---

name: question-answering description: Answer questions accurately and concisely based on knowledge and context. Use when the user asks questions, requests information, or needs clarification on any topic. license: Apache-2.0 metadata: author: paracle version: "1.0.0" category: communication level: intermediate display_name: "Question Answering" tags: - qa - conversation - knowledge - information capabilities: - factual_responses - context_understanding - clarification - follow_up_questions

Question Answering Skill

When to use this skill

Use this skill when:

• User asks direct questions requiring factual answers
• Clarification is needed on concepts or topics
• Information lookup or knowledge retrieval is required
• Follow-up questions arise during conversation

How it works

This skill enables agents to provide accurate, well-structured answers by:

1. Understanding the question - Parse intent and identify key information needs
2. Retrieving knowledge - Access relevant information from context or knowledge base
3. Formulating response - Structure answer clearly and concisely
4. Validating accuracy - Ensure factual correctness when possible

Best practices

1. Be concise and direct

Copy❌ Bad: "Well, that's an interesting question. Let me think about this. You know, there are many factors to consider..."

✓ Good: "The answer is X because Y."

2. Provide context when needed

Copy❌ Bad: "42"

✓ Good: "42 is the answer to life, the universe, and everything according to Douglas Adams' 'The Hitchhiker's Guide to the Galaxy'"

3. Admit uncertainty

Copy❌ Bad: [Making up information]

✓ Good: "I don't have enough information to answer that accurately. Could you provide more context?"

4. Structure complex answers

For multi-part questions:

1. Break down the question into components
2. Answer each part clearly
3. Summarize key points

Example:

Question: "What is Python and why is it popular?"

Answer:

• What: Python is a high-level, interpreted programming language
• Why popular:
  • Easy to learn and read
  • Versatile (web, data science, automation)
  • Large ecosystem of libraries
  • Strong community support

Examples

Example 1: Factual question

Q: "What is the capital of France?" A: "Paris"

Example 2: Conceptual question

Q: "What is machine learning?" A: "Machine learning is a subset of AI where systems learn from data to improve performance without explicit programming. It involves training algorithms on datasets to recognize patterns and make predictions."

Example 3: How-to question

Q: "How do I reverse a string in Python?" A:

Copy# Method 1: Slicing (most Pythonic)
text = "hello"
reversed_text = text[::-1]  # "olleh"

# Method 2: reversed() function
reversed_text = ''.join(reversed(text))

# Method 3: Loop (explicit)
reversed_text = ''
for char in text:
    reversed_text = char + reversed_text

Example 4: Comparison question

Q: "What's the difference between lists and tuples in Python?" A:

Common patterns

Pattern 1: Definition questions

Format: "Term is [definition]. It [purpose/context]."

Pattern 2: Process questions

Format:

1. Step 1: [action]
2. Step 2: [action]
3. Result: [outcome]

Pattern 3: Comparison questions

Format: Use tables or bullet points to compare side-by-side

Pattern 4: Troubleshooting questions

Format:

• Problem: [issue]
• Cause: [reason]
• Solution: [fix]

When to ask for clarification

Ask for more details when:

• Question is ambiguous or has multiple interpretations
• Missing critical context to provide accurate answer
• User terminology is unclear
• Scope of question is too broad

Example responses:

• "Could you clarify what you mean by [term]?"
• "Are you asking about [option A] or [option B]?"
• "What's your use case? This will help me provide a more relevant answer."

Quality checks

Before responding, ensure:

• ✓ Question is fully understood
• ✓ Answer is accurate and factual
• ✓ Response is appropriate length (not too verbose)
• ✓ Examples provided when helpful
• ✓ Sources cited if making specific claims

Limitations

This skill:

• Does not access real-time information unless tools are available
• Cannot provide opinions (only factual information)
• Cannot guarantee 100% accuracy on all topics
• Works best with clear, specific questions

Related skills

• text-summarization: For condensing long answers
• code-generation: For coding-related questions
• data-analysis: For data-driven questions