Agent skill
research
Install this agent skill to your Project
npx add-skill https://github.com/GetBindu/Bindu/tree/main/examples/typescript-langchain-agent/skills/research
SKILL.md
Research Skill
Research and information retrieval capability powered by LangChain.js. Uses GPT-4o to answer questions, summarize information, provide detailed analysis, and generate structured research outputs.
Capabilities
Question Answering
- Direct question answering with contextual understanding
- Multi-turn conversation with history awareness
- Follow-up questions and clarification handling
Summarization
- Summarize complex topics into clear outputs
- Bullet points, paragraphs, or structured JSON
- Adjustable depth and detail level
Analysis
- Comparative analysis (pros/cons, trade-offs)
- Technical evaluation of tools and frameworks
- Architectural decision support
Code Explanation
- Explain code concepts and patterns
- TypeScript, Python, Rust, Go, Java support
- Architecture and design pattern suggestions
Examples
- "What is the current state of quantum computing?"
- "Summarize the key points of machine learning"
- "Explain the A2A protocol in simple terms"
- "Compare React vs Vue for a new project"
- "What are the best practices for API design?"
- "Analyze the pros and cons of microservices architecture"
Performance
| Metric | Value |
|---|---|
| Average response time | 1-5s (model dependent) |
| Max concurrent requests | 10 |
| Context window | Up to 128k tokens |
Requirements
- OpenAI API key (used by LangChain.js
ChatOpenAI) - Internet connection for API calls
Integration
This skill is used by the TypeScript LangChain agent example:
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({ model: "gpt-4o", temperature: 0.7 });
bindufy({
skills: ["skills/research"],
}, async (messages) => {
const response = await llm.invoke(messages);
return response.content;
});
Assessment
Keywords
research, explain, summarize, analyze, compare, question, answer, what, how, why
Specializations
- domain: research (confidence_boost: 0.3)
- domain: analysis (confidence_boost: 0.2)
- domain: summarization (confidence_boost: 0.2)
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