| name | scqa-framework |
| description | Narrative structure for complex topics - Situation, Complication, Question, Answer |
| foundation | McKinsey SCQA framework (derived from pyramid principle) |
| use_case | Opening hooks, persuasive narratives, problem-solution content |
SCQA Framework Skill
Foundation: McKinsey's SCQA (Situation-Complication-Question-Answer) framework, derived from Barbara Minto's pyramid principle
Core Concept: Build compelling narrative by establishing situation, introducing complication, raising question, then delivering answer.
Why This Works:
- Engages reader with familiar situation
- Creates tension with complication (problem)
- Raises question reader now wants answered
- Delivers answer with impact (reader is primed)
- Natural storytelling flow
The SCQA Structure
Four elements in sequence:
1. Situation (S)
What: The stable, uncontroversial starting point everyone agrees on
Purpose: Establish common ground with reader
Example:
"Software engineers use AI coding assistants to boost productivity."
Characteristics:
- Non-controversial (reader nods along)
- Familiar to target audience
- Sets the stage for complication
2. Complication (C)
What: The problem, change, or challenge that disrupts the situation
Purpose: Create tension and make reader care
Example:
"But generic AI agents produce unreliable code - state corruption, context loss, hallucinations. Teams abandon them after weeks of frustration."
Characteristics:
- Introduces conflict/problem
- Makes status quo untenable
- Resonates with audience pain
- Creates urgency
3. Question (Q)
What: The question reader now wants answered (often implicit)
Purpose: Focus attention on the answer you're about to provide
Example:
"How can we get AI productivity benefits without the reliability chaos?"
Characteristics:
- Natural question arising from complication
- What reader is now thinking
- Can be explicit or implicit
- Sets up your answer
4. Answer (A)
What: Your solution, recommendation, or core message
Purpose: Deliver the answer reader is now primed to receive
Example:
"CAF provides production-grade architecture that prevents AI chaos through stateless, file-based patterns proven over 6 months."
Characteristics:
- Directly addresses question
- This is your core message
- Reader is now receptive
- Rest of content supports this answer
Complete Example
Topic: Introducing CAF
Situation:
Claude Code has become a popular AI coding assistant, with developers using it daily for everything from bug fixes to feature development.
Complication:
However, Claude Code out-of-box is a generic assistant. It doesn't understand your domain, workflows, or constraints. As projects grow complex, generic responses become frustrating - developers spend more time correcting AI suggestions than coding.
Question (implicit):
How can we customize Claude Code for our specific domain without losing the ease of the IDE-based experience?
Answer:
The Claude Agent Framework (CAF) transforms Claude Code into domain-specific agents through simple markdown-based customization. Define domain commands, specialized agents, and behavioral skills - no programming required.
Variations
SCQ (Implicit Answer)
When to use: Answer is your entire document
Structure:
- Situation
- Complication
- Question
- [Your entire article/doc is the answer]
Example opening:
Situation: AI coding assistants promise productivity gains.
Complication: Generic assistants don't understand domain specifics.
Question: How do we customize AI for our domain?
[Rest of white paper answers this question]
SCA (Implicit Question)
When to use: Question is obvious from complication
Structure:
- Situation
- Complication
- Answer (question implied)
Example:
Situation: Developers use AI assistants daily.
Complication: But generic AI produces unreliable code - state corruption, context loss.
Answer: CAF prevents this through stateless architecture.
[Implied question: "How do we prevent AI reliability issues?"]
CSA (Situation Assumed)
When to use: Audience already knows situation
Structure:
- Complication (jump right to problem)
- [Situation implied]
- Answer
Example:
Complication: Generic AI assistants keep producing unreliable code.
Answer: Domain-specific customization through CAF solves this.
[Implied situation: "We're using AI assistants"]
Application to Content Types
Application 1: Opening Hook
Use SCQA for article/document opening:
# [Title]
[Situation - 1 paragraph]
Establish the current state that readers recognize.
[Complication - 1-2 paragraphs]
Introduce the problem or challenge that disrupts equilibrium.
Make readers feel the pain.
[Question - optional, can be implicit]
What readers are now asking.
[Answer - 1 paragraph]
Your core message / solution / recommendation.
This is what the rest of the document will elaborate on.
---
[Rest of document provides evidence and detail for the answer]
Application 2: Persuasive Narrative
Structure for convincing readers:
Situation: "Here's how things are..."
Complication: "But this problem exists..."
Question: "So what should we do?"
Answer: "We recommend [solution]"
Body: Provide evidence for answer
Closing: Reinforce answer + call to action
Application 3: Problem-Solution Content
SCQA maps to problem-solution:
- Situation + Complication = Problem definition
- Question = Bridge to solution
- Answer = Solution statement
- Body = Solution elaboration
Application 4: Positioning Statement
SCQA for competitive positioning:
Situation: "Current alternatives exist (Alt A, Alt B)"
Complication: "But they have these limitations..."
