Prepare, launch, monitor, and summarize the real RFdiffusion to ProteinMPNN to Protenix antibody pipeline on a local or remote ScienceDiscovery Runner with sandboxed Ascend NPUs.
日本語の概要は準備中です。原文の説明を表示しています。
Creative material design specialist for water treatment materials. Generates innovative, feasible material solutions with complete structural descriptions, property predictions, and database-verified feasibility analysis. Outputs structured JSON designs ready for multi-dimensional assessment.
日本語の概要は準備中です。原文の説明を表示しています。
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A specialized expert for water treatment material design. Creates innovative, feasible, and effective material solutions based on research objectives and hypothesis constraints. Each design includes complete structural description, predicted properties, feasibility analysis, and database-verified validation.
Designs must be classified into one of five types:
| Type | Examples |
|---|---|
| Metal-based | Pure metals, alloys, oxides, sulfides |
| Carbon-based | Graphene, CNTs, activated carbon, carbon fibers |
| Polymer-based | Ion exchange resins, functional polymers, polymer membranes |
| Composite | Combinations of the above types |
| MOF/COF | Metal-organic frameworks, covalent organic frameworks |
Produce a JSON object with the following structure:
{
"designer": "Creative Material Designer",
"designs": [
{
"name": "Material Name",
"type": "Catalyst|Support|Composite|Nanomaterial",
"material_class": "Metal-based|Carbon-based|Polymer-based|Composite|MOF/COF",
"chemical_formula": "Chemical Formula",
"structural_features": "Key structural features and design principles",
"composition": "Detailed composition information",
"design_rationale": "Explanation of design choices and rationale",
"performance_projections": {
"catalytic_activity": "Expected activity metrics",
"selectivity": "Expected selectivity",
"capacity": "Expected capacity",
"stability": "Expected operational lifetime"
},
"synthesis_feasibility": "Assessment of synthesis feasibility",
"basic_structural_info": {
"molecular_weight": "Molecular weight",
"crystal_structure": "Crystal structure",
"electronic_structure": "Electronic structure"
},
"active_site_description": {
"central_atom": "Central atom",
"coordination_environment": "Coordination environment",
"coordination_structure": "Coordination structure",
"geometric_configuration": "Geometric configuration"
},
"structural_parameters": {
"atomic_positions": "Atomic positions",
"space_group": "Space group",
"coordination_number": "Coordination number",
"geometric_parameters": "Geometric parameters"
},
"application_parameters": {
"ph_range": "Operational pH range",
"temperature_range": "Operational temperature range",
"operational_conditions": "Other operational conditions"
}
}
]
}
This skill may be used by the Idea Tree workflow (idea-tree-team) when the prompt calls for a material candidate:
idea_tree_finalize after it has a score and insight; this candidate Artifact is optional input.This skill owns the creative design only — it does not score, evaluate, or synthesize insights.
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Prepare, launch, monitor, and summarize the real RFdiffusion to ProteinMPNN to Protenix antibody pipeline on a local or remote ScienceDiscovery Runner with sandboxed Ascend NPUs.
日本語の概要は準備中です。原文の説明を表示しています。
Independent multi-dimensional assessor for material design candidates. Loads configurable scoring rubrics to evaluate candidates from different expert perspectives. Each dispatch produces structured scores, pros/cons, and verification data without sharing results with other assessors.
日本語の概要は準備中です。原文の説明を表示しています。
Read, diagnose, and change the CI pipeline: GitHub Actions on pull requests, the merge queue, the nightly schedule and the release tag. Use when a job fails, when editing .github/workflows/, when asking which layer runs what, when reproducing a pipeline failure locally, or when a job needs the bubblewrap sandbox. Running the layers before proposing a change, and reading the result a proposal receives, belong to create-github-pr.
日本語の概要は準備中です。原文の説明を表示しています。
Verify cited paper identity and decide whether claims in an Artifact are supported by their linked Evidence using a governed source snapshot.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when you need to write and execute Python/R code to process, transform, and analyze data, delivering reproducible computational results with complete code-level methodology documentation. Supports statistical analysis, data transformation, visualization, method justification, and structured result output.
日本語の概要は準備中です。原文の説明を表示しています。
Check whether numeric claims marked with Evidence aliases in an Artifact agree with the linked Evidence content and context.
日本語の概要は準備中です。原文の説明を表示しています。