prompt-engineer

Original：🇺🇸 English

Translated

Design, test, and optimize prompts for LLM interactions. Cover prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques. Triggers on "write prompt", "optimize prompt", "design system prompt", "few-shot examples", "chain of thought", "prompt evaluation", "LLM output formatting", "prompt testing", or "prompt patterns".

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Sourcedaemon-blockint-tech/agentic-enteprises-skill

Added on2026-05-20

NPX Install

npx skill4agent add daemon-blockint-tech/agentic-enteprises-skill prompt-engineer

SKILL.md Content

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Prompt Engineer

Overview

Design, test, and optimize prompts for LLM interactions. This skill covers prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques.

Features

Prompt patterns: few-shot, zero-shot, chain-of-thought, ReAct, self-consistency
System prompt design: role definition, constraints, output format specification
Output formatting: JSON, XML, markdown, structured templates
Prompt evaluation: quality metrics, consistency testing, edge case analysis
Prompt optimization: token reduction, clarity improvement, robustness testing

Usage

Identify the user's prompt need (pattern selection, system prompt, output format, or optimization)
Follow the corresponding workflow below
Produce structured outputs: prompt templates, system prompts, output schemas, or evaluation reports

Examples

User: "Write a prompt for summarization" Agent: Runs Prompt Design workflow, selects zero-shot pattern, defines role and constraints, produces prompt with output format
User: "Optimize this prompt" Agent: Runs Prompt Optimization workflow, identifies ambiguity, reduces token count, adds clarity, tests edge cases
User: "Evaluate prompt quality" Agent: Runs Prompt Evaluation workflow, tests against quality metrics, identifies failure modes, produces improvement recommendations

When to Use

Designing, versioning, and evaluating prompts for LLM-powered features
Building agent workflows (ReAct, tool use, multi-agent coordination)
Optimizing accuracy, format compliance, latency, and token cost
Deploying guardrails, observability, and abuse defenses for GenAI in production

When NOT to Use

Classical ML model training, feature engineering, or statistical A/B tests → use
```
data-scientist
```
General technical writing, API reference, or runbooks → use
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tech-writer-researcher
```
Cloud infrastructure, CI/CD, or Kubernetes operations → use
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infrastructure-engineer
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Revenue recognition or finance close procedures → use
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senior-revenue-accountant
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Multi-feature token reduction roadmap → use
```
ai-token-improvement-plan-engineer
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Rigorous token-efficiency experiments and ablations → use
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research-engineer-scientist-tokens
```

Core Workflows

1. Prompt Design Workflow

Step-by-step process:

Define the task clearly
- What input does the user provide?
- What output format is required?
- What constraints must be enforced?

Choose the pattern

Pattern	When	Structure
Zero-shot	Simple, well-defined tasks	Instructions + input
Few-shot	Pattern recognition, formatting	Examples + task
Chain-of-thought	Reasoning, math, logic	"Let's think step by step"
Role-based	Domain expertise needed	"You are a senior X..."
Structured	API/programmatic consumption	JSON schema, XML template

Draft and iterate
- Start simple, add complexity only where needed
- Use clear separators (###, XML tags, markdown)
- Specify output format explicitly
- Include constraints and what to avoid
Test with edge cases
- Empty input, malformed input, adversarial input
- Boundary conditions
- Multiple languages or formats

2. Prompt Optimization & Testing

Evaluation dimensions:

Accuracy: Does it produce correct results? (human or model judge)
Consistency: Same input → same output? (temperature, seed control)
Format compliance: Does output match the schema? (JSON validator)
Latency: Time to first token, total generation time
Cost: Tokens consumed (input + output)

Testing workflow:

Build a benchmark dataset (50-200 diverse examples)
Establish baseline with current prompt
Modify one variable at a time (prompt, model, temperature)
Run A/B comparison on benchmark
Measure and document improvement

3. Agent Orchestration

Agent patterns:

Pattern	When	Components
ReAct	Tool-using agent	Reasoning + Action + Observation loop
Plan-and-Solve	Multi-step tasks	Planner → Executor → Checker
Reflexion	Self-improvement	Execute → Evaluate → Revise
Multi-agent	Complex workflows	Specialist agents + coordinator

Tool use checklist:

Tool schemas are clearly defined (name, description, parameters)
Agent can handle tool failure gracefully
Tool results are summarized, not passed raw to user
Rate limits and costs are monitored

4. Production Patterns

Security checklist:

Input validated and sanitized
Prompt injection defenses in place (delimiters, output filtering)
No sensitive data in prompts (PII, secrets)
Output filtered for harmful content
Rate limiting and abuse detection

Observability:

Log all prompts and responses (with PII redaction)
Track token usage and cost per user/request
Monitor for drift in output quality
Alert on error rates and latency spikes