Question: "What's needed?"
Answer: "We provide [unique value] that alternatives don't"
Integration with Pyramid Principle
SCQA + Pyramid work together:
SCQA: How to structure the opening/hook (narrative flow)
Pyramid: How to structure the answer and supporting arguments (hierarchical)
Combined structure:
Opening (SCQA):
├─ Situation
├─ Complication
├─ Question
└─ Answer [This is Level 1 of pyramid]
Body (Pyramid):
├─ Argument 1 supporting Answer [Level 2]
│ └─ Evidence [Level 3]
├─ Argument 2 supporting Answer [Level 2]
│ └─ Evidence [Level 3]
└─ Argument 3 supporting Answer [Level 2]
└─ Evidence [Level 3]
Closing:
└─ Reinforce Answer + CTA
SCQA gets reader to care about your answer. Pyramid organizes proof of answer.
Crafting Effective Components
Crafting Situation
Good situation:
- ✅ Familiar to audience
- ✅ Non-controversial
- ✅ Concise (1 paragraph)
- ✅ Sets stage for complication
Bad situation:
- ❌ Controversial (audience disagrees)
- ❌ Unfamiliar (audience confused)
- ❌ Too long (reader loses interest)
- ❌ Doesn't connect to complication
Crafting Complication
Good complication:
- ✅ Resonates with audience pain
- ✅ Creates urgency
- ✅ Makes status quo untenable
- ✅ Leads naturally to question
Bad complication:
- ❌ Audience doesn't relate
- ❌ No urgency (so what?)
- ❌ Too dramatic (not believable)
- ❌ Doesn't set up your answer
Crafting Question
Good question:
- ✅ Natural from complication
- ✅ What reader is thinking
- ✅ Your answer addresses it
- ✅ Can be implicit (often better)
Bad question:
- ❌ Disconnected from complication
- ❌ Too broad or vague
- ❌ Your answer doesn't address it
- ❌ Stated explicitly when obvious
Crafting Answer
Good answer:
- ✅ Directly addresses question
- ✅ Your core message
- ✅ Concise and memorable
- ✅ Rest of content supports it
Bad answer:
- ❌ Doesn't answer question
- ❌ Vague or generic
- ❌ Too complex (save detail for body)
- ❌ Not your actual core message
Common Mistakes
Mistake 1: Starting with Complication
❌ Don't jump to problem without context:
AI coding assistants produce unreliable code...
Why: Reader doesn't have context. What AI assistants? Who's using them?
✅ Establish situation first:
Software engineers use AI coding assistants daily. But these assistants produce unreliable code...
Mistake 2: Weak Complication
❌ Complication doesn't create urgency:
Situation: Developers use AI assistants.
Complication: Sometimes the suggestions could be better.
Why: "Could be better" doesn't create enough tension.
✅ Strong complication with real pain:
Situation: Developers use AI assistants.
Complication: But 40% abandon them within weeks due to state corruption and unreliable output - wasted time, broken code, lost trust.
Mistake 3: Answer Doesn't Match Question
❌ Mismatched Q&A:
Question: How do we make AI assistants more reliable?
Answer: CAF provides markdown-based customization.
Why: Answer talks about customization, not reliability.
✅ Matched Q&A:
Question: How do we make AI assistants more reliable?
Answer: CAF prevents failures through stateless architecture and file-based persistence.
Mistake 4: Answer Too Complex
❌ Answer tries to explain everything:
Answer: CAF is a meta-framework that provides commands, agents, and skills organized in a plugin architecture with markdown-based configuration that allows...
Why: Too much detail. Save for body.
✅ Concise answer, details later:
Answer: CAF transforms Claude Code into domain-specific agents through markdown-based customization.
[Body provides details about commands, agents, skills]
Quality Checklist
When applying SCQA, verify:
Integration with Positioning
SCQA + Positioning Manifest:
From positioning manifest:
- Core message = Your Answer (A)
- Audience context/pain = Informs Complication (C)
- Desired action = Influences how you state Answer
Example:
{
"core_message": "CAF transforms Claude Code into domain agents",
"audience_context": "Developers frustrated by generic AI assistants"
}
Becomes SCQA:
- S: Developers use AI assistants
- C: But generic assistants frustrate with domain-ignorant responses
- Q: How to get domain-specific AI?
- A: CAF transforms Claude Code into domain agents
References
Foundation: McKinsey's SCQA framework, derived from Barbara Minto's pyramid principle
Key insight: People engage with stories that start where they are (Situation), introduce tension (Complication), raise a question they want answered (Question), then deliver the answer (Answer).
Application: Use SCQA for openings and persuasive narratives. Use Pyramid for organizing supporting evidence.
Skill Version: 1.0
Created: 2025-10-31
Used by: author agent (optional, for narrative structure)
Key Innovation: Narrative engagement that primes reader to receive core message
