The economic layer for AI agents to track, allocate, and settle inference costs during collaborative tasks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context-window-economics
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-window-economics 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 Window Economics?
Context Window Economics Protocol (CWEP) is a specialized framework designed to handle the financial complexities of multi-agent interactions. As AI agents increasingly collaborate on complex workflows, managing the underlying inference costs becomes a significant challenge. This module within Openclaw Skills provides a robust system for token metering, cost tracking, and fair allocation using advanced game theory models like Shapley values.
By implementing this economic layer, developers can ensure transparency and trust in autonomous agent ecosystems. It allows for precise settlement of context window usage, enabling agents to negotiate cost-sharing and report expenditures without manual intervention. It serves as a critical component of the Agent Trust Stack for professional AI deployments.
Context Window Economics Use Cases
- Allocating shared context window costs across multiple autonomous agents in a collective workflow.
- Negotiating cost-sharing agreements before executing resource-intensive multi-agent transactions.
- Implementing usage-based billing and surge pricing for context-constrained AI environments using Openclaw Skills.
- Tracking granular inference costs per agent, task, or model to optimize budget allocation.
How Context Window Economics Works
- Log every interaction including input tokens, output tokens, and specific model costs using the integrated CostTracker system.
- Define the set of participating agents and their relative contributions to the shared context for a specific transaction.
- Apply mathematical allocation methods such as Shapley or Nash bargaining to determine each agent's financial liability based on marginal utility.
- Calculate congestion pricing adjustments based on real-time context window utilization thresholds to manage resource scarcity.
- Export detailed settlement reports to reconcile costs and finalize the collaborative billing cycle within the 24-hour recommended window.
Context Window Economics Setup
Install the package via pip to begin managing costs within your Openclaw Skills environment:
pip install context-window-economics
Context Window Economics Data Schema & Taxonomy
The protocol utilizes a structured JSONL format for data persistence, ensuring high-performance logging and easy parsing. The schema focuses on the following primary attributes:
| Attribute | Description |
|---|---|
| agent_id | Unique identifier for the participating AI agent |
| transaction_id | Correlates logs with specific tasks or multi-agent sessions |
| input_tokens | Total tokens provided in the prompt context |
| output_tokens | Total tokens generated by the model |
| model | The specific model identifier (e.g., claude-sonnet-4-6) |
| cost_usd | The raw financial cost of the transaction |
| allocation_method | The method used (Shapley, Proportional, etc.) for settlement |
Context Window Economics Advanced Features
- Support for Shapley-fair cost splitting based on the mathematical marginal contribution of each agent.
- Implementation of Nash bargaining protocols for bilateral agent settlements and negotiations.
- Dynamic surge and congestion pricing logic based on configurable context utilization thresholds.
- Automated generation of time-bound settlement reports for multi-agent ecosystems built with Openclaw Skills.
- Low-overhead JSONL logging designed for high-frequency agent-to-agent transactions.
SKILL.md
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