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
AI Audit Trail
AI Audit Trail
Keep a reviewable record of what the AI saw, produced, recommended, and actually did.
What it does
A per-run record of inputs, sources, configuration, output, approvals, and executed actions, restricted and retained by purpose.
How it works
- 1 Write one record per run covering the feature invoked, the configuration and prompt version in force, the sources retrieved, the tools called, the output produced, who approved it, and what was carried out.
- 2 Distinguish a draft from an executed action explicitly in the record. A trail that shows only what text was produced cannot answer the question that matters in a review, which is whether anything happened.
- 3 Redact at the moment of writing. Strip credentials, tokens, and payment details before the record is stored, and keep only the personal content genuinely needed to understand the decision.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-audit-trail
AI PII Redaction
AI PII Redaction
Catch personal information in text before it leaves the app, and mask it.
What it does
A redaction pass over text and attachments on the paths that share, export, or forward content to another system.
How it works
- 1 Identify every path where text leaves its original context: exports, share links, support handoffs, outbound webhooks, and anything sent to a model provider. Redaction belongs on those boundaries, not scattered through the UI.
- 2 Run two detectors together and merge their results. Pattern matching handles the formats with a fixed shape, and a model pass handles the cases only context reveals, such as a name and a home address written into a free-text note.
- 3 Treat the model as one signal, not the decision. Anything the model alone flags must carry lower confidence than a deterministic match, and the feature must keep working with pattern detection only when the model is unavailable.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-pii-redaction
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.