Automated summary
Provides a formal framework (MVEE) for integrating multilevel selection, phenotypic plasticity, and eco-evo-devo in evolutionary studies.
Strengths
- Formalizes variational methods for evolutionary modeling.
- Integrates interdisciplinary data from biology, social sciences, and computation.
- Addresses multilevel selection and open-ended evolution.
- Applicable to diverse evolutionary systems.
Limitations
- Requires substantial domain knowledge in evolutionary theory.
- May be too abstract for empirical applications without adaptation.
- Not a software tool but a conceptual framework.
Use when developing or analyzing models that span multiple levels of evolutionary organization.
Avoid when a simple single-level evolutionary model suffices or when detailed empirical data is lacking.
Security analysis
SafeThe skill is purely informational, describing a scientific framework for evolutionary studies. It contains no executable code, system commands, or instructions that could cause harm.
No concerns found
How this analysis is madeExamples
Using the MVEE framework, model the evolution of cooperation in a multilevel selection scenario with phenotypic plasticity.How can MVEE help integrate eco-evo-devo data from developmental plasticity and ecological variability into a unified evolutionary model?Explain how to formalize a variational method for open-ended evolution within the MVEE framework.name: "MVEE" description: "Expertise in developing frameworks for evolutionary studies, integrating interdisciplinary data, and formalizing complex biological interactions." tags: ["presentations, courses & media", "mvee", "evolutionary-theory", "multilevel-evolution", "phenotypic-plasticity", "eco-evo-devo"]
MVEE: A Framework for Evolutionary Studies
Daniel Ari Friedman (2018) · Presentations, Courses & Media
Instructions
Use this skill when working with topics related to MVEE, evolutionary theory, multilevel evolution, phenotypic plasticity.
When applying this skill:
- Apply variational methods to evolutionary and ecological modeling
- Integrate multilevel selection theory with developmental and behavioral biology
- Design formal frameworks for open-ended evolutionary systems
Key Concepts
- MVEE
- evolutionary theory
- multilevel evolution
- phenotypic plasticity
- eco-evo-devo
- variational methods
- multilevel selection
- open-ended evolution
Methods & Techniques
- Extends existing theories in Variational Neuroethology.
- Utilizes a formalized approach to model evolutionary processes.
- Incorporates interdisciplinary data from biological, social, and computational systems.
- Analyzes ecological variability over multiple time scales.
Key Findings
- Establishes a general framework applicable to various evolutionary studies.
- Demonstrates the importance of integrating diverse data sources.
- Identifies key factors influencing evolutionary dynamics.
- Suggests new avenues for experimental research in evolutionary biology.
Prerequisites
- Domain knowledge in Presentations, Courses & Media
🎯 Consulting & Tutoring
Daniel Ari Friedman, PhD is available for AI Research Consulting and Tutoring related to this skill.
Related Skills
See BIBLIOGRAPHY.md for the complete publication catalog and related papers.
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/mvee-evolutionary-studies-framework-mmio720s/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/mvee-evolutionary-studies-framework-mmio720s' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/mvee-evolutionary-studies-framework-mmio720s/download' --output '.skill-downloads/mvee-evolutionary-studies-framework-mmio720s/SKILL.md.part' && mv '.skill-downloads/mvee-evolutionary-studies-framework-mmio720s/SKILL.md.part' '.skill-downloads/mvee-evolutionary-studies-framework-mmio720s/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Original excerpt: Prerequisites
- Domain knowledge in Presentations, Courses & Media
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
This file does not provide a usable name/description header for this native procedure. It remains available to download and inspect; use the manual method below.
Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/mvee-evolutionary-studies-framework-mmio720s/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
Create Interactive Maps with kepler.gl
Data & AI
Create interactive map visualizations and export standalone HTML using the keplergl Python package. Use to plot geospatial data from DataFrames, GeoJSON, or CSV.
Agent-Driven Parameter Optimization
Data & AI
Guide for optimizing configurable systems with a measurable objective, using bounded trials and evidence-based recommendations.
ICP Visitor Outreach
Data & AI
Identify signed-up users matching the ideal customer profile but stalled, and draft personalized outreach to re-engage them.