Prompt Engineering Skill
Optimize AI-facing documentation using Anthropic's best practices.
When to Use
Use this skill when:
- •Creating or reviewing
CLAUDE.mdor similar AI context files - •Writing review prompts for AI assistants
- •Optimizing any documentation meant to be consumed by LLMs
- •Improving AI assistant instructions
Optimization Checklist
1. Structure & Clarity
- • Clear hierarchy: Use headers (H1 > H2 > H3) to organize content
- • Scannable format: Tables, bullet points, code blocks for quick parsing
- • Logical flow: Most important information first (inverted pyramid)
- • Consistent formatting: Same patterns throughout (tables, lists, code)
- • No ambiguity: Every instruction has one clear interpretation
2. Context & Specificity
- • Project context: What is this project? What does it do?
- • Directory structure: Where are important files?
- • Terminology defined: Project-specific terms explained
- • Explicit constraints: What should NOT be done?
- • Success criteria: What does "done well" look like?
3. Instructions & Tasks
- • Sequential steps: Numbered lists for multi-step processes
- • Specific commands: Exact commands to run, not vague descriptions
- • Edge cases covered: What to do in unusual situations
- • Examples provided: Show, don't just tell
- • Boundaries clear: When to stop, when to ask for help
4. Examples (Multishot Patterns)
- • 3-5 diverse examples: Cover different scenarios
- • Realistic examples: Mirror actual use cases
- • Edge cases included: Show handling of unusual inputs
- • Tagged consistently: Use
<example>tags for clarity - • Input + Output pairs: Show what goes in and what comes out
5. XML Tag Usage
| Tag | Purpose | Example |
|---|---|---|
<instructions> | Main task instructions | Workflow steps |
<context> | Background information | Project description |
<constraints> | Boundaries and limits | What NOT to do |
<examples> | Sample inputs/outputs | 3-5 diverse examples |
<format> | Output format spec | JSON schema, table format |
<data> | Input data to process | Files, content |
Anti-Patterns to Fix
Vague Instructions
markdown
<!-- BAD --> Make the code better. <!-- GOOD --> Refactor the function to: 1. Extract repeated logic into a helper function 2. Add TypeScript types to all parameters 3. Handle the edge case where input is empty array
Missing Context
markdown
<!-- BAD --> Update the module. <!-- GOOD --> Update module-45.md (A2 level, Ukrainian grammar): - Located at: curriculum/l2-uk-en/modules/module-45.md - Format: See docs/MARKDOWN-FORMAT.md - Requirements: 8+ activities, 22-30 vocab words
Ambiguous Boundaries
markdown
<!-- BAD --> Add some examples. <!-- GOOD --> Add exactly 5 example sentences: - Each 6-10 words (A2 level complexity) - Include Ukrainian + English translation - Cover different use cases of the grammar point
No Examples
markdown
<!-- BAD --> Format the vocabulary table correctly. <!-- GOOD --> Format the vocabulary table like this: | Slovo | Vymova | Pereklad | ChM | Prymitka | |-------|--------|----------|-----|----------| | **knyha** | /kniha/ | book | im. | zhin. rid |
Optimization Process
- •
Analyze current document
- •Identify vague/ambiguous sections
- •Find missing context or examples
- •Check structure and hierarchy
- •
Apply checklist
- •Go through each section above
- •Mark items as done or needing work
- •
Rewrite problem areas
- •Use templates as guides
- •Add examples where missing
- •Make instructions specific and sequential
- •
Test with fresh context
- •Imagine reading the document with no prior knowledge
- •Could someone follow instructions exactly?
- •Are all edge cases covered?
- •
Iterate based on results
- •If AI produces wrong output, fix the prompt
- •Add examples of the failure case
- •Clarify ambiguous instructions