An intelligent recommendation system that generates optimal AI agent technology stacks based on project requirements, constraints, and team expertise.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install toolweb-ai-agent-stack-builder
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 toolweb-ai-agent-stack-builder 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 AI Agent Stack Builder?
The AI Agent Stack Builder is a sophisticated recommendation engine designed for developers and architects navigating the complex ecosystem of modern AI. By leveraging this tool within the Openclaw Skills ecosystem, teams can eliminate the guesswork involved in selecting the right combination of large language models, orchestration frameworks, and vector databases. It provides a structured approach to technical decision-making, ensuring that every choice is backed by data and aligned with specific business constraints such as budget, latency, and compliance.
This skill addresses a critical pain point in modern software engineering: the overwhelming number of choices when building agentic systems. Whether you are developing a customer service chatbot, an autonomous research agent, or a data analysis tool, the AI Agent Stack Builder evaluates your unique constraints to generate a prioritized set of recommendations tailored to your operational environment.
AI Agent Stack Builder Use Cases
- Architecting a customer service chatbot with specific HIPAA compliance and low-latency requirements using Openclaw Skills.
- Selecting cost-effective LLMs and frameworks for autonomous research agents with high anticipated user load.
- Comparing different orchestration libraries like LangChain and LlamaIndex for complex multi-turn conversation tools.
- Evaluating on-premise versus cloud deployment strategies for sensitive data analysis agents.
- Onboarding new development teams by recommending tools that match their current expertise level.
How AI Agent Stack Builder Works
- The user provides project requirements via a JSON payload, including project type, expected user load, budget, and required capabilities.
- The system analyzes the input against a managed database of frameworks, such as orchestration and retrieval libraries, and various LLM providers.
- A scoring algorithm calculates suitability scores for each component based on technical constraints like latency and team expertise.
- The skill generates a comprehensive recommendation report including prioritized technology lists, detailed reasoning, and estimated monthly costs.
- Developers can then query specific endpoints to get the latest metadata on available frameworks and models supported by the Openclaw Skills architecture.
AI Agent Stack Builder Setup
To integrate this skill into your workflow, ensure you have access to the Openclaw Skills environment. You can call the API endpoint directly using the following configuration:
curl -X 'POST' \
'https://api.mkkpro.com/creative/ai-agent-stack-builder/api/agent/stack' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"formData": {
"projectType": "customer_service_chatbot",
"userLoad": "high",
"budget": "medium",
"latency": "low",
"deployment": "cloud",
"expertise": "intermediate",
"capabilities": ["natural_language_understanding"],
"timestamp": "2024-01-15T14:30:00Z"
},
"sessionId": "sess_123"
}'
AI Agent Stack Builder Data Schema & Taxonomy
The AI Agent Stack Builder utilizes a structured taxonomy to categorize recommendations. The data is organized into three primary clusters to ensure clarity in the output generated by Openclaw Skills:
| Component | Description | Metadata Example |
|---|---|---|
| Frameworks | Orchestration and retrieval libraries | Suitability score, reasoning, maturity |
| LLMs | Large Language Models | Context window, cost per 1M tokens, latency |
| Supporting Tech | Infrastructure components | Vector DBs, monitoring tools, category |
AI Agent Stack Builder Advanced Features
- Automated suitability scoring for frameworks based on team skill level and project complexity.
- Dynamic cost estimation for multi-model deployment strategies to optimize ROI.
- Integration with enterprise-grade monitoring and security compliance notes for regulated industries.
- Multi-agent orchestration support through specialized Openclaw Skills technical protocols.
- Real-time updates on LLM context windows and pricing from major providers.
SKILL.md
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