Inversion Exercise
Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"
10 stars
byBlurjp
Best use case
Inversion Exercise is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"
Teams using Inversion Exercise should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/inversion-exercise/SKILL.md --create-dirs "https://raw.githubusercontent.com/Blurjp/ImagePrepMCP/main/.claude/skills/superpowers-problem-solving/inversion-exercise/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/inversion-exercise/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Inversion Exercise Compares
| Feature / Agent | Inversion Exercise | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Inversion Exercise ## Overview Flip every assumption and see what still works. Sometimes the opposite reveals the truth. **Core principle:** Inversion exposes hidden assumptions and alternative approaches. ## Quick Reference | Normal Assumption | Inverted | What It Reveals | |-------------------|----------|-----------------| | Cache to reduce latency | Add latency to enable caching | Debouncing patterns | | Pull data when needed | Push data before needed | Prefetching, eager loading | | Handle errors when occur | Make errors impossible | Type systems, contracts | | Build features users want | Remove features users don't need | Simplicity >> addition | | Optimize for common case | Optimize for worst case | Resilience patterns | ## Process 1. **List core assumptions** - What "must" be true? 2. **Invert each systematically** - "What if opposite were true?" 3. **Explore implications** - What would we do differently? 4. **Find valid inversions** - Which actually work somewhere? ## Example **Problem:** Users complain app is slow **Normal approach:** Make everything faster (caching, optimization, CDN) **Inverted:** Make things intentionally slower in some places - Debounce search (add latency → enable better results) - Rate limit requests (add friction → prevent abuse) - Lazy load content (delay → reduce initial load) **Insight:** Strategic slowness can improve UX ## Red Flags You Need This - "There's only one way to do this" - Forcing solution that feels wrong - Can't articulate why approach is necessary - "This is just how it's done" ## Remember - Not all inversions work (test boundaries) - Valid inversions reveal context-dependence - Sometimes opposite is the answer - Question "must be" statements
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