AI Data Cleanup Suggestions
Surface inconsistent or suspicious records and propose corrections a human approves.
What it adds
A review queue of proposed data corrections, each showing the original value, the reason, and an approval step.
What your agent is told to do
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What your agent is told to do
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Identify the fields where dirty data actually hurts: names, addresses, phone numbers, currencies, categories, and any field used for matching or reporting. Scope the first pass to those rather than scanning every column.
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Run deterministic checks first — casing, whitespace, duplicate punctuation, known formats, values outside an allowed set — and only send the residue to the model. Do not spend model tokens on problems a rule already solves.
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Separate formatting cleanup from semantic correction and present them as two kinds of suggestion. Trimming a stray space and changing a country from one value to another carry very different risk and deserve different approval thresholds.
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Write every suggestion to a review queue with the original value, the proposed value, the reason, and a confidence. Do not mutate source records directly, and do not auto-apply anything except rules an operator has explicitly promoted to automatic.
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This brief owns data already stored in the app. Rows failing validation during an upload belong to AI Import Error Repair, and header-to-field matching belongs to AI Import Column Mapping; reuse their approval surface rather than building a second one.
Edge cases it handles
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Edge cases it handles
8- Nothing may overwrite a source record without a review step or an operator-authored rule. An unattended cleanup pass that guesses wrong is worse than the dirt it removed.
- Formatting cleanup and semantic correction must stay distinguishable in the queue, because a reviewer will approve a hundred whitespace fixes in a batch and must not sweep a changed value through with them.
- Each suggestion must show the original value alongside the proposal and state the reason plainly, or reviewers approve on trust and stop reading.
- Values that appear frequently in already-dirty data are not thereby correct. Validate proposals against the app's own reference data and allowed values, not against the majority pattern in the same dirty table.
- Applying a batch twice must produce the same result as applying it once, and every applied change must be recorded with who approved it and be reversible.
- Records edited by a user after a suggestion was generated must be re-checked before the fix lands, or approval silently reverts someone's work.
- Field values may be personal data. Send the minimum needed to judge the value, exclude fields the reviewer has no permission to see, and never send an entire record when one column is under review.
- When the model is unavailable or returns malformed structured output, keep the deterministic suggestions flowing and mark the semantic pass as unavailable rather than emptying the queue.
Definition of done
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Definition of done
9- Suggestions appear in a review queue and no source record changes without approval or an explicit operator rule.
- Formatting and semantic suggestions are labelled distinctly and can be filtered and approved separately.
- Every suggestion displays the original value, the proposed value, and a stated reason.
- Applying the same batch twice changes nothing the second time, and every applied change is recorded and reversible.
- Deterministic checks run before the model and the model only sees what rules could not resolve.
- A record modified since its suggestion was generated is re-checked before the change is applied.
- Model unavailability leaves the rule-based suggestions working.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Prompt Injection Defense
Prompt Injection Defense
Stop instructions hidden in documents, pages, and tool output from steering the AI.
What it does
A boundary between the app's own instructions and untrusted content, backed by enforced tool permissions and confirmation for consequential actions.
How it works
- 1 Enumerate every route by which content the app did not author reaches a model: uploaded files, retrieved passages, fetched pages, records from connected accounts, and the results of tool calls.
- 2 Keep the app's own rules in the trusted portion of the request and enclose untrusted content in a clearly delimited region labelled as material to be analysed rather than instructions to be obeyed.
- 3 Enforce what the model may reach outside the model. Tool permissions, tenant scoping, and rate limits must hold even when the model has been completely persuaded — an instruction telling the model not to delete things is not a control.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/prompt-injection-defense
AI Import Column Mapping
AI Import Column Mapping
Guess how an uploaded file's columns line up with the app's fields, then ask.
What it does
A proposed mapping from uploaded columns to destination fields, shown with samples and confirmed before the import runs.
How it works
- 1 Take the uploaded headers and a small sample of rows and propose a mapping to the destination fields. Match on exact and near-exact header names first and only consult the model for the columns that remain unresolved.
- 2 Constrain the candidate set to fields the current user and the importer are actually permitted to write. A mapping that targets a read-only, computed, or privileged field must never be offered, whatever the header says.
- 3 Show the proposal as a table: source column, sample values, destination field, confidence, and the transformation that will be applied. A user cannot approve a mapping they cannot see the effect of.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-import-column-mapping
Model Fallback
Model Fallback
Keep AI features working when the primary model provider fails.
What it does
A bounded retry path that reruns a failed AI task on a compatible alternative model and discloses the substitution.
How it works
- 1 Classify failures before reacting. Provider outages, capacity rejections, rate limiting, and timeouts are worth retrying elsewhere; a refusal, an invalid request, or an authentication error will fail identically on any destination and must surface immediately.
- 2 Honour rate limiting with backoff before moving on. Retrying instantly against a provider that asked you to wait deepens the outage and can extend the penalty period.
- 3 Allow fallback only to models that can honour the same output shape and the same tool behaviour as the original. A substitute that returns a different structure turns a provider outage into a data problem inside the app.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/model-fallback
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.