Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition.
SkillScore95Maintained
Automatic
◆AI Engineer pack
Last verified 8/21/2026
What it changes
Passed 21 of 22 test cases with the skill, and 18 of 22 without it.
Without skill82%
With skill95%
Checks it passed that the model alone missed
Tracks consistency
Specifies testing against happy path, edge case, and adversarial inputs
Recommends applying a new optimization strategy with single-variable testing
3 fixed18 passed either way1 still failing0 broke
Pass-rate lift
+14 pp
Tokens
+68%
Agent turns
0%
Response time
-37%
Model gemini-3.6-flashMeasured 8/21/2026v121 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 3
Prompt
When optimizing an existing prompt, if the accuracy reaches or exceeds baseline performance but remains under 90%, what step should follow?
What was checked, without and with the skill
Checked
Without skill
With skill
Recommends applying a new optimization strategy with single-variable testing
Missed
Passed
case-12 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md274 lines6 files
Prompt Engineering
Overview
Use this skill to design prompt systems that are clear, testable, and reusable.
It covers prompt drafting, optimization, evaluation, and production-oriented
patterns for few-shot prompting, reasoning workflows, templates, and system
prompts.
Keep the main workflow in this file and load the targeted reference files only
for the pattern you are applying.
When to Use
Use this skill when:
A user asks to write, rewrite, or improve a prompt
A prompt needs better structure, reliability, or output formatting
Few-shot examples or reasoning scaffolds are needed
A system prompt or reusable prompt template must be created
An existing prompt needs measurable optimization and testing
Read the relevant files in references/ when you need deeper guidance on a
specific pattern.
Core Patterns
1. Few-Shot Learning
Example Selection Strategy
Use references/few-shot-patterns.md for comprehensive selection frameworks
Balance example count (3-5 optimal) with context window limitations
Include edge cases and boundary conditions in example sets
Prioritize diverse examples that cover problem space variations
Order examples from simple to complex for progressive learning
Few-Shot Example (Sentiment Classification)
Classify the sentiment as Positive, Negative, or Neutral.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model perf