A technical skill that empowers AI agents to provide real-time, accurate data on model pricing, rankings, and hardware requirements by bypassing outdated training data.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai
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 ai 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 Artificial Intelligence?
The Artificial Intelligence skill is designed to solve the problem of knowledge cutoff in LLMs. By integrating this skill into Openclaw Skills, agents are instructed to stop relying on internal memory for volatile information like API pricing or model rankings. Instead, it enforces a protocol of checking live sources like OpenRouter and LM Arena. This ensures that any advice given regarding AI infrastructure, model selection, or cost-optimization is based on current market reality rather than obsolete training data.
Beyond just data retrieval, this skill provides a framework for technical decision-making. It offers specific heuristics for hardware allocation, hallucination reduction strategies, and the nuanced choice between RAG and fine-tuning. This makes it an essential tool for developers and architects using Openclaw Skills to build production-grade AI implementations.
Artificial Intelligence Use Cases
- Comparing real-time API pricing across all major providers to optimize operational costs.
- Evaluating current model performance using weekly updated crowdsourced ELO rankings.
- Planning local infrastructure by calculating exact VRAM requirements for 7B, 13B, and 70B parameter models.
- Architecting RAG pipelines with specific system prompt requirements and JSON schema validation.
- Performing accurate token math for budget forecasting using tiktoken and provider-specific tokenizers.
How Artificial Intelligence Works
- The agent identifies a query related to AI model availability, pricing, or technical specifications.
- It ignores internal training data estimates for volatile metrics like context windows or rate limits.
- It references specified live aggregators (OpenRouter, LM Arena) to fetch current rankings and pricing.
- It applies standardized hardware formulas (e.g., Q4 quantization halving VRAM requirements) to generate local deployment specs.
- It delivers a structured response incorporating verified sources, citation requirements, and temperature-specific recommendations.
Artificial Intelligence Setup
To enable this skill within your Openclaw Skills environment, add the following configuration to your agent's skill manifest:
# Install via your skill manager
openclaw install artificial-intelligence
Ensure the agent has network access to fetch data from openrouter.ai and lmarena.ai for real-time updates.
Artificial Intelligence Data Schema & Taxonomy
The skill organizes AI technical data according to the following taxonomy:
| Category | Source | Metrics Tracked |
|---|---|---|
| Model Pricing | openrouter.ai | Cost per 1M tokens, Rate limits |
| Benchmarks | lmarena.ai | Crowdsourced ELO, Weekly rankings |
| Hardware | VRAM Specs | GB requirements for 7B, 13B, 70B models |
| Tokenization | tiktoken | Character-to-token ratios for code and English |
Artificial Intelligence Advanced Features
- Outage awareness protocol that checks provider status pages before diagnosing user code errors.
- Quantization-aware hardware estimation for local LLM deployment (Ollama, LM Studio).
- Advanced hallucination mitigation strategies including verified source enforcement and temperature 0 settings.
- Cost-benefit analysis thresholding (Local vs API) based on a $100/month spend logic.
SKILL.md
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