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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 6 documented failure modes 5 diagnostic checks 7 quality gates

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

₹149 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

Build definition, timings, and cache statistics

It inspects

Layer ordering and secret exposure for decision extraction

It decides

A contradiction detection change that keeps every gate intact

You verify

Per-stage duration and cache hit rate re-measured

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. Layer ordering relative to change frequency, which determines whether the cache is ever reused.
  2. Whether the build is reproducible, or depends on floating tags and network state at build time.
  3. Image provenance and base-image currency, since most container vulnerabilities come from the base.
  4. Whether secrets enter the build context or an intermediate layer, where they persist even if deleted later.
  5. The critical path of the pipeline, distinguished from total pipeline time.

Failure modes it recognizes

  • Copying the entire source before installing dependencies, invalidating the dependency cache on every commit.
  • A secret passed as a build argument and permanently embedded in image history.
  • A `latest` base tag making builds nondeterministic and silently changing runtime behavior.
  • Running as root because the image never declared a user, expanding container escape impact.
  • A cache key that includes a timestamp, so the cache never hits.
  • Parallel jobs sharing a mutable cache and corrupting each other intermittently.

Answers it will reject

  • Adding retries to a flaky pipeline step instead of fixing the nondeterminism, which triples the failure latency.
  • Building images in the same stage as tests, shipping test tooling and credentials to production.
  • Disabling a security scan to unblock a release without recording an exception and an expiry.
  • Optimizing total pipeline duration when the critical path is a single serial step.

Decision rules it applies

  • Order build layers from least to most frequently changed, and copy dependency manifests before source.
  • Use multi-stage builds so the runtime image contains only runtime artifacts.
  • Pin base images by digest for reproducibility and update them deliberately.
  • Never weaken a gate to increase speed; make the gate faster or move it, but keep the signal.

Evidence it asks for

  • Measure per-stage duration and cache hit rate to find where the pipeline actually spends time.
  • Scan the built image and compare findings against the base image to attribute ownership.
  • Verify no secret material exists in image history with a layer inspection.

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 custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

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.