Multi-Model Routing
Send each AI request to the right model using rules you can read and test.
What it adds
A deterministic routing layer that picks a model per request from task type, context size, latency budget, and data sensitivity.
What your agent is told to do
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What your agent is told to do
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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.
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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.
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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.
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Record the chosen route, the reason, the token usage, and the outcome for every request, so quality and cost can be compared per route rather than argued about.
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Per-task model choices exposed to users belong to Model Selection, and retrying a failed request belongs to Model Fallback. This feature owns only the rules that pick a destination for a healthy request; do not reimplement either neighbour here.
Edge cases it handles
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Edge cases it handles
8- Routing rules must be inspectable and testable, with a way to ask which route a given set of inputs would take without sending a request. An opaque router cannot be debugged when one class of requests starts returning poor results.
- Where prompts must differ per destination to get comparable results, those variants must be versioned alongside the prompt rather than patched at run time, or the rendered instructions become impossible to reconstruct.
- Data classified as sensitive must never be routed to a destination that has not been approved for it, including when a rule change or a new default would otherwise send it there. The block must fail closed.
- Quality and cost must be recorded per route, or the routing rules can never be improved and expensive routes accumulate unnoticed.
- When the preferred destination is unavailable, the substitution must be recorded and visible rather than silently changing what produced the answer. Handing off to a materially different model without a trace makes results inexplicable.
- A context size estimate that undershoots will send a large request to a destination that cannot hold it. Measure before routing and route on the measurement, with a margin.
- Rule changes must be versioned and reversible, and a request must record which rule set version routed it.
- A request matching no rule must hit a defined default route that is stated explicitly, not whichever destination happens to be listed first.
Definition of done
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Definition of done
9- Routing decisions are produced by explicit ordered rules and are deterministic for identical inputs.
- There is a way to see which route a given set of inputs would take without issuing a request.
- Sensitive data is blocked from unapproved destinations before the request is assembled.
- Every request records its route, the reason, token usage, cost, and outcome.
- Destination-specific prompt variants are versioned rather than adjusted at run time.
- A request matching no rule follows an explicitly defined default route.
- Rule sets are versioned and each request records the version that routed it.
- 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
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
Sentry Error Forwarding
Sentry Error Forwarding
Send real application errors to Sentry with clean context and grouping worth acting on.
What it does
Server and client error reporting into Sentry, with release and environment tagging, scrubbing, and deliberate noise control.
How it works
- 1 Find the app's existing error handling paths and route from there. Reporting belongs where errors are already caught and logged, not sprinkled into individual controllers and components.
- 2 Separate genuine faults from expected outcomes. Validation failures, permission denials, not-found responses, and cancelled requests are normal behaviour and must not be reported as crashes, or the signal is buried within a day.
- 3 Scrub every event before it leaves the process: authorisation headers, tokens, passwords, payment details, request bodies, query strings, and any personal data the app is not permitted to send to a third party. Attach an internal user or account identifier instead of a name or an email address.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/sentry-error-forwarding
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.