Reusable AI capability

Prepare and Audit a Reproducible Research Package

Apply a repeatable evidence gate to a research handoff covering provenance, permissions, code, environment, dependencies, seeds, run instructions, expected outputs and reproduction gaps.

This Skill packages a reusable way to use the linked Prompt or Workflow; Amo.ng does not run it for you.

Skill ID
AMO-S-000033
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Purpose

Prepare and audit a self-contained computational research package so an authorized reviewer can distinguish reproduced results, divergences, inaccessible dependencies and unresolved evidence without inferring success from an incomplete run.

Required inputs

Have these details available before following the usage instructions.

  • Research-package manifest and expected output references.
  • Data provenance, licences, permissions and access instructions.
  • Code, environment specification, dependencies, seeds and run instructions.
  • Actual execution logs, output checksums or reviewer observations when a run occurred.
  • Known deviations, inaccessible components and decision context.

How to use this Skill

When to use:
- Before handing a computational study to a collaborator, reviewer, repository or archive.
- When testing whether a replication package contains enough evidence for an independent authorized run.
- When documenting divergence between expected and observed outputs.

When not to use:
- To claim successful reproduction without supplied execution records.
- To bypass data licences, participant restrictions, ethics conditions or secure-compute requirements.
- As a substitute for disciplinary methods review, repository acceptance or research-integrity investigation.

Instructions:
Use the source Prompt as the operating audit, not as evidence that execution occurred.

1. Inventory every required package component and bind it to provenance, permission, version and owner evidence.
2. Separate static inspectability from executed reproduction. Record the environment, command, seed, input fingerprint, exit state and output comparison only when corresponding evidence exists.
3. Classify each expected result as reproduced within tolerance, diverged, not tested, inaccessible or not applicable.
4. Trace divergences to the smallest supported hypothesis; preserve alternative explanations and evidence requests.
5. Produce a handoff manifest, reproducibility matrix, deviation register and smallest next action.
6. Assign data-access, licensing, infrastructure, methods and release decisions to their accountable owners.

Never insert credentials or protected data into the Skill input. A package that cannot lawfully be accessed is incomplete, not failed.

Expected output:
A versioned research-package manifest, execution evidence ledger, expected-versus-observed output matrix, divergence register, access/licensing gap list and bounded reproducibility disposition.

Constraints and boundaries:
- Do not claim an execution, checksum match or reproduced result without a supplied record.
- Do not redistribute licensed, confidential or participant data.
- Do not conceal environment drift or modify the reference package during an audit without a recorded change.
- Repository deposit, ethics, data access and release remain with the responsible owners.

Powered by an Amo.ng Prompt

Replication Package Computational Reproducibility Gate

Open the linked prompt to use the instructions that power this Skill.

Open prompt

Completion criteria

Complete when the package boundary and versions are fixed; every required artefact has provenance, permission and availability status; every reported run has reproducible evidence; expected outputs have an explicit disposition; divergences and inaccessible elements remain visible; and next actions have accountable owners. Otherwise return an incomplete-package record.

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