| name | general-workflow-planner |
| description | Hierarchically decompose high-level scientific workflows (from literature or user-proposed) into executable sequences of existing SKILLs and MCP tools for the research plan. |
| category | ["general"] |
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
-
Objective Parsing
Analyze the high-level workflow to determine the key scientific steps (e.g., Structure Generation $\rightarrow$ Relaxation $\rightarrow$ Stability $\rightarrow$ Dynamics).
-
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).
-
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.
-
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.
-
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
Author: Bowen Deng
Contact: GitHub @learningmatter-mit