---

name: poka-yoke description: "Mistake-proof code, config and process: make the wrong action impossible or self-announcing rather than documented." category: development risk: safe source: rainmanjam/poka-yoke source_repo: rainmanjam/poka-yoke source_type: community date_added: "2026-08-25" author: rainmanjam tags: [mistake-proofing, code-review, api-design, guardrails, reliability] tools: [claude, cursor, codex] license: "MIT" license_source: "https://github.com/rainmanjam/poka-yoke/blob/v0.1.2/LICENSE"

Poka-Yoke: Mistake-Proofing for Software

Shigeo Shingo's insight, from the Toyota Production System: people will always make mistakes; that is not the problem worth solving. The problem is letting a mistake become a defect. So you stop trying to make humans more careful and start redesigning the work so the mistake either cannot physically happen or announces itself immediately.

A poka-yoke ("poh-kah yoh-kay", ポカヨケ) is a device: a jig, a shape, a counter: not an instruction. In software: a type, a constraint, a hook, a schema, a state machine. The single most important consequence:

A comment, a docstring, a wiki page, a code review checklist, or a line in CLAUDE.md saying "don't do X" is not a poka-yoke. It is training. Training degrades. A device does not. If your proposed fix relies on someone remembering something, keep going.

When to Use This Skill

• Use when the user says "poka-yoke this", "mistake-proof it", or "make this harder to get wrong".
• Use when designing an interface, schema or state machine and the ask is "make invalid states unrepresentable" or "so callers cannot screw it up".
• Use when auditing existing code for footguns: "what could bite us here", "what is easy to misuse".
• Use after an incident, when the fix must close the class rather than the case: "make sure this never happens again", "this is the third time".
• Especially for money, auth, permissions, deletion, migrations and pipelines, where failure is silent.

The two axes

Every real poka-yoke answers two questions. Use both when you classify a hazard or propose a device. They are the difference between this method and generic code review.

Axis 1, Regulatory function: what happens when the mistake occurs?

This is a strict preference ladder. Always reach for the highest rung you can afford.

Rung	Name	What it does	Software examples
1	Control	The mistake is impossible. The work cannot proceed.	Type won't compile · NOT NULL / CHECK / unique constraint · required function argument · private constructor + smart constructor · PreToolUse hook returns deny · protected branch
2	Warning	The mistake is possible but announced at the moment it happens.	Lint error in the editor · failing CI gate · runtime assertion that throws · confirmation prompt naming the exact thing being destroyed
3	Detection	The mistake ships, and something finds it afterward.	Tests · monitoring · alerting · reconciliation job
0	(not a poka-yoke)	Relies on a human remembering.	Docs · comments · training · "be careful" · review checklists

Shingo's rule: prefer control over warning, always, and only settle for warning when control is genuinely too expensive, then say why out loud. In software the honest reason is usually "the language can't express it" or "it would break every existing caller," and both are worth stating explicitly so the tradeoff is visible.

Axis 2, Setting function: how does the device notice?

Shingo's three detection methods map cleanly onto software. These are your inspection lenses, run all three over any interface and you will find hazards that a general code review misses.

Method	Factory floor	The question to ask code	Software devices
Contact	The part physically won't seat unless it's the right shape and orientation	Can the wrong thing fit?	Distinct types instead of shared primitives · branded/newtype IDs · parse-don't-validate at boundaries · units in the type · discriminated unions instead of bags of optionals
Fixed-value	A counter says all 6 screws were fitted	Can the wrong count or an incomplete set pass?	Exhaustive match/switch over an enum · required fields · "all migrations applied" check · row-count guard on a bulk write · checksums · config validated as a whole at boot
Motion-step	A sensor confirms step 3 happened before step 4	Can the steps happen in the wrong order, or be skipped?	Typestate · builder that cannot .build() until required steps run · state machines with illegal transitions unrepresentable · idempotency keys · RAII / defer / context managers · transactions

The third principle: inspect at the source

Shingo separated source inspection from informative inspection, which finds the defect only after it exists and comes in two forms. Ranked best first, that is three places you can put the device.

