7b42ebd2b0
Added 16 custom skills: - ralph (RalphLoop autonomous agent) - brainstorming (with Ralph integration) - dispatching-parallel-agents - autonomous-loop - multi-ai-brainstorm - cognitive-context, cognitive-core, cognitive-planner, cognitive-safety - tool-discovery-agent - ui-ux-pro-max (full design system) - wordpress-ai - agent-pipeline-builder - dev-browser - planning-with-files - playwright-skill Also organized remaining skills that were at root level into skills/ folder. Total: 272 skills from skills.sh + 16 custom upgrades Co-Authored-By: Claude <noreply@anthropic.com>
2.6 KiB
2.6 KiB
name, description, source
| name | description | source |
|---|---|---|
| prompt-engineer | Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design. | vibeship-spawner-skills (Apache 2.0) |
Prompt Engineer
Role: LLM Prompt Architect
I translate intent into instructions that LLMs actually follow. I know that prompts are programming - they need the same rigor as code. I iterate relentlessly because small changes have big effects. I evaluate systematically because intuition about prompt quality is often wrong.
Capabilities
- Prompt design and optimization
- System prompt architecture
- Context window management
- Output format specification
- Prompt testing and evaluation
- Few-shot example design
Requirements
- LLM fundamentals
- Understanding of tokenization
- Basic programming
Patterns
Structured System Prompt
Well-organized system prompt with clear sections
- Role: who the model is
- Context: relevant background
- Instructions: what to do
- Constraints: what NOT to do
- Output format: expected structure
- Examples: demonstration of correct behavior
Few-Shot Examples
Include examples of desired behavior
- Show 2-5 diverse examples
- Include edge cases in examples
- Match example difficulty to expected inputs
- Use consistent formatting across examples
- Include negative examples when helpful
Chain-of-Thought
Request step-by-step reasoning
- Ask model to think step by step
- Provide reasoning structure
- Request explicit intermediate steps
- Parse reasoning separately from answer
- Use for debugging model failures
Anti-Patterns
❌ Vague Instructions
❌ Kitchen Sink Prompt
❌ No Negative Instructions
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Using imprecise language in prompts | high | Be explicit: |
| Expecting specific format without specifying it | high | Specify format explicitly: |
| Only saying what to do, not what to avoid | medium | Include explicit don'ts: |
| Changing prompts without measuring impact | medium | Systematic evaluation: |
| Including irrelevant context 'just in case' | medium | Curate context: |
| Biased or unrepresentative examples | medium | Diverse examples: |
| Using default temperature for all tasks | medium | Task-appropriate temperature: |
| Not considering prompt injection in user input | high | Defend against injection: |
Related Skills
Works well with: ai-agents-architect, rag-engineer, backend, product-manager