AI Dashboard Insights
Explain what actually changed on a dashboard, in the viewer's own filters and period.
What it adds
A short generated commentary attached to a dashboard, describing the movements that matter under the filters currently applied.
What your agent is told to do
5
What your agent is told to do
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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.
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State the period and the comparison period explicitly in every insight. A sentence saying signups are up is meaningless without saying up against what.
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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.
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Show the commentary as a distinct, labelled block that is clearly generated and carries the time it was produced. Do not weave generated sentences into the dashboard's own labels where a reader cannot tell what came from a model.
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This feature owns the narrative paragraph for a whole dashboard. Per-metric findings with their own supporting query belong to AI Insight Cards; if that feature is present, generate the summary from its cards rather than running a second analysis pass.
Edge cases it handles
8
Edge cases it handles
8- Every figure quoted must come from the app's own aggregation under the viewer's current filters. If the filters changed after the commentary was produced, mark it stale and offer a regenerate rather than showing numbers that no longer match the charts above them.
- Each statement must name its window and its comparison, such as the last 30 days against the previous 30. Without both, a reader will assume whichever comparison flatters the number.
- The model will reach for causes it cannot know. Constrain output to what moved and by how much, and forbid explanations of why unless the dashboard actually contains the driver as a dimension.
- Run the permission filter on the inputs, not the outputs. Generating commentary across all metrics and then trying to strip the forbidden sentences afterwards will eventually miss one.
- An uneventful period should produce nothing. Say that nothing material changed rather than padding the block with generic observations about steady performance.
- Cap the tokens and the spend per generation, and cache the result against the exact filter set and period so that reloading the dashboard does not bill for a fresh run.
- On a timeout, refusal, or truncated response, hide the block entirely and leave the dashboard fully usable. This is commentary on top of the real data, never a prerequisite for it.
- Partial data, a metric still backfilling, or a period that has not closed must be called out, or the commentary will report a drop that is only late-arriving data.
Definition of done
9
Definition of done
9- Every number in the commentary matches the dashboard under the viewer's current filters.
- Each statement names its period and comparison period.
- Metrics the viewer cannot access are excluded before generation, not after.
- Uneventful periods produce an explicit no-material-change result rather than filler.
- Results are cached per filter set and marked stale when filters change.
- A model failure hides the block and leaves the dashboard fully functional.
- The block is visibly labelled as generated and timestamped.
- 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
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Add this feature to my app:
https://addthisfeature.com/x/retrieval-augmented-generation
AI Insight Cards
AI Insight Cards
Turn a report into a few grounded cards, each tied to a number the app calculated.
What it does
A small set of cards summarising notable movements in a report, each linked to the query and figure that produced it.
How it works
- 1 Detect candidate movements in the app first, using the report's own aggregations. The model ranks and phrases the candidates it is given; it does not go looking for them and it does not produce the arithmetic.
- 2 Bind each card to the query, metric, and period that support it, and make the card link through to the filtered view so a reader can check it in one click.
- 3 Set a materiality threshold before generation, in both relative and absolute terms, so a swing on a metric with three events does not outrank a real change on a metric with thousands.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-insight-cards
AI Trend Analysis
AI Trend Analysis
Explain what a chart is actually showing, in sentences backed by computed numbers.
What it does
A written summary attached to a time series or metric set, describing direction, magnitude, and comparison period.
How it works
- 1 Compute every number in ordinary code before the model is involved: the change, the rate, the comparison period, the baseline, the seasonal adjustment. The model writes prose about figures it is given and must never produce a figure of its own.
- 2 State the comparison window and the time zone in the summary text, resolved from the workspace's configured zone rather than the server's. A change described as week over week is meaningless without saying which weeks.
- 3 Instruct the model to describe what the data shows and to stop there. Do not let it assert causes, attribute movement to campaigns or releases, or predict what happens next.
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Add this feature to my app:
https://addthisfeature.com/x/ai-trend-analysis
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.