Agents In Use
What’s new todayLast update
499 additions

How to use Claude to turn meetings into queryable notes

The author reports queryable meeting notes across months of conversations and increasingly accurate summaries requiring less intervention as the context document accumulates corrections.

TRY A BOUNDED VERSION

Prompt to try

Editorial adaptation, not the source author’s exact prompt. Replace bracketed inputs and review tool permissions first.

Read [context document], then [meeting transcript]. Reconcile uncertain names and terminology against [error table]. Ask about unresolved cases before drafting an overview, topical notes, decisions and action items. Save [Obsidian note] and send confirmed tasks to [Todoist].

What happened

The author reports queryable meeting notes across months of conversations and increasingly accurate summaries requiring less intervention as the context document accumulates corrections.

Reported by FireBeyond on Hacker News

How the agent did it

  1. Provided Claude with a Krisp transcript and a hand-built context document.
  2. Reconciled uncertain names and terms against the master error table.
  3. Confirmed unresolved questions before Claude drafted the summary.

Where a person stepped in

The author confirms unresolved names or terms before Claude emits the final summary.

A person approved each step before the agent continued.

FireBeyond built a meeting pipeline to address transcription errors in colleague names, customer names and internal terminology. Krisp supplies the raw transcripts. The author hands each transcript to Claude with a manually prepared context document containing a people directory, terminology glossary and master transcription error table.

Claude follows a fixed sequence: read the context, read the transcript, and reconcile uncertain names or terms against the error table before drafting. It asks only about unresolved cases. After the author confirms those, it produces a Markdown summary with an overview, topical notes, decisions and action items. Work items are pushed into Todoist.

The summaries go into Obsidian with YAML frontmatter and live Dataview queries, making meeting metadata and open actions behave like database records. Through Cowork, the author queries the accumulated folder with questions that require reasoning across separate meetings, including whether earlier customer requests have led to shipped work.

Resolved clarifications are written back into the context document. The author says this means previously corrected errors are handled automatically on later transcripts, and summaries have become tighter with less intervention.

Who it's for: teams with recurring meetings and specialized names or terminology that transcription tools frequently misrecognize.

Prompt
Suggested prompt (written by Agents In Use, not the author's)

Source: Hacker News · FireBeyond

Frequently asked questions

What does the author confirm manually?

Claude surfaces only the names or terms it cannot resolve using the context file and master error table. The author confirms those before the summary is produced.

How does the pipeline improve over time?

Each resolved clarification is written into the context document, including its people directory, glossary and transcription error table, so recurring errors can be corrected automatically.

More details

Remaining steps

  1. Stored structured notes in Obsidian and pushed work items into Todoist.
  2. Wrote resolved clarifications back into the context document.

About this account

Claude corrects resolvable errors silently, drafts structured summaries and sends work items to Todoist after clarification.

Platform: Hacker News. Attribution: https://news.ycombinator.com/item?id=48454349. Prompt: Suggested (written by Agents In Use, not the author's). Sourced by the Agents In Use pipeline.

Credit: FireBeyond on Hacker News

Source published 9 Jun 2026

Source checked 14 Oct 2026

Tools and topics

Claude · Krisp · Obsidian · Dataview · Todoist · Cowork

Meeting notes · Transcripts · Knowledge base

Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.