skills.rest
soheunyi
soheunyiCOMMUNITY
Explore all skills by this author →
GitHub

Research Ops and Reproducibility

Define reproducible experiment workflows with run-level metadata and artifact lineage.

1|Updated Feb 8, 2026
Search Tags:#research#metadata#handoff#traceability#reproducibility#artifact
One-click install
npx skills add https://github.com/soheunyi/get-research-done --skill research-ops-and-reproducibility
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Research Ops and Reproducibility
Source: https://github.com/soheunyi/get-research-done/tree/main/skills/grd-ops-and-reproducibility
Command: npx skills add https://github.com/soheunyi/get-research-done --skill research-ops-and-reproducibility

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Define and enforce experiment tracking, artifact lineage, and reproducibility packaging for handoff-ready research outputs.

Core Features & Use Cases

  • Run metadata discipline: standardized capture of environment, data, and code provenance for each experiment.
  • Artifact lineage and reproducibility packaging: clear mapping from inputs to outputs to enable reliable reruns and audits.
  • Handoff and publication readiness: structured outputs, aliases, and templates to support cross-team handoffs and archival documentation.

Quick Start

Create a new run under .grd/research/runs and update the latest alias to point to it.

Frequently Asked Questions about Research Ops and Reproducibility

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce run metadata for reproducible research experiments?▼

Enforce run metadata for reproducible research by defining a workflow that captures environment, data, and code provenance per run. This ensures durable handoffs and auditable reruns across teams.

How do I track artifact lineage for publication-ready research packaging?▼

Track artifact lineage for publication-ready packaging by mapping inputs to outputs using artifact aliasing rules. This provides structured outputs and versioned provenance stored under .grd/research/runs.

What is run-level metadata and when do I need it for research handoffs?▼

Run-level metadata is the standardized capture of environment, data, and code provenance for each experiment. You need it for durable cross-team handoffs, publication-ready archival documentation, and reliable audits.

How do I package reproducible experiments with environment capture and provenance for cross-team audits?▼

Package reproducible experiments by satisfying a run-tracking schema that includes environment capture, artifact aliasing, and versioned provenance. This creates auditable reruns and structured outputs for cross-team handoffs.

Does this reproducibility workflow support versioned provenance for auditable reruns?▼

Yes, the reproducibility workflow supports versioned provenance by storing run-level metadata and artifact lineage under .grd/research/runs, enabling reliable reruns and satisfying run-tracking schemas for research audits.

When do I need to update artifact aliases for reproducible research runs?▼

Update artifact aliases when creating a new run under .grd/research/runs to point the latest alias to it. This maintains clear mapping from inputs to outputs for reliable reruns and publication readiness.

Related Skills

Explore More Education & Research Skills→
az9713az9713

dark-code-suite-init

Initialize a project with dark code prevention directories, CLAUDE.md section, and baseline audit.

Community
Intermediate
adscorp100adscorp100

Blog Creator Skill

Generate SEO-optimized blog posts with cannibalization checks and keyword research.

Community
Advanced
Luney-IndustriesLuney-Industries
4

ship-task

Merge GitHub pull requests and transition Linear or Jira tickets to Done.

Official
Advanced
qcmuuqcmuu

weights-and-biases

Track experiment runs, log metrics, and manage hyperparameters with Weights & Biases.

Community
Advanced