Prompt Engineering
- 65 installs
- 7 repo stars
- Updated January 15, 2026
- eyadsibai/ltk
Helps with ai & agent building tasks.
About
prompt-engineering is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- prompt-engineering
- AI & Agent Building
- AI-coding skill
Prompt Engineering by the numbers
- 65 all-time installs (skills.sh)
- Ranked #6,042 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
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| Installs | 65 |
|---|---|
| repo stars | ★ 7 |
| Last updated | January 15, 2026 |
| Repository | eyadsibai/ltk ↗ |
What it does
Helps with ai & agent building tasks.
Files
Prompt Engineering Guide
Effective prompts, RAG systems, and agent workflows.
When to Use
- Optimizing LLM prompts
- Building RAG systems
- Designing agent workflows
- Creating few-shot examples
- Structuring chain-of-thought reasoning
---
Prompt Structure
Core Components
| Component | Purpose | Include When |
|---|---|---|
| Role/Context | Set expertise, persona | Complex domain tasks |
| Task | Clear instruction | Always |
| Format | Output structure | Need structured output |
| Examples | Few-shot learning | Pattern demonstration needed |
| Constraints | Boundaries, rules | Need to limit scope |
Prompt Patterns
| Pattern | Use Case | Key Concept |
|---|---|---|
| Chain of Thought | Complex reasoning | "Think step by step" |
| Few-Shot | Pattern learning | 2-5 input/output examples |
| Role Playing | Domain expertise | "You are an expert X" |
| Structured Output | Parsing needed | Specify JSON/format exactly |
| Self-Consistency | Improve accuracy | Generate multiple, vote |
---
Chain of Thought Variants
| Variant | Description | When to Use |
|---|---|---|
| Standard CoT | "Think step by step" | Math, logic problems |
| Zero-Shot CoT | Just add "step by step" | Quick reasoning boost |
| Structured CoT | Numbered steps | Complex multi-step |
| Self-Ask | Ask sub-questions | Research-style tasks |
| Tree of Thought | Explore multiple paths | Creative/open problems |
Key concept: CoT works because it forces the model to show intermediate reasoning, reducing errors in the final answer.
---
Few-Shot Learning
Example Selection
| Criteria | Why |
|---|---|
| Representative | Cover common cases |
| Diverse | Show range of inputs |
| Edge cases | Handle boundaries |
| Consistent format | Teach output pattern |
Number of Examples
| Count | Trade-off |
|---|---|
| 0 (zero-shot) | Less context, more creative |
| 2-3 | Good balance for most tasks |
| 5+ | Complex patterns, use tokens |
Key concept: Examples teach format more than content. The model learns "how" to respond, not "what" facts to include.
---
RAG System Design
Architecture Flow
Query → Embed → Search → Retrieve → Augment Prompt → Generate
Chunking Strategies
| Strategy | Best For | Trade-off |
|---|---|---|
| Fixed size | General documents | May split sentences |
| Sentence-based | Precise retrieval | Many small chunks |
| Paragraph-based | Context preservation | May be too large |
| Semantic | Mixed content | More complex |
Retrieval Quality Factors
| Factor | Impact |
|---|---|
| Chunk size | Too small = no context, too large = noise |
| Overlap | Prevents splitting important content |
| Metadata filtering | Narrows search space |
| Re-ranking | Improves relevance of top-k |
| Hybrid search | Combines keyword + semantic |
Key concept: RAG quality depends more on retrieval quality than generation quality. Fix retrieval first.
---
Agent Patterns
ReAct Pattern
| Step | Description |
|---|---|
| Thought | Reason about what to do |
| Action | Call a tool |
| Observation | Process tool result |
| Repeat | Until task complete |
Tool Design Principles
| Principle | Why |
|---|---|
| Single purpose | Clear when to use |
| Good descriptions | Model selects correctly |
| Structured inputs | Reliable parsing |
| Informative outputs | Model understands result |
| Error messages | Guide retry attempts |
---
Prompt Optimization
Token Efficiency
| Technique | Savings |
|---|---|
| Remove redundant instructions | 10-30% |
| Use abbreviations in examples | 10-20% |
| Compress context with summaries | 50%+ |
| Remove verbose explanations | 20-40% |
Quality Improvement
| Technique | Effect |
|---|---|
| Add specific examples | Reduces errors |
| Specify output format | Enables parsing |
| Include edge cases | Handles boundaries |
| Add confidence scoring | Calibrates uncertainty |
---
Common Task Patterns
| Task | Key Prompt Elements |
|---|---|
| Extraction | List fields, specify format (JSON), handle missing |
| Classification | List categories, one-shot per category, single answer |
| Summarization | Specify length, focus areas, format (bullets/prose) |
| Generation | Style guide, length, constraints, examples |
| Q&A | Context placement, "based only on context" |
---
Best Practices
| Practice | Why |
|---|---|
| Be specific and explicit | Reduces ambiguity |
| Provide clear examples | Shows expected format |
| Specify output format | Enables parsing |
| Test with diverse inputs | Find edge cases |
| Iterate based on failures | Targeted improvement |
| Separate instructions from data | Prevent injection |
Resources
- Anthropic Prompt Engineering: <https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering>
- OpenAI Cookbook: <https://cookbook.openai.com/>
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