transcript-critic

To analyze meeting transcripts for hidden decision-making flaws, you apply a diagnostic skill that scans the text for false consensus, marginalized voices, unstated dependencies, and scope drift. This process generates a structured report with severity ratings and verbatim evidence to validate post-meeting decisions.

False consensus in meeting notes occurs when apparent agreement masks unaddressed objections or marginalized voices. You detect it by running a consensus audit on the transcript, which cross-references stated decisions with participant contributions to flag instances of interrupted topics and problematic authority dynamics.

To reformulate raw requirements into proper user stories, you apply an interactive reformatting process that checks problem-vs-solution framing, validates observable behavior against CRUD status, and probes for rigid quantifiers. This maps hidden domain decisions to ensure aligned implementation and reduce rework.

Yes, you can audit third-party meeting notes for scope drift and unstated dependencies by processing the text through a decision validation analysis. This identifies misaligned problem-solution framing and generates targeted diagnostic questions to mitigate risks in subsequent implementation phases.

The best way to prepare targeted questions for a follow-up meeting is to run a pre-action review on the previous meeting's transcript. This surfaces hidden flaws like severity mismatch and fact-vs-opinion escalation, outputting structured diagnostic questions to guide the next discussion.

Your requirements spec mixes problem and solution framing because raw requirements often entangle the underlying need with specific implementation details. Applying a problem-vs-solution framing check separates these concerns, validating observable behavior and uncovering unstated domain assumptions.