A professional-grade skill for managing DeepInfra model routing, provider authentication, and cost-aware fallback chains.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install deep-infra
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 deep-infra 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 Deep Infra?
The Deep Infra skill for Openclaw Skills provides a robust framework for integrating OpenAI-compatible workflows with the DeepInfra ecosystem. It allows developers to leverage a wide array of open-source and frontier models while maintaining strict control over routing logic, reliability, and costs. By utilizing Openclaw Skills to manage these connections, teams can ensure their AI agents are using the most efficient models for specific tasks like coding, summarization, or long-context analysis.
This skill centralizes operational intelligence, including provider authentication and fallback policies, within a local memory structure. It emphasizes model diversity, allowing users to build resilient systems that can gracefully handle rate limits or provider outages by switching across independent model families like DeepSeek, Moonshot, or NVIDIA.
Deep Infra Use Cases
- Routing specific AI workloads (coding, extraction, analysis) to the most cost-effective and capable models.
- Setting up multi-model fallback chains to maintain high availability during provider incidents.
- Enforcing strict budget guardrails and cost ceilings for large-scale LLM deployments.
- Managing authentication and provider metadata for DeepInfra-hosted inference requests.
How Deep Infra Works
- The system first classifies the request into a specific workload class such as coding or summarization.
- It checks the local routing-rules.md to map the task to a primary model and a secondary fallback model.
- The skill validates the DEEPINFRA_API_KEY from the environment to ensure secure and authorized access.
- Inference is executed via the DeepInfra API, with the skill monitoring for potential errors or rate limits.
- If a failure occurs, the predefined fallback logic triggers a switch to a different model family to ensure task completion.
- All routing decisions, spend data, and incident logs are recorded in the local filesystem for future optimization.
Deep Infra Setup
To activate this capability within Openclaw Skills, follow these installation and configuration steps:
# Ensure required tools are installed
sudo apt-get install curl jq
# Configure your environment variable
export DEEPINFRA_API_KEY="your_api_key_here"
# Initialize the memory directory
mkdir -p ~/deep-infra/
Deep Infra Data Schema & Taxonomy
The skill organizes its state and policies locally within the ~/deep-infra/ directory using the following schema:
| File | Description |
|---|---|
memory.md |
Tracks the active routing profile and global constraints. |
providers.md |
Stores confirmed provider choices and authentication statuses. |
routing-rules.md |
Defines the mapping of tasks to specific models and fallbacks. |
incidents.md |
A historical log of rate limits, outages, and recovery notes. |
budgets.md |
Contains spend guardrails, ceilings, and optimization logs. |
Deep Infra Advanced Features
- Intelligent workload classification to prioritize model performance based on task complexity.
- Cross-family fallback strategies to prevent cascading failures across similar LLM architectures.
- Granular cost controls that allow for spend ceilings at the task class level.
- Localized incident tracking to build a knowledge base of provider reliability over time.
- Integration with other Openclaw Skills for enhanced monitoring and API payload shaping.
SKILL.md
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