AI Intent Detection
Work out what a message is asking for so the app can route it to the right place.
What it adds
Classification of incoming free text against a fixed catalogue of supported intents, with an explicit unknown outcome.
What your agent is told to do
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What your agent is told to do
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Define the intent catalogue as data owned by the app, not as prose inside a prompt. Every result must resolve to an entry that already exists, and anything else becomes unknown.
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Detect intent from the actionable part of the message only. Strip quoted replies, forwarded threads, signatures, and prior messages, because the intent of a two-month-old quoted paragraph is not the intent of the message in front of you.
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Return a ranked shortlist with confidence rather than a single verdict, and treat everything below a configured threshold as ambiguous so the interface can ask instead of guessing.
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Where intent triggers an action, show the detected intent and let the user confirm or correct it. Corrections must be stored, because they are the only honest measure of whether the classifier is working.
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Sentiment scoring on the same text belongs to AI Sentiment Analysis; this feature owns what the user wants and that one owns how they feel about it. Share one text extraction and redaction step between them rather than sending the same message to the provider twice.
Edge cases it handles
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Edge cases it handles
8- Results must be constrained to the closed catalogue. An invented intent, however plausible, is not routable and must be collapsed to unknown rather than created on the fly.
- Ambiguous and multi-purpose messages are common. Unknown must be a real outcome with its own path through the interface, not a hidden state that silently picks the first option.
- Quoted, forwarded, and historical text will otherwise dominate the classification. Separate the new content before analysis and record which portion was used.
- Text in a message may itself contain instructions aimed at the model. Treat all incoming content as untrusted data, never as configuration, and reject any result that names an intent outside the catalogue as evidence of exactly this.
- Anything with a consequence — cancelling, refunding, deleting, escalating, notifying a customer — needs an explicit confirmation step showing what was detected and what will happen.
- When the provider is unavailable, messages must still arrive and land in a default queue for manual triage. Routing may degrade; delivery may not.
- Truncated or malformed output must be rejected outright. A partially returned intent name that happens to prefix-match a real one is a wrong answer, not a near miss.
- Changing the catalogue does not reclassify history. Store the catalogue version with each result so old routing decisions remain explicable.
Definition of done
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Definition of done
8- Every detected intent maps to an entry in the stored catalogue, with the catalogue version recorded.
- Unknown and ambiguous outcomes are supported end to end and visible in the interface.
- Quoted and historical text is excluded from the analysed content.
- Consequential actions require explicit user confirmation of the detected intent.
- User corrections are captured and reportable against detected intents.
- A provider outage routes messages to manual triage without dropping them.
- 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.