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
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
Retrieval Debugger
Retrieval Debugger
Show exactly which sources, chunks, and scores produced a given AI answer.
What it does
A per-answer inspector showing the query as issued, the filters applied, the candidate chunks with their scores, and what reached the model.
How it works
- 1 Capture for each answer the query as it was issued, the filters applied, the candidates returned with their scores, and which of those actually made it into the request after the context ceiling was applied.
- 2 Show results after permission filtering, with a count of how many candidates were excluded and why. Displaying the pre-filter set turns the debugger into a way to read content the viewer cannot open.
- 3 Present each scoring stage separately — keyword, semantic, and any reranking — because a chunk that ends up first overall may have been rescued by one stage after being buried by another, and a single blended number hides that.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/retrieval-debugger
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.