How do I install the Knowledge Prompt Engineering skill?

Run npx skillmds@latest add deankelly751/knowledge-prompt-engineering in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Knowledge Prompt Engineering free to use?

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Who published Knowledge Prompt Engineering?

DeanKelly751 (@deankelly751) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Knowledge Prompt Engineering by deankelly751

Name: Knowledge Prompt Engineering
Author: DeanKelly751

Knowledge Prompt Engineering

Prompt engineering patterns covering system prompts, few-shot examples, chain-of-thought, structured output, and prompt testing. Use when writing prompts for AI systems, designing agent instructions, or optimizing LLM interactions.

DeanKelly751 Updated Sep 21, 2026

File contents

Prompt Engineering

Purpose

Design prompts that produce reliable, high-quality outputs from language models.

Core Patterns

Pattern	When to use	Effect
System prompt	Always	Set role, constraints, format
Few-shot examples	Output format matters	Demonstrates expected behavior
Chain-of-thought	Complex reasoning	Improves accuracy on multi-step problems
Structured output	Parsing needed	JSON/XML schema enforcement
Step-by-step decomposition	Complex tasks	Breaks into manageable sub-tasks

System Prompt Design

Structure:

Role: Who the model is (expertise, perspective)
Task: What it should do
Constraints: What it must not do
Format: How to structure output
Examples: Representative input/output pairs

Prompt Reliability Techniques

Technique	Purpose
Be specific	Reduce ambiguity in interpretation
Provide format template	Ensure parseable output
Specify edge cases	"If X is missing, do Y"
Add constraints	"Never include PII", "Maximum 3 sentences"
Request self-verification	"Check your answer against..."

Common Failure Modes and Fixes

Problem	Fix
Inconsistent format	Add explicit format template + few-shot
Hallucinated data	Request citations, add "if unsure, say so"
Too verbose	"Be concise. Maximum N sentences."
Ignores constraints	Move constraints to end (recency bias) or repeat
Poor reasoning	Add chain-of-thought, decompose problem

Prompt Testing

Test with diverse inputs (not just happy path)
Check edge cases (empty input, very long input, ambiguous input)
Measure consistency (same input multiple times)
Track prompt versions (like code versions)

Anti-Patterns

Mega-prompts: Thousands of tokens of instructions (diminishing returns)
Conflicting instructions: Contradictory rules that confuse the model
Assumed context: Instructions that depend on information not provided
No examples for complex formats: Expecting specific structure without showing it

Related Skills

ai-workflows/knowledge-agent-design — prompts for agent systems
ai-workflows/knowledge-guardrails — safety constraints in prompts

---name: knowledge-prompt-engineeringdescription: Prompt engineering patterns covering system prompts, few-shot examples, chain-of-thought, structured output, and prompt testing. Use when writing prompts for AI systems, designing agent instructions, or optimizing LLM interactions.---# Prompt Engineering## PurposeDesign prompts that produce reliable, high-quality outputs from language models.## Core Patterns| Pattern | When to use | Effect ||---------|-------------|--------|| System prompt | Always | Set role, constraints, format || Few-shot examples | Output format matters | Demonstrates expected behavior || Chain-of-thought | Complex reasoning | Improves accuracy on multi-step problems || Structured output | Parsing needed | JSON/XML schema enforcement || Step-by-step decomposition | Complex tasks | Breaks into manageable sub-tasks |## System Prompt DesignStructure:1. **Role**: Who the model is (expertise, perspective)2. **Task**: What it should do3. **Constraints**: What it must not do4. **Format**: How to structure output5. **Examples**: Representative input/output pairs## Prompt Reliability Techniques| Technique | Purpose ||-----------|---------|| Be specific | Reduce ambiguity in interpretation || Provide format template | Ensure parseable output || Specify edge cases | "If X is missing, do Y" || Add constraints | "Never include PII", "Maximum 3 sentences" || Request self-verification | "Check your answer against..." |## Common Failure Modes and Fixes| Problem | Fix ||---------|-----|| Inconsistent format | Add explicit format template + few-shot || Hallucinated data | Request citations, add "if unsure, say so" || Too verbose | "Be concise. Maximum N sentences." || Ignores constraints | Move constraints to end (recency bias) or repeat || Poor reasoning | Add chain-of-thought, decompose problem |## Prompt Testing- Test with diverse inputs (not just happy path)- Check edge cases (empty input, very long input, ambiguous input)- Measure consistency (same input multiple times)- Track prompt versions (like code versions)## Anti-Patterns- **Mega-prompts**: Thousands of tokens of instructions (diminishing returns)- **Conflicting instructions**: Contradictory rules that confuse the model- **Assumed context**: Instructions that depend on information not provided- **No examples for complex formats**: Expecting specific structure without showing it## Related Skills- `ai-workflows/knowledge-agent-design` — prompts for agent systems- `ai-workflows/knowledge-guardrails` — safety constraints in prompts

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