ClawSaver is an intelligent request-batching utility that reduces AI model API costs by 20–40% through efficient message buffering.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install clawsaver
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 clawsaver 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 ClawSaver?
ClawSaver is a high-performance utility within the Openclaw Skills ecosystem designed to eliminate the waste associated with redundant LLM API calls. In standard agent architectures, every follow-up message from a user triggers a full API request, forcing the model to re-process expensive context like system prompts and chat history. ClawSaver solves this by introducing a smart buffering layer that waits a fraction of a second to collect related inputs into a single batch.
By consolidating multiple user interactions into one request, developers can significantly lower token consumption and operational overhead. This skill is a must-have for teams scaling production AI applications who want to maintain high response quality while drastically reducing their monthly API bill. It is built to be lightweight, secure, and easily integrated into any JavaScript-based AI workflow.
ClawSaver Use Cases
- Reducing token waste in conversational chat applications where users send multiple short follow-up messages.
- Optimizing customer support bots to handle multi-turn queries more cost-effectively.
- Managing high-volume data ingestion for AI agents without triggering excessive per-message API overhead.
- Scaling multi-user AI platforms where context re-loading costs are a primary financial bottleneck.
How ClawSaver Works
- The application receives a user message and routes it to the ClawSaver buffer instead of calling the API immediately.
- A debounce timer (defaulting to 800ms) starts, waiting for any additional follow-up messages from the same user session.
- If more messages arrive within the window, they are added to the queue and the timer is refreshed.
- Once the buffer window closes or a message limit is reached, the system flushes the entire batch.
- A single API call is made to the model containing all batched messages, allowing the model to process the context only once.
- The model returns a comprehensive response, and the system resets for the next interaction.
ClawSaver Setup
Install the skill via the command line:
clawhub install clawsaver
To integrate ClawSaver into your project, initialize the session debouncer in your message handler:
import SessionDebouncer from 'clawsaver';
const debouncers = new Map();
function handleMessage(userId, text) {
if (!debouncers.has(userId)) {
debouncers.set(userId, new SessionDebouncer(
userId,
(msgs) => callModel(userId, msgs)
));
}
debouncers.get(userId).enqueue({ text });
}
ClawSaver Data Schema & Taxonomy
ClawSaver maintains a local, in-memory state for active sessions to ensure zero-latency processing. The configuration and metadata are structured as follows:
| Attribute | Description |
|---|---|
userId |
A unique string identifying the specific user or chat session. |
debounceMs |
The wait time in milliseconds before flushing the batch (default: 800ms). |
maxWaitMs |
The absolute maximum time a message can wait in the buffer before a forced flush. |
maxMessages |
The maximum number of messages allowed per batch request. |
messages |
An array of message objects currently stored in the buffer. |
ClawSaver Advanced Features
- Three distinct operational profiles: Balanced (standard chat), Aggressive (high-volume batching), and Real-Time (low-latency interactive).
- Manual
forceFlushtriggers to override the buffer for high-priority system events or specific user commands. - Comprehensive
getStateAPI for real-time monitoring of buffering metrics and ROI tracking. - Zero-dependency, pure JavaScript architecture ensuring that your data never leaves your local environment.
- Intelligent token reservation settings to prevent batched requests from exceeding model context window limits.
SKILL.md
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