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reproducible-ecology-pipeline

Automate provenance tracking and environment logging for reproducible ecological analyses.

2|Updated Mar 2, 2026

Search Tags:#workflow #documentation #provenance #reproducibility #audit-trail #environment-documentation #parameter-manifest

Ready to run

npx skills add https://github.com/baratadiego/ecological-agent-skills --skill reproducible-ecology-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reproducible-ecology-pipeline
Source: https://github.com/baratadiego/ecological-agent-skills/tree/main/skills/reproducible-ecology-pipeline
Command: npx skills add https://github.com/baratadiego/ecological-agent-skills --skill reproducible-ecology-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Ensures full reproducibility of ecological analyses through provenance tracking, decision logging, parameter manifests, and environment documentation. Use this skill when the user mentions reproducibility, audit trails, data provenance, decision logs, file manifests, session info, renv, targets, DVC, MLflow, parameter versioning, checksums, or pipeline documentation and project initialization.

Core Features & Use Cases

Provenance tracking and audit trails for data sources, software versions, and analytical decisions to enable reproducibility.
Centralized parameter manifests and environment documentation to avoid hard-coded values and ensure exact replication.
Standardized project initialization and workflow templates to support reproducible ecology analyses across R and Python.
Compatibility with tools like renv, targets, DVC, and MLflow to enhance tracking, versioning, and experiment management.

Quick Start

Initialise a reproducible ecology project structure and populate the standard logging and provenance templates.

Frequently Asked Questions about reproducible-ecology-pipeline

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

FAQPage Schema

How do I ensure reproducibility for my ecology data analysis pipeline?▼

Yes, reproducibility tools like renv, targets, DVC, and MLflow are fully supported. The skill integrates with these frameworks to enhance tracking, versioning, and experiment management across your R and Python workflows.

Does this work with both R and Python workflows for tracking provenance?▼

What is a parameter manifest and why do I need it for ecological analysis?▼

A parameter manifest is a centralized file that acts as a single source of truth for your analytical parameters. It is needed to avoid hard-coded values and ensure exact replication of ecological results across different environments.

How do I set up an audit trail for environmental data provenance?▼

You set up an audit trail by initializing a project structure that automatically logs data sources, software versions, and analytical decisions. This streams provenance details into audit-ready artifacts during ongoing analysis.

When should I use a decision log in my research workflow?▼

You should use a decision log whenever you make analytical choices during your research workflow. It enforces a single source of truth by documenting the reasoning and parameters applied throughout the ecological analysis.