AI Trend Analysis
Explain what a chart is actually showing, in sentences backed by computed numbers.
What it adds
A written summary attached to a time series or metric set, describing direction, magnitude, and comparison period.
What your agent is told to do
5
What your agent is told to do
5-
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.
-
4
Detect the conditions under which a summary should not be written — too few data points, a gap in collection, a tracking change mid-window — and skip generation with a plain explanation rather than narrating noise.
-
5
This entry owns the routine explanation of a normal series. Explaining a specific outlier belongs to AI Anomaly Explanation, and describing projected future values belongs to AI Forecast Narrative. Share the same computed-metrics layer across all three and do not let this feature drift into either.
Edge cases it handles
8
Edge cases it handles
8- Every figure that appears in the prose must have come from the deterministic calculation. Cross-check the numbers in the generated text against the computed values and reject the summary if they disagree, because a wrong number in a confident sentence is the worst possible output.
- A seasonal pattern, a recurring weekly cycle, and a genuine trend all look like movement. Feed the model the seasonally adjusted view alongside the raw one and require it to say which it is describing.
- A single large spike can dominate an average and make a flat series look like growth. Identify outliers in code and tell the model they are outliers so it does not describe a one-off as a trend.
- Sparse periods, missing days, and a metric whose definition or tracking changed mid-window must suppress the summary or carry an explicit caveat, not be silently averaged over.
- Causal language must be blocked. A summary claiming the drop was caused by anything is stating something the data cannot support and must be treated as a defect.
- The summary must be cached against the exact data window and filter set that produced it, or a user changing the date range will read last week's prose above this week's chart.
- If the model times out or returns truncated text, show the chart alone. A half-finished sentence under a graph is worse than silence.
- Regenerating a summary on every filter change on a dashboard is a runaway cost. Generate on demand or on a schedule, with a per-workspace ceiling.
Definition of done
9
Definition of done
9- All numbers in the summary come from deterministic calculation and are verified to match the underlying computation.
- The comparison period and time zone are stated explicitly in the text.
- Trend, seasonality, and one-off spikes are distinguished rather than conflated.
- Sparse, incomplete, or definition-changed data suppresses the summary or produces an explicit caveat.
- No summary asserts a cause for the movement it describes.
- Summaries are cached against the data window and filters that produced them, and generation is rate-limited.
- A model timeout or truncated response leaves the chart rendered with no summary.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
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
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 Key Point Extraction
AI Key Point Extraction
Reduce long content to the decisions, facts, and requests that actually matter.
What it does
A short list of the salient points in a long document or thread, each tied back to the passage it came from.
How it works
- 1 Apply this where length is the problem: long threads, meeting notes, call transcripts, lengthy tickets, multi-page documents. Content that already fits on a screen does not need extracting.
- 2 Classify each point by kind — decision, fact, risk, request, open question — so a reader can scan for the category they came for instead of reading a flat list.
- 3 Carry a reference to the source passage with every point and let the reader jump to it. A point nobody can verify is a claim, not a summary.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-key-point-extraction
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.