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sck_0 b5675d55ce feat: Add 57 skills from vibeship-spawner-skills

Ported 3 categories from Spawner Skills (Apache 2.0):
- AI Agents (21 skills): langfuse, langgraph, crewai, rag-engineer, etc.
- Integrations (25 skills): stripe, firebase, vercel, supabase, etc.
- Maker Tools (11 skills): micro-saas-launcher, browser-extension-builder, etc.

All skills converted from 4-file YAML to SKILL.md format.
Source: https://github.com/vibeforge1111/vibeship-spawner-skills

2026-01-19 12:18:43 +01:00

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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:

Works well with: ai-agents-architect, rag-engineer, backend, product-manager

2.6 KiB Raw Blame History