dsa-process-controller
Directed Self-Assembly skill for block copolymer lithography and nanoparticle templating
Best use case
dsa-process-controller is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Directed Self-Assembly skill for block copolymer lithography and nanoparticle templating
Teams using dsa-process-controller 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/dsa-process-controller/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dsa-process-controller Compares
| Feature / Agent | dsa-process-controller | 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?
Directed Self-Assembly skill for block copolymer lithography and nanoparticle templating
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
# DSA Process Controller
## Purpose
The DSA Process Controller skill provides directed self-assembly process control for block copolymer lithography and nanoparticle templating, enabling sub-lithographic patterning through controlled polymer phase separation.
## Capabilities
- Block copolymer selection and design
- Annealing protocol optimization
- Defect density analysis
- Pattern transfer protocols
- Graphoepitaxy and chemoepitaxy
- Long-range order characterization
## Usage Guidelines
### DSA Process Control
1. **BCP Selection**
- Match pitch to target
- Consider chi-N product
- Select morphology (lamellar, cylindrical)
2. **Annealing Optimization**
- Choose thermal vs solvent vapor
- Optimize temperature/time
- Achieve equilibrium morphology
3. **Defect Analysis**
- Classify defect types
- Quantify defect density
- Identify root causes
## Process Integration
- Directed Self-Assembly Process Development
- Nanolithography Process Development
## Input Schema
```json
{
"bcp_system": "string (e.g., PS-b-PMMA)",
"target_pitch": "number (nm)",
"morphology": "lamellar|cylindrical|spherical",
"guiding_type": "graphoepitaxy|chemoepitaxy",
"substrate_pattern": "string"
}
```
## Output Schema
```json
{
"annealing_protocol": {
"method": "thermal|svA",
"temperature": "number (C)",
"time": "number (hours)",
"solvent": "string (optional)"
},
"achieved_pitch": "number (nm)",
"defect_density": "number (defects/um2)",
"correlation_length": "number (nm)",
"pattern_quality": "string"
}
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