AI Key Point Extraction
Reduce long content to the decisions, facts, and requests that actually matter.
What it adds
A short list of the salient points in a long document or thread, each tied back to the passage it came from.
What your agent is told to do
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What your agent is told to do
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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.
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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.
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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.
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Copy figures, dates, names, and identifiers through verbatim and verify each one appears in the source before display. Rounding a number or shifting a date inside a summary is the failure mode that destroys trust in the whole feature.
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Cache the result against a content hash so reopening a thread does not re-run the model, and only regenerate when the underlying content changes.
Edge cases it handles
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Edge cases it handles
8- Numbers, dates, monetary amounts, and identifiers must survive unchanged. Check each against the source text and drop any point containing a figure that cannot be found there.
- Repetition is not importance. A phrase in every message of a thread is usually boilerplate or a signature, and weighting by frequency surfaces exactly the wrong content.
- Keep what was established apart from what was suggested. Marking a proposal as a decision changes the meaning of the thread, so label facts, decisions, and recommendations distinctly.
- Every point needs a link back to its passage, and in a thread that means the specific message rather than the thread as a whole.
- Thin or purely social content should yield two points or none. Padding a list to a fixed length forces the model to invent significance that is not there.
- Long content must be chunked and the per-chunk points merged, with near-duplicates collapsed so the same decision does not appear three times in different words.
- In a thread with mixed permissions, only include content the viewer is allowed to read. A summary is a distribution channel, and it will leak a restricted message as readily as any other.
- If the model is unavailable or returns a truncated list, show the original content with an unavailable notice. Never present a partial list as the full set of key points.
Definition of done
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Definition of done
9- Every point is typed as a decision, fact, risk, request, or open question.
- Every point links to the specific source passage or message.
- Numbers, dates, and identifiers in points match the source exactly.
- Sparse content produces few or no points rather than padded output.
- Results are cached against a content hash and regenerate only when the source changes.
- Points derived from content the viewer cannot access are excluded.
- A truncated or failed run shows an unavailable state rather than a partial list.
- 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
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
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.