Dead CSS Detection
Find the selectors that no route or component still renders, and remove them safely.
What it adds
An audit of the app's stylesheets separating selectors still reachable at runtime from those nothing renders.
What your agent is told to do
5
What your agent is told to do
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Enumerate the selectors the app ships and match them against the class names and structures the components actually produce, including every route, not only the ones that render on first load.
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Drive the audit from a real render of the app across its routes, breakpoints, and themes, rather than from static text matching alone. Static matching alone cannot see what a component composes at runtime.
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Classify each selector as confidently used, confidently unused, or unknown, and require a human decision on everything in the unknown bucket.
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Remove in small, reviewable batches, each one shippable and revertible on its own, so a regression can be traced to a specific removal instead of to a thousand-line deletion.
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Do not treat this as the same job as Unused Token Detection. That feature owns the token definitions and their references; this one owns the rules and selectors in the stylesheets, and the two reports must not both claim authority over the same file.
Edge cases it handles
7
Edge cases it handles
7- Class names composed at runtime from variables, and class names arriving inside content authored elsewhere such as a CMS body or a user-supplied rich text field, will never appear literally in the source. Both must be treated as live unless proven otherwise.
- Routes that load on demand are the classic false positive: a selector used only by a screen that is never visited during the audit will be reported as dead. Exercise every lazy route, or exclude their stylesheets from the sweep entirely.
- States that are rare or hard to reach — an error banner, a print layout, a disabled control, a high-contrast or reduced-motion variant, an empty result set — are used but almost never rendered during a crawl. Protect them explicitly rather than relying on coverage.
- Every removal batch must go through visual comparison against the previous build across the routes and breakpoints that matter, because the failure mode here is silent and cosmetic rather than a thrown error.
- Selectors that exist purely to override a third-party or vendor stylesheet look unused when the vendor markup is absent from the audit environment, and deleting them breaks the page the moment that widget loads.
- Print styles, email styles, and anything served to a context the audit never renders must be listed as out of scope in the report rather than silently swept.
- Server-rendered markup and client-rendered markup can produce different class sets for the same screen, so an audit run against only one of them is incomplete.
Definition of done
8
Definition of done
8- The audit covers every route, including those loaded on demand, plus every theme and breakpoint the app supports.
- Selectors are classified as used, unused, or unknown, and unknown is never removed automatically.
- Dynamically composed class names and class names from authored content are excluded from removal.
- Rare states such as errors, empty states, print, and accessibility variants are explicitly protected.
- Each removal batch is independently revertible and passes a visual comparison against the previous build.
- The report states which contexts were out of scope.
- 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
Model Selection
Model Selection
Let each AI task run on the model that suits its quality, speed, and cost needs.
What it does
A per-task model choice, drawn from the models the app already has configured, with capability filtering and safe defaults.
How it works
- 1 Enumerate the models the app already has access to and record what each one can actually do: context capacity, whether it can return the structured output the task requires, whether it supports the tools the task calls, and its relative cost and speed.
- 2 Offer only the models that satisfy the task's requirements. A task that needs structured output must not list a model that cannot reliably produce it, because the failure appears later as malformed responses rather than as an unavailable option.
- 3 Store the choice against the specific task, not as one global setting. A single default forces a summarisation task and a classification task onto the same tier when they have opposite needs.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/model-selection
How it works
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1
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.