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
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
AI Dashboard Insights
AI Dashboard Insights
Explain what actually changed on a dashboard, in the viewer's own filters and period.
What it does
A short generated commentary attached to a dashboard, describing the movements that matter under the filters currently applied.
How it works
- 1 Compute the numbers first, in the app, using the dashboard's existing queries with the viewer's active filters. Pass the model the computed figures and let it write the wording, never the raw rows to add up itself.
- 2 State the period and the comparison period explicitly in every insight. A sentence saying signups are up is meaningless without saying up against what.
- 3 Filter the input to metrics the viewer is permitted to see before it reaches the model. A commentary that mentions revenue to someone whose dashboard hides revenue is a permissions leak.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-dashboard-insights
Knowledge Source Sync
Knowledge Source Sync
Keep the AI's knowledge current as documents, records, and external sources change.
What it does
An ingestion pipeline that syncs approved sources on a schedule, tracks versions, and removes content whose source disappears.
How it works
- 1 Give an operator an explicit approval step for every source, and default to nothing being indexed. Automatic inclusion of whatever the connected account can see is how private material ends up answerable.
- 2 Store a content fingerprint or version for each document alongside the time of the last successful sync, and re-ingest only what has actually changed since then.
- 3 Reuse the app's existing background-job system and make each sync resumable at the document level, so an interruption partway through a large source restarts from the last completed document rather than from the beginning.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/knowledge-source-sync
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.