An automated decision-support system that evaluates AI Agent ideas across eight key dimensions and generates a professional, interactive HTML feasibility report.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-decision
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-decision 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 Decision Assistant?
The AI Agent Decision Assistant is a comprehensive, multi-dimensional decision-support system designed to guide developers, builders, and product managers through the complex process of planning, building, and launching AI Agents. By leveraging deep web search and parallel data gathering capabilities, this skill transforms a simple agent concept into an exhaustive, data-driven feasibility study.
Whether you are selecting the ideal LLM, choosing between agent frameworks like CrewAI, LangChain, or Dify, estimating Token cost budgets, or analyzing competitor saturation, this tool automates the heavy lifting. It integrates directly with Openclaw Skills to compile technical specifications, market trends, and risk assessments into a fully interactive, radar-chart-equipped HTML report, giving you a definitive go/no-go recommendation for your AI initiatives.
AI Agent Decision Assistant Use Cases
- Assessing AI Agent Feasibility: Rapidly evaluate whether a newly proposed AI Agent concept (such as a legal assistant, customer service agent, or smart e-commerce guide) is technically and economically viable.
- Technical Architecture Selection: Determine the ideal LLMs (GPT-4o, Claude 3.5, DeepSeek, Qwen, etc.), frameworks (LangChain, CrewAI, Dify, Coze), and memory schemas for a specific use case.
- Market & Competitor Research: Automate the identification of market saturation, key competitors, growth rates, and blue-ocean opportunities in the agent landscape.
- ROI and Budget Estimation: Calculate realistic projections for API Token consumption, vector database hosting, infrastructure, and human labor to determine the overall Total Cost of Ownership (TCO).
How AI Agent Decision Assistant Works
- Parallel Data Collection: Upon receiving your Agent's concept and domain, the system triggers parallel WebSearch queries across six vectors: technology ecosystems, competitor landscapes, market data, industry trends, LLM API pricing, and business models.
- Deep Fetching & Extraction: The skill identifies data-rich pages from search results and executes a WebFetch to scrape granular data on competitor features, frameworks, and pricing matrices.
- Multi-Dimensional Evaluation: A weighted Python scoring model evaluates the gathered data across 8 core dimensions (Tech Maturity, Competition, Market Prospect, Industry Fit, Dev Feasibility, System Stability, Cost Control, and Go-to-Market potential).
- Interactive Report Generation: The system executes a specialized Python generation script to compile a responsive, interactive HTML file featuring real-time radar charts, risk matrices, and structured milestones.
AI Agent Decision Assistant Setup
To get started with the AI Agent Decision Assistant, ensure you have Python installed. Clone the repository and configure your environment.
# Navigate to your workspace directory
cd to/your/workspace
# Clone the skill repository
git clone https://github.com/bettermen/agent-decision.git
cd agent-decision
# Ensure python is available as required by Openclaw Skills
python --version
To trigger the evaluation workflow directly within your AI environment, use the trigger keywords followed by your product name and application direction. For example:
Agent feasibility: Build a multi-agent customer service systemAssess the feasibility of a coding copilot agentagent decision: eCommerce Shopping Intelligent Assistant
AI Agent Decision Assistant Data Schema & Taxonomy
The skill processes structured metrics to output a beautiful interactive report. Below is the taxonomy used to weigh and generate the decision matrix:
Evaluation Dimension Weights
| Dimension | Weight | Key Assessment Standards |
|---|---|---|
| Technical Maturity | 15% | LLM capability fit, framework ecosystem maturity, open-source alternatives |
| Competitive Landscape | 15% | Number of competitor agents, market concentration, differentiation headroom |
| Market Prospect | 15% | Market size, niche growth, customer willingness to pay |
| Industry Fit | 10% | Alignment with industry demands, substitution level over traditional systems |
| Dev Feasibility | 15% | Technical hurdles, team AI readiness, timeline, technical debt risk |
| System Stability | 10% | Hallucination controls, reliability under multi-turn conversations, error recovery |
| Cost Control | 10% | Token costs scalability, infrastructure overhead, ROI projection |
| Promotion Potential | 10% | Viral loops, GTM clarity, SaaS pricing viability |
Generated Report Output Schema
The output HTML report organizes its findings into standard components:
Executive Summary: Core go/no-go recommendation (Strongly Recommended [Score >=80], Proceed with Caution [65-79], Pause & Observe [50-64], Not Recommended [<50]).Interactive Radar Chart: Built on Canvas, dynamically mapping the 8 evaluation vectors.Risk Matrix: High, Medium, and Low classifications of Technical, Market, Compliance, and Operational risks.Technical Matrix: A side-by-side mapping comparing LLM models, frameworks, and memory options (vector, graph, summary).
AI Agent Decision Assistant Advanced Features
- Automated Competitive Analysis: Employs real-time search queries to scan up-to-date market directories, avoiding outdated static intelligence.
- Dynamic Web Scraper (WebFetch): Deep-dives into highly relevant technical blogs and pricing pages to extract raw token costs.
- Robust Weighted Scoring Engine: Features an algorithmic decision matrix to convert complex qualitative data into quantitative feasibility metrics.
- Offline-ready HTML Reports: Generates self-contained, interactive HTML reports with built-in styling, charts, and diagrams, making them easy to share with stakeholders or integrate with other Openclaw Skills workflows.
SKILL.md
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