schema:
| Parameter | Type | Description |
|---|---|---|
| contextLimit | Number | The maximum token window (default: 64000 for DeepSeek) |
| archivePath | String | Local directory for storing the hierarchical memory archive |
| strategies | Array | List of active strategies: semantic, temporal, extractive, or adaptive |
| relevanceDecay | Float | The rate at which older message relevance scores decrease (0.0 to 1.0) |
| logToChat | Boolean | Determines if optimization events are reported in the chat interface |
Context Pruner Advanced Features
- Semantic Deduplication: Identifies and merges messages with similar meanings to preserve space without losing context.
- Hierarchical Memory System: Implements a RAM-vs-Storage architecture where the current context stays fast while the archive handles deep history.
- Query-Aware Relevance: Dynamically re-scores the importance of historical messages based on the semantic similarity to the active user prompt.
- Real-time Health Monitoring: Continuously logs optimization events, providing transparency into how Openclaw Skills are managing token density.
SKILL.md
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