AI Meeting Notes
Turn a transcript into notes, decisions, and follow-ups the organiser reviews.
What it adds
A reviewable draft of structured meeting notes — summary, decisions, owners, and follow-up items — generated from an existing transcript and linked back to it.
What your agent is told to do
5
What your agent is told to do
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Find where transcripts already arrive in the app and treat the transcript as the only input. Do not add recording, upload, or speaker diarization here; if no transcript exists, this feature has nothing to run on.
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Run the analysis through the app's existing background-job system and show the meeting in a clearly labelled processing state. A long transcript will outlive a web request and a queued job is the only honest way to represent the wait.
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Produce a draft that the organiser reads, edits, and publishes. Do not create tasks, assign owners, or notify anyone until a person has approved the draft, because a confidently wrong assignment costs more than the note saves.
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Attach a transcript position or timestamp to every decision and follow-up so a reader can check the claim against what was actually said, and make that link the primary way disputes get settled.
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Set a token ceiling, a wall-clock timeout, and a retry limit per meeting. When a ceiling is reached, keep whatever was produced, mark the notes as covering only part of the meeting, and say where the coverage stopped.
Edge cases it handles
8
Edge cases it handles
8- A transcript mixes firm commitments with musing and hypotheticals. Anything recorded as a decision or an action must be traceable to explicit language; suggestions and speculation belong in the summary, not the follow-up list.
- Diarization is often wrong or missing, leaving unlabelled or merged speakers. When the speaker behind a commitment is uncertain, leave the owner unset and mark it for the organiser rather than guessing.
- An item whose supporting timestamp cannot be located must be shown as unsourced, not silently presented alongside items that do link back.
- Assigning a follow-up to the wrong person is the failure that destroys trust in the feature. Only offer owners who were actually present, and never match a name to a user account on a partial or ambiguous string.
- Transcripts arrive truncated, full of crosstalk, or switching between languages mid-meeting. Detect these conditions up front, state them on the draft, and do not present degraded output as if it were complete.
- A refusal, a timeout, or output that does not parse into the expected structure must leave the meeting in a failed state with a retry, not an empty set of notes that reads as a meeting where nothing happened.
- The transcript may contain material the account has not agreed to send to a model provider. Make the provider boundary explicit in settings, and let an account turn the feature off entirely without breaking the meeting record.
- When the model is unavailable the meeting page must still show the raw transcript and its participants, with the notes section marked unavailable rather than the whole page erroring.
Definition of done
9
Definition of done
9- Notes are generated from an existing transcript in a background job, with a visible processing state.
- Every decision and follow-up either links to a transcript position or is marked unsourced.
- No task is created and no notification is sent until the organiser approves the draft.
- Owners are drawn only from confirmed participants, and an uncertain owner is left unset.
- Truncated, noisy, or multilingual transcripts produce output labelled with those limitations.
- Token, time, and retry ceilings are enforced per meeting and a partial result is retained rather than discarded.
- With the model unavailable, the meeting and its transcript remain viewable.
- The feature matches the existing design system.
- No existing functionality is broken.
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How it works
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Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-insight-cards
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How it works
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Copy the prompt
No account needed
Add this feature to my app:
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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.
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- 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.