AI Forecast Narrative
Turn the numbers a forecast already produced into a plain explanation of the outlook.
What it adds
A written interpretation of an existing forecast, covering the horizon, the confidence range, and the assumptions behind it.
What your agent is told to do
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What your agent is told to do
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Take the forecast values, the confidence interval, and the horizon from the app's existing forecasting calculation and pass them to the model as fixed inputs. The model interprets numbers; it never produces or adjusts them.
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Mark historical values and projected values distinctly in what you send, and require the narrative to keep them distinct in the prose. A sentence that blends last quarter's actuals with next quarter's projection is misinformation.
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State the horizon, the confidence range, and the assumptions the forecast rests on in the narrative itself, in the metric's own units and the workspace's time zone.
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Invalidate the narrative whenever the forecast inputs change — new actuals, a changed model, a corrected historical series — and regenerate rather than leaving prose that no longer matches the chart above it.
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Describing past movement is owned by AI Trend Analysis and explaining a specific outlier is owned by AI Anomaly Explanation. This feature covers the projected portion only; do not let it re-narrate the history the sibling features already describe.
Edge cases it handles
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Edge cases it handles
8- Any number in the narrative that did not come from the forecasting calculation is a defect. Verify the figures in the generated text against the source values and discard the narrative on a mismatch.
- The horizon, the confidence interval, and the assumptions must be named. A projection stated without its uncertainty reads as a promise and will be quoted as one.
- Projected values must never be described in the past tense or alongside actuals without a label. Enforce the distinction in the output format, not only in the instruction.
- A narrative generated from a superseded forecast must be marked stale or removed. Tie it to the forecast run that produced it and drop it when a new run lands.
- A wide confidence interval or a short history means the forecast is weak. Suppress the narrative or lead with the caveat rather than writing a confident paragraph about a guess.
- If the model refuses, times out, or returns a truncated paragraph, show the forecast chart and its interval alone. The numeric forecast is the product; the prose is a convenience.
- Forecasts often cover revenue or headcount. Decide what may leave the app, and do not send customer names, account identifiers, or other detail the narrative does not require.
- Narratives should be generated once per forecast run and stored, with a ceiling on regeneration, so a dashboard refresh does not trigger a new call each time.
Definition of done
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Definition of done
9- Every figure in the narrative traces back to the forecasting calculation and is verified against it.
- Historical and projected values are labelled distinctly in the output.
- The horizon, confidence range, and assumptions are stated in every narrative.
- A narrative is invalidated and regenerated when its forecast inputs change.
- Low-confidence forecasts are either suppressed or carry an explicit caveat rather than reading as commitments.
- The forecast chart and its interval remain available when the model is unavailable.
- Narratives are stored per forecast run and are not regenerated on each page view.
- 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
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
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
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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.