Prompt Engineer
Design, structure, and evaluate system prompts and few-shot patterns so your coding agent reliably follows intent in real products.
Overview
Prompt Engineer is an agent skill most often used in Build (also Validate and Ship) that turns product intent into structured LLM instructions with few-shot examples and systematic evaluation.
Install
npx skills add https://github.com/davila7/claude-code-templates --skill prompt-engineerWhat is this skill?
- Structured system prompt blueprint: Role, Context, Instructions, Constraints, Output format, and Examples
- Few-shot example design: 2–5 diverse cases including edge cases with consistent formatting
- Context window management and output format specification for production prompts
- Systematic prompt testing and evaluation—not guesswork on quality
- Chain-of-thought and optimization patterns for instructions models actually follow
- Structured system prompt template covers six sections: Role, Context, Instructions, Constraints, Output format, Examples
- Few-shot pattern recommends 2–5 diverse examples including edge cases
Adoption & trust: 911 installs on skills.sh; 27.8k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your agent works in demo chat but drifts, ignores format, or fails on edge cases because prompts were never designed or tested like production code.
Who is it for?
Solo builders shipping LLM features who need repeatable system prompts and measurable prompt quality before users hit weird outputs.
Skip if: Teams that only need a single disposable chat thread with no reusable prompt spec or no plan to regression-test agent behavior.
When should I use this skill?
prompt engineering, system prompt, few-shot, chain of thought, prompt design
What do I get? / Deliverables
You get a documented system prompt architecture, vetted few-shot examples, and an evaluation habit so agent behavior stays stable across releases.
- Structured system prompt spec with constraints and output format
- Curated few-shot example set for expected and edge inputs
- Prompt evaluation notes or test cases for iteration
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Prompt architecture is where solo builders turn LLM features into dependable agent behavior during implementation. Agent-tooling is the canonical shelf because this skill targets reusable instructions, context windows, and output contracts for LLM-powered apps—not one-off chat.
Where it fits
Draft a minimal system prompt and two few-shot examples before wiring a landing-page AI demo.
Define role, constraints, and JSON output schema for a tool-calling agent in your repo.
Regression-check prompt changes against edge-case transcripts before release.
How it compares
Use for prompt-as-programming methodology—not a hosted prompt playground or a generic copywriting skill.
Common Questions / FAQ
Who is prompt-engineer for?
Indie and solo developers building agent features, internal copilots, or SaaS AI surfaces who need instructions that hold up under real inputs.
When should I use prompt-engineer?
During Build when wiring agent-tooling; during Validate when prototyping LLM UX; during Ship when reviewing whether system prompts still match shipped behavior.
Is prompt-engineer safe to install?
Review the Security Audits panel on this Prism page and inspect SKILL.md in the repo before granting network or secrets access during prompt experiments.
SKILL.md
READMESKILL.md - Prompt Engineer
# 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 ```javascript - 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 ```javascript - 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 ```javascript - 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`