Webhook Delivery Log and Retry
Show exactly what you sent, what came back, and how to send it again safely.
What it adds
An outbound webhook pipeline with signed payloads, automatic retries with backoff, a per-attempt log, and manual replay.
What your agent is told to do
5
What your agent is told to do
5-
1
Record every delivery attempt: event ID, endpoint, request body, response status, response body excerpt, duration, and attempt number.
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Sign each payload with a per-endpoint secret and include a timestamp in the signed material so receivers can reject replays.
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Retry failed deliveries with exponential backoff and jitter, cap the attempts, then move the delivery to a dead-letter state the user can see and act on.
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4
Include a stable event ID on every attempt so receivers can deduplicate, and let users replay any past event on demand.
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5
Do NOT retry on a 4xx that indicates the receiver rejected the payload outright — retrying a 400 forever just burns the endpoint. Retry timeouts, connection failures, 429, and 5xx.
Edge cases it handles
6
Edge cases it handles
6- Support two active signing secrets during rotation so receivers can cut over without dropped deliveries.
- Redact tokens, keys, and personal fields from the stored request and response bodies; the log is a support tool, not a secret store.
- Retries arrive out of order by definition — the payload must carry enough state, or a sequence number, for the receiver to resolve that.
- A consistently failing endpoint should be auto-disabled after a threshold, with the owner notified, rather than retried indefinitely.
- Cap and time out the response body you store; a receiver returning a 10MB HTML error page must not fill the log table.
- A manual replay of a very old event must be marked as a replay, not presented as a fresh delivery.
Definition of done
8
Definition of done
8- Every attempt is logged with status, timing, and attempt number.
- Payloads are signed with a timestamped signature and rotation is supported.
- Failures retry with exponential backoff and land in a dead-letter state after a cap.
- The same event ID is preserved across all attempts of a delivery.
- Users can replay any logged event, and replays are marked as such.
- Stored request and response bodies are redacted and size-capped.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
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
Design Token Linter
Design Token Linter
Find the hardcoded colours, spacings, and radii that drifted away from the token set.
What it does
A check over the app's styles and markup that reports raw colour, spacing, typography, radius, and shadow values where an approved token exists.
How it works
- 1 Read the app's existing token definitions as the source of truth for the check, so adding a token to the design system automatically widens what the linter recognises without a second list to maintain.
- 2 Scan every place a value can be written: stylesheets, style objects, inline style attributes, utility class strings, and component props that take a raw value.
- 3 Report the nearest matching token alongside each finding, with the difference, so a developer sees that a 15px gap is one pixel from an existing step and can decide rather than re-derive.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/design-token-linter
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
How it works
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1
Copy the link
Grab the Markdown instruction URL for this feature.
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2
Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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3
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.