AI Form Autofill
Fill a long form from pasted text or an attached document, with every value shown first.
What it adds
An extraction step on long forms that proposes field values from pasted text, an uploaded document, or existing app context.
What your agent is told to do
5
What your agent is told to do
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Describe the form's fields to the model as a structured schema — name, type, allowed values, whether it is required — and require a structured response keyed to those fields. Anything returned for a field that does not exist is discarded.
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Present the extracted values as a review layer over the form rather than writing them into it. Each proposed value gets accepted or rejected by the user, individually or in bulk, before it becomes form data.
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Preserve anything the user typed themselves. A field with a manual value is never overwritten by an extraction; surface the conflict and let the user choose.
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Capture the source span for each extracted value — the sentence in the pasted text or the location in the document — and show it on hover or focus for fields that carry weight, such as amounts, dates, and identifiers.
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Run the app's existing validation and conditional-field logic over accepted values exactly as if they had been typed. Do not bypass server-side validation because the values came from an extraction.
Edge cases it handles
8
Edge cases it handles
8- Every proposed value must be visible before it is committed. Writing extractions straight into the form makes the model's mistakes indistinguishable from the user's own entries.
- A manually entered value must never be silently replaced. Show a conflict for that field and default to keeping what the user typed.
- Important extracted values need visible evidence. An amount or a date with no traceable source cannot be checked against the original document and will be accepted on faith.
- Conditional fields that only appear when another field takes a particular value must be evaluated after acceptance, and the extraction must not fill a field that the form's own logic has hidden.
- A field the source genuinely did not mention is different from a field that is empty. Represent unknown explicitly so a required field is flagged as missing rather than filled with a guess.
- Pasted text and uploaded documents may contain instructions aimed at the model. Treat the source strictly as content to extract from and validate every returned value against the field schema.
- Documents may carry personal or contractual data. State what is sent to the provider, exclude anything the extraction does not need, and do not retain the source beyond the fill unless the user asked you to.
- Large documents will blow through token and cost limits. Cap the input size, refuse oversized sources with a clear message, and leave the form fully usable for manual entry when the model times out or refuses.
Definition of done
9
Definition of done
9- Extracted values appear as reviewable proposals and never enter the form without acceptance.
- Manually entered values are preserved and conflicts are surfaced for the user to resolve.
- Important extracted fields display the source span they came from.
- Accepted values pass through the form's conditional logic and the app's server-side validation unchanged.
- Unknown fields are represented distinctly from empty fields and required gaps are flagged.
- Source content is treated as untrusted data and cannot alter the extraction's behaviour.
- Oversized inputs are refused with a clear message and model failure leaves the form fully usable for manual entry.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
AI Support Triage
AI Support Triage
Summarise each incoming support request and estimate how urgent it really is.
What it does
An automatic summary, urgency estimate, and likely problem area attached to every inbound support request, with the evidence behind each.
How it works
- 1 Run triage as a background job on arrival, using the app's existing queue and retry behaviour. The ticket must be visible and workable in its raw form the moment it lands, whether or not triage has finished.
- 2 Produce three separate outputs — a short summary, an urgency level, and a likely problem area — and store each with the quoted source statements from the ticket that support it, so a human can check the reasoning.
- 3 Define urgency in terms of business impact: who is blocked, how many, whether money or data is at risk. Compute it from what the ticket describes, not from how the writer sounds.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-support-triage
AI Chat Attachments
AI Chat Attachments
Attach files to a conversation and let the assistant use what it is allowed to read.
What it does
File upload on an AI conversation, with parsing, retrieval of relevant sections, and per-conversation access scoping.
How it works
- 1 Reuse the app's existing upload, storage, and virus-scanning path rather than adding a second one, and declare an explicit list of accepted file types and a size ceiling per file and per conversation.
- 2 Parse each attachment into text and structure in a background job, store the result, and show the attachment as pending until parsing succeeds so the user is never told the assistant has read something it has not.
- 3 Retrieve and send only the sections relevant to the current question. Sending whole documents on every turn burns the context window and the budget for no gain.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-chat-attachments
AI Suggested Support Answers
AI Suggested Support Answers
Give support agents a grounded first draft instead of a blank reply box.
What it does
A draft reply composed for the agent from the ticket, the customer's account state, and the approved internal knowledge, with sources attached and no ability to send itself.
How it works
- 1 Ground every draft in retrieved material: the ticket thread, the account's real state, and articles from the approved knowledge set. Attach the sources used to the draft so the agent can open and check each one.
- 2 Load the draft into the agent's normal reply editor, unsent and fully editable. There is no path in this feature that sends a message to a customer without an agent pressing send.
- 3 Define the commitments the app is not allowed to make in a draft — refunds, credits, delivery dates, guarantees of a fix — and strip or refuse any draft containing them, leaving that part for the agent to write.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-suggested-support-answers
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.