AI Sentiment Analysis
Estimate the tone of feedback and messages without pretending the model is certain.
What it adds
A sentiment label with a confidence value attached to feedback, reviews, tickets, and messages the app already stores.
What your agent is told to do
5
What your agent is told to do
5-
1
Find the free-text surfaces where tone would change how someone triages work: reviews, support messages, survey responses, comments. Score those on write and on demand, not the entire corpus in one pass.
-
2
Store the label, the confidence, the text version it was derived from, and the time it was produced. A score with no provenance cannot be audited or recomputed when the prompt changes.
-
3
Allow four outcomes plus a fifth: positive, neutral, negative, mixed, and unknown. A message that praises the product and condemns the billing is genuinely mixed, and forcing it into one bucket loses the part someone needs to act on.
-
4
Run scoring through the app's existing background-job system with a per-workspace token ceiling and a maximum input length, truncating on a sentence boundary rather than mid-word. Record spend per run so the cost of the feature is visible before it becomes a surprise.
-
5
Do not let a sentiment score gate anything consequential on its own. It may sort, filter, and flag for a human, but it must never close a ticket, downgrade an account, or suppress a message without review.
Edge cases it handles
8
Edge cases it handles
8- Sentiment describes one piece of text at one moment, not the person who wrote it. Do not roll scores up into a standing label on a customer record or display anything that reads as a judgement of the author.
- A single message can carry conflicting tone across its parts. Support a mixed outcome, and where the surface allows it, indicate which passages pulled which way rather than averaging them into a bland neutral.
- Sarcasm, one-word replies, emoji-only messages, and heavy in-house jargon defeat tone detection. Return unknown with low confidence in these cases and show nothing rather than showing a confident wrong answer.
- Automated decisions built on a probabilistic label will be wrong at scale. Keep escalation, closure, refunds, and account actions behind a human who can see the underlying text.
- The same words carry different weight across languages, regions, and product domains, so a threshold tuned on English support tickets will misread everything else. Calibrate per language and per surface, and hold back the feature where it has not been calibrated.
- When the model is unavailable, times out, or refuses, leave the record unscored and show it as not yet analysed. Do not fall back to neutral, which is indistinguishable from a real result.
- Malformed or unexpected output must be discarded rather than coerced. One retry with a lower ceiling is reasonable; a second failure means the record stays unscored.
- Redact or exclude payment details, credentials, and identity documents before any text leaves the app, and give operators a way to see exactly which fields are sent.
Definition of done
8
Definition of done
8- Every scored record stores its label, confidence, source text version, and timestamp.
- Mixed and unknown are first-class outcomes and appear in the interface.
- Low-confidence results are withheld from display rather than shown as fact.
- No account, ticket, or moderation action is taken from a sentiment score without human review.
- Provider outages, timeouts, and refusals leave records visibly unscored instead of defaulting to neutral.
- Token and cost ceilings are enforced per workspace and spend is reportable.
- 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
-
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.