AI Insight Cards
Turn a report into a few grounded cards, each tied to a number the app calculated.
What it adds
A small set of cards summarising notable movements in a report, each linked to the query and figure that produced it.
What your agent is told to do
5
What your agent is told to do
5-
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.
-
4
Cap the set at a handful of cards and deduplicate before rendering. Two cards describing the same movement from different angles read as two separate problems.
-
5
This feature owns the individual, evidenced cards. The dashboard-level narrative paragraph belongs to AI Dashboard Insights; where both exist, that feature should summarise these cards rather than analysing the data a second time.
Edge cases it handles
8
Edge cases it handles
8- A card with no traceable support is not shippable. Every card must carry the metric and query behind it, and any card whose supporting query cannot be re-run must be dropped rather than shown unsourced.
- The same movement will surface through several metrics at once. Deduplicate on the underlying movement, not on the wording, or a single drop will produce four near-identical cards.
- Percentage change on a small base is noise. Enforce a minimum absolute volume alongside the relative threshold so a move from two to four does not present as a hundred percent surge.
- Every number rendered on a card comes from the app's own calculation. Do not display a figure the model produced, even when it appears to match, because the one time it does not will be silent.
- Cards are tied to a filter set and a data snapshot. When filters change or the source data is reprocessed, expire the cards and regenerate rather than leaving figures that no longer reconcile with the report.
- Cards must respect the viewer's permissions individually. A card built on a metric this viewer cannot access must not render, even when the report itself is visible to them.
- Set a per-generation cost and token ceiling, and cache by filter set and snapshot so that reopening a report does not trigger a fresh run.
- A refusal, timeout, or unparseable response should show no cards and no error framing that suggests the report itself is broken.
Definition of done
9
Definition of done
9- Every card cites the metric and query supporting it and links to the filtered view.
- All displayed figures come from the app's own calculations, never from model output.
- Cards describing the same underlying movement are collapsed into one.
- Materiality thresholds combine relative and absolute change, so small-base noise is excluded.
- Cards expire and regenerate when filters or source data change.
- Cards built on metrics the viewer cannot access are not rendered.
- A model failure yields no cards while the report remains fully usable.
- 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
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.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-trend-analysis
How it works
-
1
Copy the link
Grab the Markdown instruction URL for this feature.
-
2
Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
-
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.