AI Topic Tagging
Tag content against a controlled vocabulary instead of letting labels multiply.
What it adds
Automatic assignment of topics from a managed tag catalogue, applied as suggestions with an approval path for new terms.
What your agent is told to do
5
What your agent is told to do
5-
1
Treat the tag catalogue as the authority. Suggested tags must resolve to existing terms, and anything else goes into a proposal queue where an administrator accepts, renames, or rejects it.
-
2
Cap the number of tags per item and apply a relevance threshold, so only topics genuinely central to the content are applied. Three accurate tags beat twelve that are technically defensible.
-
3
Apply tags as suggestions the user can accept or dismiss on first rollout, and record acceptance rates before considering automatic application anywhere.
-
4
Store the catalogue version with each tagging run and give administrators a way to re-tag affected content when terms are merged, renamed, or retired, without re-tagging the entire corpus.
-
5
Where this sits beside AI Intent Detection, keep the two catalogues separate: intents describe what someone wants done and topics describe what the content is about. Merging them produces a vocabulary that serves neither.
Edge cases it handles
8
Edge cases it handles
8- New terms must never be created by the model directly. Route every unmatched suggestion to an approval queue, because an auto-growing vocabulary becomes unusable within weeks and cannot be un-grown.
- Tagging everything a document mentions in passing makes tags useless for filtering. Enforce a per-item ceiling and a relevance threshold, and prefer omission to a weak match.
- Content spanning several topics should receive several tags, and content matching nothing should receive none. An untagged item is a valid outcome and must not be forced into the nearest available term.
- When terms are merged, renamed, or retired, existing assignments must be migrated or invalidated. Stale tags pointing at removed terms should disappear from filters rather than render as blanks.
- Private, restricted, or draft content must not shape tags visible on public surfaces, and tag counts on public pages must not reveal the existence of records the viewer cannot see.
- Re-running the tagger must be idempotent for unchanged content. Repeated runs must not accumulate near-duplicate tags or overwrite tags a person applied by hand.
- Human-applied tags outrank model-applied ones. Record which is which and never let a later automatic run remove a tag a user chose.
- When the model is unavailable, tagging is skipped and the item is queued for a later pass. Publishing must never block on the tagger.
Definition of done
8
Definition of done
8- Every applied tag exists in the controlled catalogue at the recorded version.
- Unmatched suggestions enter an approval queue and are never created automatically.
- A per-item tag ceiling and relevance threshold are enforced, and zero tags is a supported result.
- Catalogue changes trigger targeted re-tagging rather than a full corpus pass.
- Manual tags are distinguished from automatic ones and are never removed by a later run.
- Restricted content does not influence tags or counts on surfaces the viewer cannot access.
- 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.