Question	Why It Matters
Platform?	Claude API / OpenAI API / Gemini API / CLAUDE.md / ChatGPT / n8n
Model?	Opus / Sonnet / Haiku / GPT-4o / GPT-5 / Gemini
Prompt type?	System prompt / User prompt / Both
Goal?	What should the AI do?
Current issues?	What's not working?

Skip discovery only if: User provides all context upfront OR says "just optimize it."

Phase 2: DIAGNOSE

Score against platform-appropriate criteria (see checklist.md).

Phase 3: OPTIMIZE

Apply platform-specific techniques. Show before/after for each change.

Phase 4: VALIDATE

Verify no conflicting instructions and structure is coherent.

Quick Reference

System Prompt vs User Prompt

Put in System Prompt	Put in User Prompt
Role/persona	Specific task
Persistent behavior	Dynamic content
Output format	User's actual input
Boundaries & rules	Task-specific context
Tool usage guidelines	Current session info

Rule: Consistency across sessions → System. Specific to this interaction → User.

Platform Quick Guide

Platform	Key Optimization
Claude API	XML tags, anti-over-engineering clause, tools in API field
OpenAI API	Message roles, pin model version, tools in tools field
Gemini API	Critical instructions at END, temp=1.0, knowledge cutoff
CLAUDE.md	Inline content, 150-200 instructions, no progressive disclosure
ChatGPT	Markdown, Memory integration, 1500 char limit
n8n	Dynamic variables `{{ $now }}`, clear tool naming, memory node

Model-Specific Additions

Model	Required Addition
Claude Opus 4.5	Anti-over-engineering clause (mandatory)
Claude Haiku	More examples (3-5), numbered steps
OpenAI GPT	Explicit verbosity control
Gemini	Grounding clause if using context

Core Techniques

1. Structure by Platform

Claude (API & CLAUDE.md): XML tags

<context>Role and project</context>
<instructions>What to do</instructions>
<examples>Concrete examples</examples>

OpenAI/ChatGPT: Markdown with clear sections

## Role
You are a helpful assistant...

## Instructions
1. Always...
2. Never...

Gemini: XML or Markdown (pick one, don't mix)

Place critical instructions at END for long context

2. Instruction Hierarchy

Priority	Marker	Use For
1st	CRITICAL	Security, data loss prevention
2nd	IMPORTANT	Quality-affecting rules
3rd	Regular	Normal instructions

Max 2-3 CRITICAL items. If everything is critical, nothing is.

3. Anti-Over-Engineering (Opus Required)

Avoid over-engineering. Only make changes that are directly requested.
Keep solutions simple and focused. Don't add features beyond what was asked.
Don't create abstractions for one-time operations.

4. Examples (3-5 Diverse)

<example>
User: "Add caching"
Bad: "Adding Redis..."
Good: "What's the bottleneck? Let me profile first."
</example>

5. Tables Over Paragraphs

| Tool | Purpose | Flag |
|------|---------|------|
| sql_query.py | Query | --schema |
| sql_write.py | Update | --save |

Anti-Patterns

Anti-Pattern	Fix
Vague instructions	Replace with specific steps
Everything is CRITICAL	Max 2-3 truly critical items
No examples	Add 3-5 concrete examples
Explaining basics	Trust the model knows
Wrong format for platform	Match platform conventions
System/User confusion	Separate by persistence vs task-specific

Output Format

## Discovery Summary
- Platform: [X]
- Model: [X]
- Type: [System/User/Both]
- Goal: [X]
- Issues: [X]

## Score: [X/100]

## Issues Found
1. **[Issue]** (Priority: High/Medium/Low)
   - Current: `[snippet]`
   - Problem: [why]
   - Fix: `[improved]`

## Optimized Prompt
[Full optimized version]

## Changes Made
- [bullet list]

References

Reference	Use For
platforms.md	Platform-specific deep dive
system-vs-user.md	System/User prompt guidance
techniques.md	Core optimization techniques
checklist.md	Scoring checklist by platform
model-comparison.md	Model-specific behaviors

Related Skills

/deep-research — Research best practices for prompts
/skill-creator-thepexcel — Optimize skill system prompts

optimize-prompt

Prompt Optimization Consultant

Consultant Workflow

Phase 1: DISCOVER (Always First)