A strategic framework to audit and slash AI agent token usage through the Token Efficiency Matrix.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context-budget-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-budget-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 Budget Optimizer?
The Context Budget Optimizer is a specialized skill designed for power users of AI agents who face high monthly API costs. It utilizes the Token Efficiency Matrix, a 4-quadrant audit tool, to categorize every element of an agent's context—from system prompts to memory files—based on its token weight and value delivered.
This skill identifies invisible waste such as stale memory entries, redundant skills, and oversized files that often account for 60-70% of total token burn. By integrating this into your Openclaw Skills library, you can transform your AI operations from expensive and bloated to lean and high-performance, ensuring you only pay for tokens that actually improve output quality.
Context Budget Optimizer Use Cases
- Identifying high-cost, low-ROI context elements for immediate removal to save budget.
- Reducing monthly AI API bills that have scaled unexpectedly during heavy development.
- Transitioning from eager loading of skills to efficient on-demand lazy loading.
- Tiering agent memory into Hot, Warm, and Cold categories to maintain context relevance without bloat.
- Implementing model-routing strategies to use cheaper models like Haiku for routine tasks.
How Context Budget Optimizer Works
- Perform a Context Inventory to map all layers including system prompts, active skills, and project files.
- Apply the Token Efficiency Matrix to score each item based on Token Cost (1-5) and ROI (1-5).
- Execute the Reduction Playbook by immediately cutting low-ROI items and optimizing high-cost, high-value content.
- Implement structural optimizations like lazy-loading skills via a central index and memory tiering using a dedicated FOCUS.md file.
- Follow the 3-week roadmap to lock in savings and establish a quarterly monitoring routine for your Openclaw Skills environment.
Context Budget Optimizer Setup
To implement the Context Budget Optimizer in your workspace, follow these configuration steps:
- Create a FOCUS.md file to serve as your Tier 1 (Hot) memory.
- Set up a SKILL-INDEX.md to enable lazy loading for your broader library.
- Update your agent's system prompt to reference the index instead of auto-loading full skill files.
# Create the tiered memory structure
mkdir -p memory/archive/
touch FOCUS.md SKILL-INDEX.md
- Populate your initial context inventory table to calculate baseline daily costs and identify the first items to cut.
Context Budget Optimizer Data Schema & Taxonomy
The skill organizes data through a multi-tier memory structure and efficiency scoring tables to maintain a lean context window.
| Layer | Component | Description | Target Token Weight |
|---|---|---|---|
| Tier 1 | FOCUS.md | Active priorities and current task context | < 500 tokens |
| Tier 2 | MEMORY.md | Summarized historical data and decisions | < 2,000 tokens |
| Tier 3 | /archive/ | Cold storage for old notes and archived projects | N/A (External) |
| Index | SKILL-INDEX.md | Directory for lazy-loading Openclaw Skills | ~500 tokens |
| Audit | Inventory | Matrix scoring for cost vs. ROI analysis | Internal Metadata |
Context Budget Optimizer Advanced Features
- Token Efficiency Matrix scoring for precise context auditing and cost forecasting.
- Multi-tier memory architecture (Hot/Warm/Cold) to prevent long-term context drift.
- Automated model-routing decision tree to switch between Opus, Sonnet, and Haiku based on task complexity.
- Compression templates designed to reduce system prompt size by up to 87%.
- Lazy-loading framework that ensures secondary skills only consume tokens when explicitly triggered.
- 3-week structured cost reduction roadmap for systematic optimization of Openclaw Skills.
SKILL.md
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