AI Import Error Repair
Propose fixes for rejected import rows and let the user retry only those.
What it adds
A repair view for rows that failed validation, with suggested corrections, approval, and a partial retry.
What your agent is told to do
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What your agent is told to do
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Find where the importer records rejected rows and their validation errors, and build the repair view on that record. If failures are only logged, persist them first with the original row intact.
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Attempt deterministic repair before the model: date and number formats, boolean spellings, trimmed identifiers, known aliases for enumerated values. Send only the rows those rules cannot fix.
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Group identical failures and propose one rule for the group rather than one suggestion per row. A user approving the same date-format fix four hundred times will approve it without reading.
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Never fabricate a value for a required field the file does not contain. Mark those rows as unrepairable and require the user to supply the data or drop the row.
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Retry only the approved rows against the same import job, keyed so that a row that already imported cannot be written twice. Rows that fail again return to the repair view with the new error, not the old one.
Edge cases it handles
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Edge cases it handles
8- The original row and the exact validation error must stay visible next to every proposal, so a reviewer can tell a formatting fix from a changed meaning.
- Deterministic parsing must run before the model for dates, numbers, and identifiers. A model guessing between day-first and month-first ordering will be confidently wrong on the twelve days a month where both parse.
- A missing required value must never be invented. An identifier, price, or email that the file does not contain has to come from the user, not from a plausible-looking guess.
- A repair rule approved for a group applies uniformly to that group and only after approval, and the affected row count must be shown before it is applied.
- Retries must be idempotent against the original job. Rows that succeeded on the first pass must not be re-created when the repaired remainder is submitted.
- A repaired row that now collides with an existing record must surface as a duplicate decision, not be quietly merged or quietly skipped.
- Rows may contain personal data. Send only the failing fields and their error, exclude columns irrelevant to the failure, and do not log the payload sent to the provider.
- When the model is unavailable, times out, or returns structured output the app cannot parse, the deterministic repairs and the manual edit path must still work.
Definition of done
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Definition of done
9- Rejected rows persist with their original values and validation errors and are visible in a repair view.
- Deterministic parsing resolves format failures before any model call.
- Identical failures are grouped into a single reviewable rule with the affected row count shown.
- No suggestion supplies a value for a required field absent from the source file.
- Retrying repaired rows cannot duplicate rows that already imported.
- Rows failing a second time return with their current error rather than the original one.
- Model unavailability leaves rule-based repair and manual editing intact.
- 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.