general-workflow-planner

Decompose scientific workflows into chronological action plans with skill mappings.

144|21|Updated Jan 8, 2026
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill general-workflow-planner
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: general-workflow-planner
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/general-workflow-planner
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill general-workflow-planner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It converts a high-level scientific objective into a concrete, chronological execution plan that an AI can directly carry out with existing AtomisticSkills and MCP tools.

Core Features & Use Cases

  • Objective to action decomposition: Breaks a proposed or literature-derived workflow into key scientific stages (e.g., generation → relaxation → stability → dynamics).
  • Skill registry mapping: Matches each conceptual step to verifiable skill directories under .agents/skills and documented MCP tools.
  • Dependency-aware planning: Builds data-flow dependencies between steps and flags missing capabilities that require new scripts/skills.
  • Detailed Action Plan output: Produces a straightforward list of steps (with suggested hyperparameters) that can be inserted into research_plan.md.

Quick Start

Ask your AI to use general-workflow-planner to decompose your literature or user-proposed materials research objective into a detailed, chronological research_plan.md action list using only existing skills and MCP tools.

Frequently Asked Questions about general-workflow-planner

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

FAQPage Schema
How do I decompose a high-level materials research objective into executable steps?

Workflow planning decomposes high-level materials research objectives into a chronological, dependency-consistent action plan. It translates literature-derived scientific stages into explicit step-to-skill mappings with proposed hyperparameters for direct execution.

What is the best way to map a literature workflow to existing research skills and MCP tools?

Mapping a literature workflow to existing skills involves matching conceptual scientific stages to verifiable registry entries. This process prevents skill name hallucination by strictly using documented AtomisticSkills directories and MCP tools to build an executable plan.

How do I plan data-flow dependencies for an atomistic materials simulation workflow?

Planning data-flow dependencies for atomistic simulation workflows requires breaking the objective into chronological stages like generation, relaxation, and dynamics. The planner flags missing capabilities that require new scripts and structures the data sequence.

Can I use this workflow planning approach for drug discovery and chemistry tasks?

Yes, workflow planning applies to materials, chemistry, and drug-discovery style tasks. It operationalizes user-provided objectives or literature workflows into a detailed action list with suggested hyperparameters suitable for these scientific domains.

How do I generate a chronological research plan with suggested hyperparameters?

Generating a chronological research plan involves breaking a high-level objective into sequential scientific stages and mapping them to verified tools. The output is a detailed action plan with proposed hyperparameters ready for insertion into research_plan.md.