Webhook Endpoint Management
Let users choose where the app sends events, and which events go there.
What it adds
Creating, editing, and testing outbound webhook endpoints, with destination validation and per-endpoint event subscriptions.
What your agent is told to do
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What your agent is told to do
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Let users register endpoints with a URL and a chosen set of event types. Default the subscription to nothing — an endpoint that silently receives every event is a data leak waiting to happen.
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Require HTTPS and validate the destination before saving: resolve the host and refuse private, loopback, link-local, and metadata-service addresses.
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Re-resolve and re-check the destination at delivery time, not only at save time. DNS can be repointed at an internal address after validation passes.
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Offer a test delivery that sends a clearly-marked sample event and shows the raw response. Keep test deliveries out of production event history and out of any delivery-success metrics.
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Signing secrets, retries and backoff, per-attempt delivery logs, replay, and auto-disabling repeatedly-failing endpoints are all owned by Webhook Delivery Log and Retry. Extend that feature rather than building a second delivery system here.
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Show the current state of each endpoint — enabled, disabled by the user, or disabled by the system — and link through to its delivery history.
Edge cases it handles
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Edge cases it handles
6- A redirect can move a validated public URL to a private one. Do not follow redirects on delivery.
- A URL that resolves to different addresses on each lookup will pass validation once and fail safety later. Check every resolved address, not just the first.
- Changing an endpoint's subscribed events must not retroactively change what already-queued deliveries contain.
- Deleting an endpoint with deliveries in flight must stop future attempts without erasing the history someone may be debugging.
- Two endpoints pointing at the same URL are legitimate — different event sets, different environments. Warn, do not block.
- Endpoint management is a privileged action. Someone who can add a destination can exfiltrate every event in the workspace, so gate it by role and audit every change.
Definition of done
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Definition of done
8- Endpoints require HTTPS and are rejected when the destination resolves to a private or link-local address.
- Destination safety is re-checked at delivery time and redirects are not followed.
- Each endpoint subscribes to an explicit list of event types, empty by default.
- Test deliveries are visibly marked and excluded from production delivery history.
- The endpoint's enabled or disabled state, and who or what disabled it, is visible.
- Creating, editing, and deleting endpoints is role-gated and audited.
- 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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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.