catalyst-analyzer
Catalyst performance analysis skill for activity testing, deactivation modeling, and optimization
Best use case
catalyst-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Catalyst performance analysis skill for activity testing, deactivation modeling, and optimization
Teams using catalyst-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/catalyst-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How catalyst-analyzer Compares
| Feature / Agent | catalyst-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Catalyst performance analysis skill for activity testing, deactivation modeling, and optimization
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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