automd-gromacsAutoMD-GROMACS: AI-friendly molecular dynamics automation for GROMACS with workflow, enhanced sampling, special-system simulation, advanced analysis, and pub...
Install via ClawdBot CLI:
clawdbot install billwanttobetop/automd-gromacsGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses system directories or attempts privilege escalation
sudo cpCalls external URL not in known-safe list
https://github.com/Billwanttobetop/automd-gromacsUses known external API (expected, informational)
api.github.comAI Analysis
The skill is a scientific workflow tool for GROMACS that primarily executes local scripts and references its own GitHub repository for installation. The 'sudo cp' command is a common, low-risk system administration pattern for software installation, not privilege escalation. No evidence of data exfiltration, credential harvesting, or hidden malicious instructions exists.
Generated Oct 3, 2026
University labs use AutoMD-GROMACS to run protein-ligand binding free energy calculations, membrane simulations, and enhanced sampling studies without hand-crafting complex GROMACS command chains. The decision layer routes researchers to the right workflow, while experiment logs and troubleshooting references keep multi-day simulations reproducible.
Pharmaceutical computational chemistry teams deploy the skill to automate ligand topology generation (acpype/GAFF2), cross-forcefield system building, and MM/PBSA or umbrella sampling workflows. Publication-ready visualization and validated analysis pipelines accelerate lead optimization cycles and reduce manual scripting errors.
HPC cloud providers and research computing groups use the GPU installation, CUDA tuning, and execution guidance to offer turnkey GROMACS environments to customers. Built-in troubleshooting for GPU MD execution and ORCA OpenMPI parallelism reduces support tickets and onboarding time for new users.
Agent developers integrate AutoMD-GROMACS as a tool skill so AI assistants can plan, execute, log, and validate molecular dynamics experiments autonomously over long sessions. The mandated EXPT_LOG.md and PLAN.md conventions give agents persistent memory across context resets, enabling reliable multi-day scientific workflows.
Consulting firms and core facilities apply the skill to coarse-grained, electric-field, non-equilibrium, and QM/MM special-system simulations for materials science clients. Reusable MDP templates and structured error lookups let small teams deliver membrane, polymer, and scattering analyses with consistent quality.
The skill and core workflows remain MIT-licensed on GitHub, while paid support plans offer priority troubleshooting, custom workflow development, and validated parameter sets for enterprise users. Revenue comes from annual support subscriptions and per-incident consulting for GPU, ligand, and QM/MM issues.
A hosted platform wraps AutoMD-GROMACS with job scheduling, GPU compute, experiment-log dashboards, and result visualization, charging by compute-hour or project. Academic and biotech customers avoid local installation and get reproducible, logged runs with the decision layer built in.
Paid workshops, online courses, and certification exams teach researchers and agent developers how to use the decision layer, enhanced sampling workflows, and troubleshooting references effectively. Corporate training packages include custom curriculum for drug discovery or materials teams.
💬 Integration Tip
Install GROMACS (gmx) and PyYAML first, then always run the method-selector decision layer before any workflow and enforce EXPT_LOG.md/PLAN.md creation for long simulations; use references/SKILLS_INDEX.yaml and the troubleshoot/ directory to route failures quickly and keep token usage low.
Scored Oct 3, 2026
Audited Apr 17, 2026 · audit v1.0
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
Stop waiting for prompts. Keep working.
Turn OpenClaw into a learning-loop agent with seeded workspace rules, skill promotion, reflective memory, and proactive maintenance.
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
Complete toolkit for creating autonomous AI agents and managing Discord channels for OpenClaw. Use when setting up multi-agent systems, creating new agents, or managing Discord channel organization.
Billions decentralized identity for agents. Link agents to human identities using Billions ERC-8004 and Attestation Registries. Verify and generate authentic...