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Research · Version 1.0.0 · Reviewed 2026-08-02

Meeting Transcript Knowledge Miner

Produce defensible evidence for decision extraction and commitment and owner mapping with evidence, explicit trade-offs, and a verification plan.

4 method steps 4 documented failure modes 4 diagnostic checks 7 quality gates

Extracts decisions, rationale, commitments, risks, and unresolved questions from engineering meeting transcripts. It grounds the decision in the full transcript, meeting context, participants, referenced artifacts, timestamps, and subsequent corrections and explicitly prevents treating brainstorming or transcription errors as approved decisions and assigning commitments never accepted by an owner.

₹299 one-time

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What this skill helps you do

  • Decision extraction
  • Commitment and owner mapping
  • Meeting knowledge synthesis

How Meeting Transcript Knowledge Miner works

You provide

Sources, method notes, and the question being answered

It inspects

Evidence strength and negative cases for decision extraction

It decides

A commitment and owner mapping synthesis with limitations named

You verify

Findings reproducible from the cited evidence alone

What it checks first

Meeting Transcript Knowledge Miner extracts decisions, rationale, commitments, risks, and unresolved questions from engineering meeting transcripts. It grounds the decision in the full transcript, meeting context, participants, referenced artifacts, timestamps, and subsequent corrections and explicitly prevents treating brainstorming or transcription errors as approved decisions and assigning commitments never accepted by an owner. Use it when the work involves Decision extraction, Commitment and owner mapping, Meeting knowledge synthesis.

  1. Sample size, selection method, and who was excluded, since these bound every conclusion.
  2. Whether a finding is supported by observed behavior or by what participants said they would do.
  3. Disconfirming cases, which are the fastest test of whether a theme is real.
  4. Whether the question the research answers is the question the decision actually needs.

Failure modes it recognizes

  • Themes derived from the loudest participants rather than from the sampled distribution.
  • Leading questions producing agreement that disappears under neutral phrasing.
  • Survivorship bias from studying only current customers or successful accounts.
  • Coding drift where the same excerpt would be categorized differently later in the process.

Answers it will reject

  • Reporting percentages from a small qualitative sample, which implies precision that does not exist.
  • Treating a synthesized theme as a finding without the excerpts that support it.
  • Stopping at the first pattern that confirms the pre-existing hypothesis.

Decision rules it applies

  • State the sample, the method, and the limitation alongside every finding.
  • Actively seek disconfirming evidence before accepting a theme.
  • Separate what was observed, what was reported, and what was inferred.

Evidence it asks for

  • Attach verbatim excerpts to each theme so a reader can audit the interpretation.
  • Check coding consistency by re-coding a subset and comparing.
  • Report counts with denominators rather than percentages alone.

The method inside

  1. Define the research question and unit of analysis
  2. Create a transparent coding or extraction framework
  3. Preserve source traceability and negative evidence
  4. Separate findings, interpretation, limitations, and applicability

Deliverables

  • Decision extraction evidence table
  • Commitment and owner mapping findings with negative cases
  • Meeting knowledge synthesis limitations and next-research plan

Evidence requirements

  • Source documents, transcripts, data, and research question
  • Sampling method, population, and collection context
  • Known limitations, contradictory cases, and analysis criteria

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Apply the meeting transcript knowledge miner to our current decision extraction work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with the full transcript, meeting context, participants, referenced artifacts, timestamps, and subsequent corrections. The highest-risk failure is treating brainstorming or transcription errors as approved decisions and assigning commitments never accepted by an owner. Require explicit decision language or later confirmation, and separate proposals from commitments and open questions. Verify the result by linking each extracted item to a timestamp and obtaining owner confirmation for ambiguous actions.

Boundaries and compatibility

Ideal for

  • Decision extraction: produce a decision or artifact grounded in supplied evidence.
  • Commitment and owner mapping: produce a decision or artifact grounded in supplied evidence.
  • Meeting knowledge synthesis: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Fabricating sources, participants, or findings
  • Claiming representativeness without a sampling basis

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.