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Business Operations · Version 1.0.0 · Reviewed 2026-08-02

Decision Log from Meeting Corpus

Remove friction from decision extraction and contradiction detection with evidence, explicit trade-offs, and a verification plan.

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

Extracts decisions across meeting notes and transcripts with date, owner, rationale, alternatives, dependencies, reversibility, and unresolved follow-up.

₹299 one-time

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What this skill helps you do

  • Decision extraction
  • Contradiction detection
  • Owner and follow-up tracking

How Decision Log from Meeting Corpus works

You provide

Process documentation, real case timestamps, and exception volume

It inspects

Where work waits and where ownership is lost in decision extraction

It decides

A contradiction detection redesign targeting the actual constraint

You verify

End-to-end cycle time re-measured, not step time

What it checks first

Decision Log from Meeting Corpus extracts decisions across meeting notes and transcripts with date, owner, rationale, alternatives, dependencies, reversibility, and unresolved follow-up. Use it when the work involves Decision extraction, Contradiction detection, Owner and follow-up tracking.

  1. Where work waits rather than where work happens, since queues dominate cycle time.
  2. Whether exceptions are rare edge cases or a second undocumented process.
  3. Handoff points, which are where ownership and context are most often lost.
  4. Whether the measured process matches the process people actually follow.

Failure modes it recognizes

  • An exception path handling a large share of volume while remaining undocumented.
  • A handoff with no explicit acceptance, so work sits unowned between teams.
  • Automation applied to a broken process, which produces failures faster.
  • A metric that measures throughput of one step while the bottleneck moves elsewhere.

Answers it will reject

  • Mapping the documented process rather than the observed one.
  • Optimizing a step that is not the constraint, which changes nothing end to end.
  • Adding an approval to fix a quality problem, which adds delay without addressing the cause.

Decision rules it applies

  • Measure end-to-end cycle time before optimizing any individual step.
  • Make every handoff have an explicit owner and acceptance criteria.
  • Fix the process before automating it; automation freezes whatever exists.

Evidence it asks for

  • Sample real cases and record timestamps at each transition.
  • Quantify what share of volume takes the exception path.
  • Re-measure end-to-end time after the change, not step time.

The method inside

  1. Map actual work and queues rather than the documented happy path
  2. Classify waits, rework, ownership gaps, and legitimate exceptions
  3. Identify the constraint producing the largest downstream effect
  4. Redesign the handoff with measurable entry and exit criteria

Deliverables

  • Decision extraction current-state map
  • Contradiction detection root-cause register
  • Owner and follow-up tracking future-state control plan

Evidence requirements

  • Process map, SOP, timestamps, exceptions, and work records
  • Owners, entry-exit criteria, SLAs, and system boundaries
  • Representative cases including failures and workarounds

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

Build a decision log from these six project meetings and flag decisions that changed without explicit acknowledgement.

Expected output

Twelve decisions were recorded; two were reversed informally and one has conflicting owners. The launch-scope change lacks rationale and should be confirmed before planning continues...

Boundaries and compatibility

Ideal for

  • Decision extraction: produce a decision or artifact grounded in supplied evidence.
  • Contradiction detection: produce a decision or artifact grounded in supplied evidence.
  • Owner and follow-up tracking: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Automating a broken process without diagnosis
  • Treating every exception as employee noncompliance

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.