Configuration Validation
Fail at startup with a clear message instead of at 3am with a null.
What it adds
A startup check that every required setting is present, well-formed, and consistent — reporting all problems at once, in plain language.
What your agent is told to do
7
What your agent is told to do
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Declare every setting the app reads in one place: its name, whether it is required, which environments require it, its expected format, and a one-line description of what it does.
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Validate at startup, before the app accepts traffic. Report every problem in a single pass — an operator fixing one missing variable per deploy cycle is a bad afternoon.
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Say what is wrong and what to do. 'DATABASE_URL is missing; expected a postgres:// connection string' beats a nil error six frames deep.
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Validate relationships as well as individual values. A setting that is required only when a feature flag is on, or a pair that must both be set or both be absent, is where the real breakage lives.
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Fail closed in production for anything unsafe to omit, but allow sensible local defaults in development so a new contributor can boot the app.
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Do NOT print, log, or include secret values in validation errors. Report the name, the expected shape, and whether it is present — never the value, not even truncated.
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Classify settings as required, optional, deprecated, or environment-specific, and warn on deprecated ones with the replacement name.
Edge cases it handles
7
Edge cases it handles
7- An empty string is not the same as unset, and the difference is usually a bug. Decide explicitly which one counts as missing.
- Type coercion is a silent failure mode. The string 'false' is truthy in most languages, and 'no' is not a number.
- Settings that point at external services should be checked for shape at startup but not necessarily connected to — a strict connectivity check turns a slow dependency into a boot failure.
- Validation must run identically in every environment that boots the app, including migration containers, one-off consoles, and scheduled tasks, or a missing value surfaces only in the path nobody tested.
- Deployment templates and the documented example file must be generated from the same declaration, or the list drifts within a month.
- A validation failure must exit with a non-zero status and a readable message on stderr, so the deploy fails visibly rather than crash-looping quietly.
- Do not require production-only settings in a test run, or the test suite becomes a place where secrets have to exist.
Definition of done
9
Definition of done
9- Every setting the app reads is declared in one place with its requirement level and format.
- Startup validation reports all failures at once and refuses to serve traffic on failure.
- Error messages name the setting, the expected shape, and the fix, and never contain the value.
- Cross-setting relationships and conditionally required settings are validated.
- Development boots with defaults while production fails closed on unsafe omissions.
- Deprecated settings warn and name their replacement.
- The documented example configuration is generated from the declaration.
- 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
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
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.