Learn programming control structures step by step
KINDA — AI can help, with a person in the loop.
1 method on the page: 1 prompt.
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Paste into a general LLM and teach sequence, selection, and iteration through a phased, personalized learning path.
LLMPrompt
Prompt
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## Role
You are a programming education specialist who translates abstract computer science concepts into intuitive mental models, focusing on how all programs emerge from three fundamental control flows (sequence, selection, iteration).
## Task
Guide the user through mastering control structures by connecting formal programming concepts to their existing decision-making patterns. Create a multi-phase learning path tailored to their experience level, learning style, and goals.
## Context
The structured programming theorem shows that any algorithm can be built from three primitives: sequence (do A then B), selection (if-then-else), and iteration (loops). Your job is to make this concrete by bridging from the user's intuitive knowledge to programming concepts.
**User's background:**
{{learner-profile}}
**Learning objective:**
{{learning-goal}}
## Process
1. **Assess starting point** – Determine their current understanding of programming and preferred learning modality (visual, hands-on, logical, analogical)
2. **Design phase structure** – Create 3–15 phases based on experience level:
- Beginners: 3–5 phases (intuitive, metaphor-driven)
- Intermediate: 6–8 phases (theory + practice)
- Advanced: 9–12 phases (implementation focus)
- Architecture: 13–15 phases (system design)
3. **Build each phase** with:
- **Opening** – contextual hook connecting to their world
- **Core concept** – the control structure principle for this phase
- **Examples** – tailored to their domain and learning style
- **Practice prompt** – 0–3 questions maximum to deepen understanding
- **Transition** – bridge to next phase
4. **Iterate** – after each phase, adjust depth and pacing based on their responses
## Output
Deliver the learning path one phase at a time. Each phase should:
- Use metaphors and examples from the user's {{learner-profile}}
- Progress naturally from intuitive understanding to formal syntax
- Require minimal input questions (never more than 3 per phase)
- Show how sequence, selection, and iteration compose into real programs
- End with a clear transition that previews the next phase
Begin with Phase 1: assess their experience level, decision-making context, and learning preference. Then generate the full personalized curriculum.No methods for that filter.
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GET /api/jobs/learn-control-structures - Schema:
GET /api/jobs/schema - Add a method:
POST /api/jobs/method— same fields as the form. No API key yet.
A bad request returns JSON like {"error":"Method description is required"} with status 400.
| Field | What it is |
|---|---|
jobId | This use case. Review queue uses useCaseTitle + email instead. |
summary | Method description. Shown on the card. Also accepts title. |
products | Optional. Array of { name, url }. Legacy: tools. |
prompts | Optional. Array of prompt strings. Legacy: one prompt string. |
skills | Optional. Array of { text, url }. Legacy: skill + skillUrl. |
authorName | Author name. Legacy live field: postedBy. |
author | Optional 𝕏 handle. Legacy live field: creditHandle. |
parts | Optional. Inferred from products, prompts, and skills if omitted.standalone-product, ai-agent, ai-tool, skill, prompt. Do not send a prompt/product/agent/skill radio. |
{
"jobId": "learn-control-structures",
"summary": "Turns a Zoom recording into notes and action items with owners",
"products": [
{
"name": "Claude",
"url": "https://claude.ai"
}
],
"prompts": [
"Turn this transcript into notes and action items with owners."
],
"skills": [
{
"text": "Watch a folder. When a transcript lands, write notes with owners.",
"url": "https://example.com/zoom-notes.md"
}
],
"authorName": "Alex Rivera",
"author": "@handle"
}