general-workflow-planner
learningmatter-mit/AtomisticSkills/.agents/skills/general-workflow-planner/SKILL.md
Hierarchically decompose high-level scientific workflows (from literature or user-proposed) into executable sequences of existing SKILLs and MCP tools for the research plan.
Skill172 starsChanged 4 months ago
What's in it
- General Workflow Planner
- Goal
- Prerequisites
- Instructions
- Examples
- Constraints
- See Also
--- 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 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](examples/sse-discovery/README.md) 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-deep-research/SKILL.md) - [general-peer-review](../general-peer-review/SKILL.md) --- **Author:** Bowen Deng **Contact:** [GitHub @learningmatter-mit](https://github.com/learningmatter-mit)
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