1. Source inspection: check the conditions before the error can occur. Designed in where you can, enforced at runtime where you cannot. The type, the constraint, the signature.
2. Self-check (informative): the work checks itself as it happens. Runtime. Assertions, fail-fast, validation at the boundary.
3. Successive check (informative): the next station checks the previous one. Review, CI, QA.

Push every device as far up this list as it will go. A CI gate that catches a bad migration is good; a schema that makes the bad migration unwritable is better and costs less forever.

How to use this skill

Apply the method directly to the subject in front of you. A Terraform module, a support runbook, a spreadsheet everyone edits, a release checklist, a prompt template, an onboarding process, a physical workflow: the method works on any of them, because Shingo developed it on an assembly line, for people fitting springs into switches, and not for software at all.

Applying it directly means four steps, in order:

1. Name what is being done, and by whom. A device protects a specific action taken by a specific person or system. "The pipeline" is not an action; "an engineer re-runs the deploy job after it fails halfway" is.
2. Run the three lenses over that action, can the wrong thing fit, can an incomplete or wrong-sized set pass, can the steps happen in the wrong order. Most subjects yield something on at least one.
3. For each hazard found, state it as a mistake someone could make, what happens when they do, whether it is silent, and what exists today to stop it.
4. Propose the highest-rung device you can afford, and say which rung it reaches. If you land on Warning, say what Control would have required and why you did not take it.

Then apply the two rules in How to talk about this below: name the mistake rather than the mistaken, and never let the answer come out as "be more careful" or "document it". Those are rung zero, and the whole method exists because they do not work.

If the request is bare, /poka-yoke with nothing attached, look at what is actually in front of you: the current diff, the file under discussion, the thing the conversation has been about. Say what you picked in one line before starting, so it is cheap to redirect you. If there is genuinely no subject, ask what they want mistake-proofed rather than guessing.

How to talk about this

Two habits keep the analysis honest and keep people from getting defensive:

Name the mistake, not the mistaken. "This signature lets a caller swap the two IDs" is actionable and true. "The developer should have been more careful" is neither. Shingo was emphatic that blaming the operator is how organizations avoid fixing the process. Write findings about the code's affordances, never about who wrote it.

Say which rung you achieved, and what stopped you going higher. A recommendation that reads "added a runtime assertion (warning), control would need a newtype, which touches 40 call sites" gives the reader a real decision. One that reads "added validation" does not.

Example

Suppose a destructive API accepts deleteAccount(accountId: string, tenantId: string). The two identifiers can be swapped, and the call can target an account outside the caller's tenant.

1. Contact lens: two plain strings have the same shape, so the wrong value fits.
2. Motion-step lens: deletion can run before tenant ownership is established.
3. Control device: replace the strings with distinct validated ID types and expose a deletion operation that accepts only an account loaded through the authenticated tenant.
4. Warning fallback: if compatibility prevents that interface change, reject ownership mismatches at the boundary and require a confirmation that names the exact account. State explicitly that this is weaker than making the invalid call unrepresentable.
5. Detection: retain audit logging and reconciliation for failures the control does not cover; do not present those after-the-fact checks as the poka-yoke itself.

Applying changes

Propose before you edit. Show the hazard, the proposed device, and the rung it reaches, then wait for a go-ahead before changing files: the whole point of this method is that it changes the shape of an interface, and that is precisely the kind of change people want to see first. Once approved, apply it and record the prevented mistake where future maintainers can verify the constraint without mistaking the explanation itself for the device.

The exception is when someone has explicitly asked you to write new code: mistake-proofing is the code they asked for, so build it, then narrate which hazards you designed out and why.

Limitations

• Poka-yoke reduces predictable misuse; it cannot prove that a design is correct or cover hazards the analysis never identifies.
• The strongest control may be unavailable in the current language, platform or compatibility envelope. When that happens, state the tradeoff and retain appropriate tests, monitoring and recovery paths instead of presenting a warning as complete prevention.
• A guard can itself be wrong, overbroad or operationally expensive. Validate proposed devices against real callers and failure modes, especially for destructive, financial, authentication and authorization flows.
• This method complements, but does not replace, domain review, security review, testing, observability or incident response.