UI Lint Rules
Catch the interface mistakes that reviews keep missing, before they reach a branch.
What it adds
A set of automated checks over the app's own markup and component usage for unlabelled controls, invalid variants, and unsafe interactive elements.
What your agent is told to do
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What your agent is told to do
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Start from the defects the app has actually shipped — read recent bug reports and review comments — and write rules for those, rather than adopting a large generic rule set that mostly reports things nobody intends to fix.
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Cover the categories that reviewers reliably miss: controls without accessible names, variant or size values a component does not support, click handlers on non-interactive elements, headings that skip levels, and images with no alternative text decision.
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Report each finding with the file, the line, what is wrong, and what to do instead. A rule that only names itself gets suppressed rather than fixed.
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Provide a documented suppression mechanism that requires a reason on each exception, and surface the list of active suppressions, so exceptions are visible debt rather than an invisible amnesty.
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Do not autofix anything ambiguous. Replacing a missing accessible name with a guess produces markup that passes the check and still fails the user, which is worse than the original failure because it stops being reported.
Edge cases it handles
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Edge cases it handles
7- A noisy rule set is worse than none, because the whole thing gets disabled after the first review that reports two hundred findings. Introduce rules as warnings, drive the count to zero, and only then make them errors.
- Autofixing must be limited to cases with exactly one correct outcome, such as normalising a renamed variant value; anything requiring a judgement about meaning must be reported for a human.
- Every rule needs a documented exception path with a required reason, and the exception must be scoped to the specific line rather than switching the rule off for a whole file.
- Rules must be versioned with the design system, so a component renaming its variants ships the rule change alongside it and does not break every consumer at once.
- Rules that need runtime information — computed contrast, actual focus order — cannot be answered from source alone; route those to a browser-based check and say clearly which layer owns each rule.
- Findings on generated or vendored code must be excluded by default, or the report is dominated by files nobody will edit.
- Raw colour, spacing, and radius values are the subject of Design Token Linter; consume its findings here rather than writing a second, differently-tuned set of token rules.
Definition of done
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Definition of done
9- Checks run on every change and report file, line, cause, and remedy for each finding.
- The rule set covers accessible names, invalid component variants, non-interactive click targets, and heading structure.
- Autofix applies only to cases with a single unambiguous correct result.
- Exceptions require a stated reason, are scoped to a line, and are listed somewhere visible.
- Rules are versioned with the design system and change alongside the components they check.
- The current codebase reports zero errors, so any new finding is genuinely new.
- Token-value rules are delegated to Design Token Linter rather than duplicated.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
AI Cost Budgets
AI Cost Budgets
Cap what AI features are allowed to spend before the bill arrives.
What it does
Monetary spending limits on AI work, scoped by workspace, feature, and time period, enforced before a run starts.
How it works
- 1 Find every place the app calls a model and route all of them through one accounting point that records estimated and actual spend against a scope. A budget that only covers the chat feature is not a budget.
- 2 Estimate the cost of a run from the size of its input before dispatching it, and refuse anything that would exceed the remaining budget on its own.
- 3 Reserve the estimate against the budget when the run starts, then reconcile to the real usage figures when it finishes, releasing whatever was over-reserved.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-cost-budgets
Multi-Model Routing
Multi-Model Routing
Send each AI request to the right model using rules you can read and test.
What it does
A deterministic routing layer that picks a model per request from task type, context size, latency budget, and data sensitivity.
How it works
- 1 Express routing as explicit, ordered rules over inputs the app can measure: task type, estimated context size, latency budget, and the sensitivity classification of the data involved. A rule set that can be read line by line can be reviewed and tested.
- 2 Make routing deterministic. The same inputs must always produce the same route, so a bad output can be reproduced and a rule change can be evaluated. Randomised or load-based selection turns every incident into guesswork.
- 3 Classify data before routing and refuse to route restricted content to any destination not approved for it. This check is a hard block, not a preference, and it must run before the request is assembled.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/multi-model-routing
SEO Setup
SEO Setup
Make your app findable — titles, meta, Open Graph, sitemap, robots.
What it does
The baseline SEO and social-preview setup every public app should have, and most skip.
How it works
- 1 Give every public page a unique, descriptive title and meta description. Find the app's layout and add a mechanism for each page to set them.
- 2 Add Open Graph and Twitter Card tags so shared links render a preview instead of a bare URL.
- 3 Generate a sitemap.xml covering every public, indexable page, and a robots.txt pointing at it.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/seo-setup
How it works
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Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.