Natural Language Report Builder
Describe the report you want and get a configuration you can check before it runs.
What it adds
A plain-language entry point to the app's report builder that produces a normal, editable report configuration.
What your agent is told to do
5
What your agent is told to do
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1
Expose the app's defined metrics, dimensions, and grouping options to the model as an explicit vocabulary, and have it return a report configuration in the same shape the builder already saves. It must not invent a metric name or a calculation.
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2
Show the generated configuration in the builder for review and require the user to run it. Do not execute a potentially expensive aggregation on the model's first guess.
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Maintain a controlled glossary mapping business terms to defined metrics — what active means, what a qualified lead is, which revenue figure counts — and resolve ambiguous language through that glossary rather than letting the model decide each time.
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Validate the configuration against the user's data permissions before it can run, and reject combinations of dimensions that would expose restricted records through a small-group breakdown.
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5
Once generated, the report must be an ordinary saved report that runs without any model involvement. Filters on a plain list view are owned by Natural Language Filters; this feature covers metric, grouping, and date-range configuration only.
Edge cases it handles
8
Edge cases it handles
8- A request for a metric the app does not define must be refused by name. Restricting the model to the declared vocabulary is what prevents a plausible-looking report built on a number nobody computes.
- The configuration must be visible and editable before the query runs, because a misread grouping can turn a cheap report into one that scans the whole dataset.
- Business terms mean different things in different workspaces. Route them through the glossary and show which definition was used, rather than letting the model pick a reasonable meaning silently.
- Some dimension combinations expose individuals even when each dimension alone is permitted. Block breakdowns that fall below a minimum group size or that cross a permission boundary.
- A saved report must reproduce identically later without calling the model again. Persist the resolved configuration, not the original sentence, or the report changes meaning between runs.
- If the model returns a configuration that fails validation, keep the user in the builder with their request intact and explain which part could not be resolved.
- Report runs can be slow and expensive. Estimate the cost of the generated configuration and warn before running anything unusually large.
- When the model is unavailable, the manual report builder must remain fully functional with the natural language entry point clearly disabled.
Definition of done
9
Definition of done
9- Generated configurations use only metrics and dimensions the app defines.
- The configuration is shown for review and requires an explicit run before any query executes.
- Ambiguous business terms resolve through the glossary and the definition used is visible.
- Combinations that would expose restricted data or fall below the minimum group size are blocked.
- A saved report reruns identically with no further model involvement.
- Validation failures return the user to the builder with an explanation and their request preserved.
- The manual builder remains fully usable when the model is unavailable.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Retrieval-Augmented Generation
Retrieval-Augmented Generation
Ground AI answers in the app's own content by retrieving source passages first.
What it does
A retrieval layer that selects permitted passages from app content and supplies them to the model as cited evidence.
How it works
- 1 Decide which content is answerable from and treat everything else as out of scope. A retrieval feature pointed at the whole database returns confident answers about records nobody meant to expose.
- 2 Apply the requesting user's tenant, record, and field permissions during retrieval, before any passage is assembled into a request. Filtering the answer afterwards is too late — the content has already crossed the boundary.
- 3 Chunk on the content's own structure — sections, headings, rows, message boundaries — and carry enough surrounding context in each chunk that it still means something on its own.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/retrieval-augmented-generation
AI Dashboard Insights
AI Dashboard Insights
Explain what actually changed on a dashboard, in the viewer's own filters and period.
What it does
A short generated commentary attached to a dashboard, describing the movements that matter under the filters currently applied.
How it works
- 1 Compute the numbers first, in the app, using the dashboard's existing queries with the viewer's active filters. Pass the model the computed figures and let it write the wording, never the raw rows to add up itself.
- 2 State the period and the comparison period explicitly in every insight. A sentence saying signups are up is meaningless without saying up against what.
- 3 Filter the input to metrics the viewer is permitted to see before it reaches the model. A commentary that mentions revenue to someone whose dashboard hides revenue is a permissions leak.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-dashboard-insights
Knowledge Source Sync
Knowledge Source Sync
Keep the AI's knowledge current as documents, records, and external sources change.
What it does
An ingestion pipeline that syncs approved sources on a schedule, tracks versions, and removes content whose source disappears.
How it works
- 1 Give an operator an explicit approval step for every source, and default to nothing being indexed. Automatic inclusion of whatever the connected account can see is how private material ends up answerable.
- 2 Store a content fingerprint or version for each document alongside the time of the last successful sync, and re-ingest only what has actually changed since then.
- 3 Reuse the app's existing background-job system and make each sync resumable at the document level, so an interruption partway through a large source restarts from the last completed document rather than from the beginning.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/knowledge-source-sync
How it works
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1
Copy the link
Grab the Markdown instruction URL for this feature.
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2
Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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3
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.