A framework for tiered model selection and cost optimization across multi-agent AI workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-cost-strategy
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 agent-cost-strategy 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 Agent Cost Strategy?
The Agent Cost Strategy is a specialized framework designed to minimize API expenses by intelligently routing tasks to the most cost-effective model class. Within the Openclaw Skills ecosystem, this strategy categorizes AI models into Fast/Cheap, Mid-tier, and Powerful tiers, ensuring that resources are allocated based on task complexity rather than defaulting to expensive models for every interaction.
By focusing on prompt caching and explicit sub-agent configuration, this skill helps developers avoid common cost leaks that occur during long sessions or automated background tasks. It prioritizes keeping sessions alive to take advantage of cache hits, which can reduce input costs by up to 90% after the initial context is established and the cache is warmed up.
Agent Cost Strategy Use Cases
- Reducing high API bills for production AI agents
- Configuring cost-efficient background monitoring and cron jobs
- Selecting the right model for sub-agent delegation
- Implementing prompt caching strategies for repetitive tasks
- Managing model escalation when lower-tier models fail to resolve a task
How Agent Cost Strategy Works
- The system evaluates the complexity of a task, such as fixing tests versus designing architecture.
- It selects the lowest-priced model tier capable of handling that specific task complexity reliably.
- For sub-agents, it explicitly overrides default settings to use high-speed, low-cost models instead of inheriting expensive parent models.
- It manages session duration to ensure the prompt cache remains warm and available for reuse across subsequent messages.
- For non-urgent work, it routes requests through Batch APIs to secure significant discounts compared to real-time processing.
Agent Cost Strategy Setup
To integrate this strategy into your workflow, you must explicitly define models in your configuration or session spawn commands. For Openclaw Skills implementations, use the following pattern:
# Explicitly set the model when spawning sub-agents to avoid cost leaks
sessions_spawn --model "claude-3-haiku-20240307" --task "fix failing tests"
Ensure your heartbeat intervals are configured to stay just under the provider's cache TTL to maintain a warm cache state.
Agent Cost Strategy Data Schema & Taxonomy
The Agent Cost Strategy uses a tiered routing table to organize model usage and metadata taxonomy:
| Tier | Purpose | Recommended Models |
|---|---|---|
| Fast/Cheap | Automated fixes, sub-agents, simple lookups, cron jobs | Claude Haiku, GPT-4o-mini, Gemini Flash |
| Mid-tier | Conversational dialogue, planning, moderate reasoning | Claude Sonnet, GPT-4o, Gemini Pro |
| Powerful | Deep code review, complex architecture, escalation | Claude Opus, GPT-4.5, Gemini Ultra |
Cost-efficiency is tracked via cache-hit rates and model distribution metrics (e.g., target 80/20 split between Haiku and Sonnet).
Agent Cost Strategy Advanced Features
- Intelligent model escalation: Automatically switches to a more powerful model after two failed attempts on a lower tier
- Prompt cache optimization: Strategic use of heartbeats and session management to maximize cache-read rates
- Sub-agent inheritance control: Prevents sub-agents from defaulting to expensive parent session models, stopping the primary source of cost leaks
- Batch API routing: Automated scheduling for non-urgent tasks to achieve a 50% discount on API spend
SKILL.md
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