---
name: catalyst-analyzer-ead2910c70ec
description: Catalyst performance analysis skill for activity testing, deactivation modeling, and optimization
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
  - Bash
metadata:
  specialization: chemical-engineering
  domain: science
  category: Reaction Engineering
  skill-id: CE-SK-008
graph:
  domains: [domain:chemical-engineering]
  skillAreas: [skill-area:statistical-analysis, skill-area:physics-simulation, skill-area:mathematical-reasoning]
  workflows: [workflow:experiment-design]
  roles: [role:research-scientist, role:lab-technician]
---

# Catalyst Analyzer Skill

## Purpose

The Catalyst Analyzer Skill evaluates catalyst performance, models deactivation kinetics, and supports catalyst selection and optimization for chemical processes.

## Capabilities

- Catalyst activity measurement analysis
- Selectivity evaluation
- Deactivation kinetics modeling
- Regeneration cycle optimization
- Catalyst screening support
- Turnover frequency calculation
- Surface area and porosity analysis
- Poison identification

## Usage Guidelines

### When to Use
- Evaluating catalyst candidates
- Modeling catalyst deactivation
- Optimizing regeneration cycles
- Troubleshooting performance decline

### Prerequisites
- Experimental data available
- Reaction mechanism understood
- Operating conditions defined
- Reference catalyst identified

### Best Practices
- Use consistent testing protocols
- Account for mass transfer effects
- Consider long-term stability
- Validate deactivation models

## Process Integration

This skill integrates with:
- Catalyst Evaluation and Optimization
- Kinetic Model Development
- Reactor Design and Selection

## Configuration

```yaml
catalyst-analyzer:
  analysis-types:
    - activity
    - selectivity
    - deactivation
    - regeneration
  catalyst-types:
    - heterogeneous
    - homogeneous
    - biocatalyst
```

## Output Artifacts

- Activity profiles
- Deactivation models
- Regeneration protocols
- Screening comparisons
- Recommendation reports


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Education & Research, Product Management
Roles: Data Scientist, Product Manager, Researcher
Specialties: Specialty not specified
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 9bb0f2bac0191bb34feaab82672db282acefcb1cf1b16f92e9bf27b81cb3111a