prompt-optimization

name

prompt-optimization

description

Guides building, reviewing, and optimizing system prompts and prompt templates for LLMs. Use when creating system prompts, writing prompt templates, optimizing prompt structure, reducing prompt token usage, compressing prompts, improving prompt clarity, reviewing prompts for safety and bias, or making prompts more token-efficient. Also use when the user says "write a prompt," "optimize this prompt," "system prompt," "prompt engineering," "make this prompt better," "reduce tokens," or "compress this prompt."

Prompt Optimization

Build effective, safe, and token-efficient system prompts for LLMs.

How to Use This Skill

Pick the workflow that matches the task, then follow it end-to-end:

Task	Workflow	Reference
Write a new prompt from scratch	Create workflow	references/workflows.md → Workflow 1
Review/audit an existing prompt	Review workflow	references/workflows.md → Workflow 2
Reduce token count of a working prompt	Optimize workflow	references/workflows.md → Workflow 3

Each workflow references the specific detail pages below as needed. Do not read all references upfront — load them when the workflow step calls for them.

Core Principles

1. Clarity over cleverness — Write prompts a new employee would follow without confusion
2. Positive framing — Say what to do, not what to avoid
3. Structure with delimiters — XML tags or consistent separators between content types
4. Progressive specificity — Role → constraints → instructions → context → format → examples → input
5. Token economy — Every token must earn its place; ≥1 constraint per 5–10 tokens
6. Safety by default — Prevent injection, data leakage, and bias structurally

Prompt Architecture (ordering)

1. Identity / Role          — Who the model is (1–2 sentences)
2. Core behavior rules      — Non-negotiable constraints (safety, tone)
3. Task instructions        — What to do and how
4. Context / Knowledge      — Domain data, schemas, reference docs
5. Output format            — Structure, length, style requirements
6. Examples (few-shot)      — 2–3 input/output pairs in <example> tags
7. Dynamic input            — User content (always last, always delimited)

Key Rules

• Delimit user input — Wrap in tags like <user_input>; never interpolate raw
• Show, don't just tell — Few-shot examples beat format descriptions
• Match prompt style to output style — Markdown in prompt → markdown out; prose → prose
• Put long documents first — Reference data above instructions (30%+ quality gain)
• Quantify, don't qualify — "≤100 words" not "keep it short"
• Imperative voice — "Respond in JSON" not "You should format your response as JSON"
• Test adversarially — Injection, edge cases, ambiguous inputs before shipping

Reference Index

Load these when a workflow step calls for them:

Reference	Contents
references/workflows.md	End-to-end workflows for create, review, and optimize tasks
references/clarity-and-structure.md	XML tags, delimiters, role prompting, long-context patterns
references/output-control.md	Format steering, verbosity control, few-shot examples
references/safety-and-security.md	Injection prevention, bias mitigation, data leakage, red-teaming
references/compression-techniques.md	High-density writing, token pruning, compression methods, density test
references/agentic-prompts.md	Tool use, thinking/reasoning, state tracking, subagent orchestration
references/anti-patterns-and-checklist.md	8 anti-patterns, design checklist, evaluation metrics