Poka-Yoke
Poka-Yoke is an code AI skill with a core value of Mistake-proof code so misuse cannot be expressed, rather than warning against it. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Mistake-proof code so misuse cannot be expressed, rather than warning against it. Use when designing an interface, schema, or state machine and the user wants it hard to get wrong ("make invalid state
Quick Facts
mkdir -p ./skills/poka-yoke && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/poka-yoke/SKILL.md -o ./skills/poka-yoke/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Poka-Yoke: Make the Mistake Unsayable
**People will always make mistakes. That is not the problem worth solving. The problem is
letting a mistake become a defect.**
Shigeo Shingo, a Japanese industrial engineer, worked this out on a switch assembly line in
1961. Workers kept forgetting a spring. The fix was not a reminder: the job was split so the
worker first laid both springs in a dish, then fitted them from the dish. A spring left over
was the error announcing itself, before the unit could move on.
The dish is a device. "Please remember the spring" is not.
The line that does most of the work
> A comment, a docstring, a wiki page, a review checklist, or a line in an instructions file
> saying "don't do X" is **not** a poka-yoke. It is training, and training degrades. A device
> does not. If your fix relies on someone remembering something, keep going.
This applies to your own instructions too. A rule written into a config file competes for
attention with every other rule there and loses a little more as the file grows. A check that
fails the build does not.
What this changes about the output
Given a design, models will readily list what to fix. They rarely state what the fix makes
*impossible*, and that is the difference between advice you agree with and a constraint you
can rely on. That habit is most of what this skill is for.
The other half is refusing to accept a non-device as a fix. "Add validation", "be careful with
this function", "document the invariant" are all rung zero. Each has a real device behind it,
and naming that device is the work.
Axis 1: what happens when the mistake occurs
Rank every finding on this ladder, and say which rung the current code sits on and which rung
your fix reaches.
| Rung | Name | Meaning |
|---|---|---|
| 1 | **Control** | The wrong action cannot be performed. Type error, database constraint, missing permission. |
| 2 | **Warning** | It is possible, but announces itself as it happens. A linter, a runtime assertion, a confirmation you cannot skip. |
| 3 | **Detection** | It happens, and you find out afterwards. Tests, logging, monitoring, code review. |
| 0 | **rung zero** | Telling people to be careful. Docs, comments, "please remember to". |
Detection is not failure; sometimes it is all that is available. But a plan that stops at
Detection should say so, rather than presenting it as prevention.
Axis 2: how the device notices
Shingo's three inspection lenses. They are a checklist for *finding* hazards, not decoration:
- **Contact** — can the wrong thing physically fit? Two adjacent parameters of the same type can be swapped silently. A `string` that should be one of four values. Money as a float.
- **Fixed-value** — is the set complete? A switch with no exhaustiveness check. A config where a missing key silently means "off". An enum handled in three of five places.
- **Motion-step** — is the order right, and did every step happen? A two-phase write with no transaction. A retry with no idempotency key. A resource acquired on one path and released on another.
Inspect at the source
The cheapest place to catch a mistake is where it is made, not where it surfaces. A validation
that runs three layers below the input has already let the bad value travel, and the stack
trace will point at the wrong module. Push the check to the boundary the value crosses.
Designing something new
Mistake-proofing is cheapest before the code has callers. Once it has them, every device is a
migration; before it has them, a device is free.
Work from the call site. A signature that reads fine in isolation often reads terribly where
it is used:
# the mistake is expressible: nothing stops refunding an order that was never paid
def refund(order: dict) -> Refund:
return payments.refund(order["payment_id"])
# the mistake is no longer expressible
def refund(order: PaidOrder) -> Refund: ...The moves, roughly in order of how often they apply:
**Make invalid st
🎯 Best For
- GitHub Copilot users
- Claude users
- Software engineers
- Development teams
- Tech leads
💡 Use Cases
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open GitHub Copilot or Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Poka-Yoke to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Is Poka-Yoke compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Poka-Yoke?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Poka-Yoke?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/poka-yoke/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.