A sophisticated optimization framework designed to reduce token consumption in AI operations by 60-80% through architecture-aware memory and tool management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install token-efficient-agent
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 token-efficient-agent 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 Token-Efficient Agent?
The Token-Efficient Agent is a specialized skill for the OpenClaw ecosystem that focuses on minimizing computational overhead without sacrificing response quality. By understanding the underlying hierarchy of Openclaw Skills, this agent intelligently navigates between session context, working memory, and external data to retrieve only the most relevant information.
It addresses common AI bottlenecks such as context window overflow and high operational costs. By implementing battle-tested techniques like progressive disclosure and semantic pagination, the skill ensures that LLMs process high-value data rather than raw, noisy information, resulting in faster and more cost-effective AI interactions.
Token-Efficient Agent Use Cases
Primary Scenarios
- Reducing operational costs for high-frequency AI agent deployments.
- Managing complex tasks within limited context windows to prevent data loss.
- Accelerating response times by minimizing the data payload processed by the model.
- Improving long-term memory retrieval accuracy by using targeted extraction instead of brute-force loading.
- Optimizing document-heavy workflows where only specific sections are relevant to the user query.
How Token-Efficient Agent Works
- Context Evaluation: The skill first checks the cheapest available context (session history) to answer a query before escalating to higher-cost resources.
- Hierarchical Querying: If session context is insufficient, it performs a broad search with high relevance scores to identify specific files or documents.
- Targeted Extraction: Instead of loading full files, it uses line-specific or offset-based fetching to extract precise snippets.
- Tool Result Transformation: Raw data from tools is filtered and condensed, keeping only essential fields like titles or IDs before model ingestion.
- Summarization Cascades: Large datasets are processed through multi-level summarization to provide the model with a decision-focused brief.
- Predictive Preloading: The agent anticipates future needs based on user patterns and preloads data during idle cycles.
Token-Efficient Agent Setup
Installation and Configuration
To integrate these optimization techniques into your environment, ensure your Openclaw Skills configuration supports advanced tool parameters.
# Navigate to your OpenClaw skills directory
cd openclaw/skills
# Clone the token-efficient-agent repository
git clone https://github.com/openclaw/token-efficient-agent.git
# Enable the skill in your configuration file
openclaw-cli enable-skill token-efficient-agent
Update your config.yaml to set default maxResults and minScore thresholds to align with efficiency goals.
Token-Efficient Agent Data Schema & Taxonomy
Data Taxonomy and Organization
The skill organizes data based on retrieval cost and semantic relevance:
| Layer | Description | Retrieval Method |
|---|---|---|
| Level 1 | Session Context | Direct access to current conversation buffer |
| Level 2 | Working Memory | memory_search with restricted maxResults |
| Level 3 | Long-term Memory | Targeted memory_get using specific line offsets |
| Level 4 | External Tools | feishu_fetch_doc or web_search with strict character limits |
Token-Efficient Agent Advanced Features
Power-User Capabilities
- Anticipatory Caching: Preloads memory snippets based on time-of-day or calendar triggers to eliminate reactive latency.
- Tool Call Fusion: Sequences multiple API calls to minimize context switching and redundant metadata processing.
- Semantic Boundary Detection: Automatically identifies document headers and conclusions to skip irrelevant middle sections.
- Heartbeat Optimization: Utilizes background cycles for memory consolidation and context window pruning.
- Custom Transformation Hooks: Allows developers to define specific fields to keep or discard from tool outputs globally.
SKILL.md
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