A high-efficiency toolkit designed to minimize token consumption and manage API costs for Inception Labs models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install inception-token-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 inception-token-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 Inception Token Optimizer?
The Inception Token Optimizer is a specialized resource designed to help developers manage their API footprint when working with Inception Labs models like Mercury. By implementing strategies like prompt compression and context pruning, this resource ensures that your AI agents remain cost-effective while staying within rate limits.
Utilizing this tool as part of your Openclaw Skills library allows for smarter resource allocation, specifically tailored for high-frequency or budget-conscious automation environments. It bridges the gap between raw LLM power and operational efficiency by providing structured methods to keep token counts low without sacrificing response quality.
Inception Token Optimizer Use Cases
- Optimizing prompts before transmission to Inception models to reduce latency and cost.
- Monitoring and enforcing token budgets when approaching Inception Labs free-tier limits.
- Scaling automation workflows that require high-volume API calls without exceeding rate caps.
- Implementing intelligent caching to prevent redundant processing of identical user queries within Openclaw Skills frameworks.
How Inception Token Optimizer Works
- Analyzes the incoming prompt to estimate token count and identify compression opportunities through filler word removal.
- Prunes context by summarizing older interactions or removing redundant metadata to fit within specific budget constraints.
- Checks the local LRU cache to see if a valid response already exists for the prompt hash to avoid unnecessary API calls.
- Selects the most cost-effective model, reserving high-tier models like Mercury only for complex reasoning tasks.
- Enforces token bucket limits using a sliding window algorithm to ensure requests remain within the per-minute caps.
Inception Token Optimizer Setup
To integrate this optimization layer into your environment, ensure the scripts provided in the repository are accessible to your agent.
# Navigate to your agent directory
cd your-agent-workspace
# Ensure the necessary scripts are present
ls scripts/token_bucket.py scripts/lru_cache.py
You can then initialize the budget guard in your Python logic to wrap any Inception API calls, ensuring every request is pre-processed according to Openclaw Skills best practices.
Inception Token Optimizer Data Schema & Taxonomy
The skill tracks token metrics and cache states to optimize performance across Openclaw Skills integrations. It utilizes the following schema for management:
| Component | Description | Persistence |
|---|---|---|
| Token Bucket | Tracks requests, input tokens, and output tokens per minute | In-Memory (Sliding Window) |
| LRU Cache | Stores up to 256 hashed prompt-response pairs | Session-based |
| Context Buffer | Maintains a rolling window of conversation history for pruning | Active Session |
Inception Token Optimizer Advanced Features
- Automated model switching between Mercury and lighter variants based on prompt complexity and budget.
- Sliding window rate limiting to handle burst traffic gracefully without triggering API bans.
- Context summary injection which replaces old chat history with concise metadata snapshots.
- Customizable temperature and max_tokens management to prevent verbose or runaway output generation within Openclaw Skills.
SKILL.md
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