General Workflow Planner

Goal

To decompose high-level scientific workflows (either sourced from literature or proposed directly by the user) into a concrete, executable sequence. This skill parses the objective and outputs a chronological "Detailed Action Plan" that feeds directly into the research_plan.md artifact, in accordance with .agents/rules/research-standards.md. Do not overcomplicate the output; it should be a straightforward list of steps.

Prerequisites

• A high-level scientific workflow proposed by the user or derived from literature review.
• Access to the .agents/skills/ registry and available MCP tools.

Instructions

1. Objective Parsing Analyze the high-level workflow to determine the key scientific steps (e.g., Structure Generation $\rightarrow$ Relaxation $\rightarrow$ Stability $\rightarrow$ Dynamics).

2. Skill Registry Mapping Scan the repository's capabilities. Map each conceptual step to existing project tools by searching the .agents/skills/ directory and available MCP tools (e.g., mcp_mace_run_md, mcp_matgl_relax_structure).

3. Dependency Construction Map the dependencies between the identified SKILLs and MCP tools:

   • Identify data dependencies: The output of Step A must act as the input for Step B (e.g., the mat-db-mp skill outputs a .cif, which serves as the input for the mcp_mace_relax_structure MCP tool).
   • Identify parallelization opportunities if applicable.

4. Feasibility Analysis

   • Verify that there is a continuous line of data flowing from the initial state to the target objective using only existing tools.
   • If missing steps exist, flag them explicitly so the user knows where custom scripting or new skills are required.

5. Detailed Action Plan Generation Output a concrete, chronological list of steps required to execute the workflow. List the proposed hyperparameters for each SKILL and MCP tool (e.g., temperature, steps, supercell_min_length). This list is directly inserted into the Detailed Action Plan section of research_plan.md.

Examples

For an example of decomposing a high-level goal into a Detailed Action Plan using existing skills and MCP tools, see the Solid-State Electrolyte Discovery example.

Constraints

• Skill Hallucination: NEVER invent or hallucinate skill names. Every step must map to a verifiable directory inside .agents/skills/ or a documented MCP tool.
• Simplicity: Do not overcomplicate the output. Produce a linear or simple branching Action Plan suited for research_plan.md.

See Also

• general-deep-research
• general-peer-review

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Author: Bowen Deng Contact: GitHub @learningmatter-mit